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Surrogate model provides a promising way to reasonably approximate complex underlying relationships between system parameters. However, the expensive modeling cost, especially in large problem sizes, hinders its applications in practical problems. To overcome this issue, with the advantages of the multi-fidelity surrogate (MFS) model, this paper proposes a single-fidelity surrogate model with a hierarchical structure, named nonlinearity integrated correlation mapping surrogate (NI-CMS) model. The NI-CMS model first establishes the low-fidelity model to capture the underlying landscape of the true function, and then, based on the idea of MFS model, the established low-fidelity model is corrected by minimizing the mean square error to ensure prediction accuracy. Especially, a novel MFS model (named NI-MFS), is constructed to enhance the stability of the proposed NI-CMS model. More specifically, a nonlinear scaling term, which assumes the linear combination of the projected low-fidelity predictions in a high-dimensional space can reach the high-fidelity level, is introduced to assist the traditional scaling term. The performances of the proposed model are evaluated through a series of numerical test functions. In addition, a surrogate-based digital twin of an XY compliant parallel manipulator is used to validate the practical performance of the proposed model. The results show that compared with the existing models, the NI-CMS model provides a higher performance under the condition of a small sample set, illustrating the promising potential of this surrogate modeling technique. [ABSTRACT FROM AUTHOR]

Emerging shared mobility systems are gaining popularity due to their significant economic and environmental benefits. In this paper, we present a network-based approach for predicting travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection) in shared mobility systems to support system design decisions. In particular, we answer the research question of whether local network information (e.g., the network neighboring station's features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role? To answer this question, we propose using graph neural networks (GNNs), in which the concept of network embedding can capture and quantify the effect of local network structures. We compare the results with a regular artificial neural network (ANN) model that is agnostic to neighborhood information. This study is demonstrated using a real-world bike sharing system, the Divvy Bike in Chicago. We observe that the GNN prediction gains up to 8% higher performance than the ANN model. Our findings show that local network information is vital in the structure of a sharing mobility network, and the results generalize even when the network structure and density change significantly. With the GNN model, we show how it supports two crucial design decisions in bike sharing systems, i.e., where new stations should be added and how much capacity a station should have. [ABSTRACT FROM AUTHOR]

This paper develops an efficient reliability analysis method based on the improved radial basis function neural network (RBFNN) to increase the accuracy and efficiency of structural reliability analysis. To solve the problems of low computational accuracy and efficiency of the RBFNN, an improved RBFNN method is developed by transferring the sampling center of Latin hypercube sampling (LHS) from the mean values of random variables to the most probable point (MPP) in the sampling step. Then, the particle swarm optimization algorithm is adopted to optimize the shape parameters of RBFNN, and the RBFNN model is assessed by the cross-validation method for subsequent reliability analysis using Monte Carlo simulation (MCS). Four numerical examples are investigated to demonstrate the correctness and effectiveness of the proposed method. To compare the computational accuracy and efficiency of the proposed method, the traditional radial basis function method, hybrid radial basis neural network method, first-order reliability method (FORM), second-order reliability method (SORM), and MCS method are applied to solve each example. All the results demonstrate that the proposed method has higher accuracy and efficiency for structural reliability analysis. Importantly, one practical example of an industrial robot is provided here, which demonstrates that the developed method also has good applicability and effectiveness for complex engineering problems. [ABSTRACT FROM AUTHOR]

As the most prestigious award in the field of machine design, the ASME Machine Design Award relishes its prestige and prominence. In the past 65 years since its inception in 1958, 60 eminent scholars have received this honor. This paper reviews the history of this prestigious award, including a summary of the contributions to the field of all 60 recipients, and acts as an archival document. Reviewing these awards in the past 65 years by following the tracks of its recipients elucidates developments in the field and significant contributions to the advancement of knowledge in machine design. In particular, novel contributions and remarkable achievements in the fields of mechanical engineering and machine design and development are recorded. This should be of interest to those interested in the study and in the historical development of mechanical engineering, particularly in machine design. [ABSTRACT FROM AUTHOR]

It is well known that the computational cost of classic topology optimization (TO) methods increases rapidly with the size of the design problem because of the high-dimensional numerical simulation required at each iteration. Recently, the technical route of replacing the TO process with artificial neural network (ANN) models has gained popularity. These ANN models, once trained, can rapidly produce an optimized design solution for a given design specification. However, the complex mapping relationship between design specifications and corresponding optimized structures presents challenges in the construction of neural networks with good generalizability. In this paper, we propose a new design framework that uses deep learning techniques to accelerate the TO process. Specifically, we present an efficient topology optimization (ETO) framework in which structure update at each iteration is conducted on a coarse scale and a structure mapping neural network (SMapNN) is constructed to map the updated coarse structure to its corresponding fine structure. As such, fine-scale numerical simulations are replaced by coarse-scale simulations, thereby greatly reducing the computational cost. In addition, fragmentation and padding strategies are used to improve the trainability and adaptability of SMapNN, leading to a better generalizability. The efficiency and accuracy of the proposed ETO framework are verified using both benchmark and complex design tasks. It has been shown that with the SMapNN, TO designs of millions of elements can be completed within a few minutes on a personal computer. [ABSTRACT FROM AUTHOR]

Research on psychological safety has been growing in recent years due to its role in promoting creativity and innovation since teams with high levels of psychological safety feel safe to express ideas and opinions. However, there is limited evidence in how to facilitate or build it within teams, particularly in an educational context. This paper was developed to respond to this research void by identifying the impact of teaming interventions aimed at improving psychological safety in engineering design student teams. Specifically, we studied two cohorts of students in a cornerstone design class (N = 411 students), one who received a series of video interventions and introduced role playing (intervention) and one who did not (control). These role assignments--referred to as the lenses of psychologically safety--were created to promote key leadership attributes that have been shown to be crucial in facilitating psychologically safe teams. To compare the utility of the intervention, psychological safety was gathered at five key time points of a multi-week design project. The results identified three key findings. First, the interventions were successful in increasing psychological safety in engineering teams. In addition, the results indicated the utility of the lenses of psychological safety throughout the design process. Finally, the results identified that groups who used these lenses had higher perceptions of psychological safety in their teams. Overall, these results indicated that psychological safety can be improved in engineering education through the intervention methods described within. [ABSTRACT FROM AUTHOR]

Origami robot is a hotspot for research in the field of soft robot. However, there are still some major limitations to their application. This study proposed an origami robot based on spherical-linkage parallel mechanism (SLPM) for realizing some functions that cannot be accomplished by conventional robots. This study designed the manufacturing and assembling processes for the SLPM section according to the needs of practical applications, to explore the influence of flexible hinge on the resistance of SLPM section to lateral and torsional forces, the finite element simulation of SLPM section was performed, and the physical model of SLPM section was made to conduct a series of experiment. Also, an origami robot based on SLPM was also made, and the motion form of the robot was explored by adams. At last, through establishing a mathematical model, the relationship for conversion between the two control modes of the robot was deduced. Based on this, an experiment on the bending angle of the robot was carried out, and the simulation results were compared. This paper will promote the research of origami robot in structure design, motion control, etc. [ABSTRACT FROM AUTHOR]

The space docking process must be simulated on the ground to guarantee the success of the space docking task. By synthesizing a physical simulation and a numerical simulation, a hybrid simulator can simulate the complicated contact process of space docking. A hybrid simulator consists of a robot (i.e., lower platform), upper platform, docking mechanisms, contact force and torque measurement system, and numerical simulation system. To ensure the simulation accuracy, the robot is expected to have a high structure stiffness, high structure natural frequency, and high bandwidth frequency response. However, these performances are always limited in implementation. In this paper, how the structural dynamics of the robot affect the hybrid simulation accuracy is studied. Due to the structural dynamics of the robot, the divergence and convergence of the hybrid simulation are both possible. The stability conditions are given. A distortion compensation method for the structure dynamics of the robot is proposed. The stability analysis after the compensation is given. The software emulations and experiments are used to verify the analysis and the distortion compensation method. Experiments on real docking mechanisms are given to show the applications. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a holistic framework to design optimized spiral bevel and hypoid gearsets with accurate finite element simulations in the loop. Starting from the basic transmission data, we first size gear and pinion blanks, and then we synthesize the basic machine-tool settings required to generate the two toothed members. This first step represents the macro-geometry design phase and its outcome is a conjugate spiral bevel or hypoid gearset. The second design phase is represented by the definition of the optimal pinion micro-geometry. This is formulated as a multi-objective optimization problem (MOOP) where the obtained optimal ease-off is guaranteed to be manufacturable. To this end, an original strategy is proposed where the search for the pinion optimal tooth surface happens in the space of the coefficients of a polynomial representation of its micro-topography. However, thanks to a fast identification algorithm that can handle all the higher-order motions, the ideal ease-off is projected onto set of machine-tool settings, thus ensuring manufacturability from the outset. It is worth remarking that the objective functions in the MOOP are evaluated by calling as a back-end solver one of the most accurate loaded tooth contact analysis software available on the market. A dedicated parallel implementation of such MOOP allows to maintain computation times within very reasonable limits. A fully worked out numerical test case clearly demonstrates that the whole procedure far surpasses the current state of the art. [ABSTRACT FROM AUTHOR]

The reliability of the prediction model and test scheme for the positioning accuracy of the cycloid reducer is very important. This paper proposes an efficient bidirectional drive tooth contact analysis (BDTCA) model that can simultaneously analyze the forward transmission error (TE), the reverse TE, and the global lost motion (GLM) of a cycloid drive, considering a variety of manufacturing and assembly errors (including pin assembly error). The results of the BDTCA case studies show that the sensitivity of the pin radius error to the GLM considering pin assembly error is twice that of the sensitivity without considering pin assembly error. Therefore, the influence of pin assembly error on the GLM cannot be ignored. The equivalent error model for BDTCA is established based on Latin hypercube sampling and template measurement, and the positioning accuracy of 50,000 reducer virtual prototypes is predicted. According to the positioning accuracy requirements, the tolerance allocation is optimized by using the discrete particle swarm optimization algorithm, and the qualified rate after optimization is significantly improved. A bidirectional drive test (BDT) scheme is proposed according to the BDTCA model. By comparing with the traditional hysteresis curve test, the superiority of the BDT scheme and the rationality of the tolerance allocation optimization are proved. The sensitivity of the pin radius error to the GLM is obtained through a comparison BDT, which verifies the correctness of the BDTCA model considering pin assembly error. This BDTCA model provides more reliable theoretical guidance for the design and manufacture of cycloid reducers. [ABSTRACT FROM AUTHOR]

Epistemic uncertainty is widespread in reliability analysis of practical engineering products. Evidence theory is regarded as a powerful model for quantifying and analyzing epistemic uncertainty. However, the heavy computational burden has severely hindered its application in practical engineering problems, which is essentially caused by the repeated extreme analysis of limit-state function (LSF). In order to address the issue, this paper proposes a novel method to solve the evidence-theory-based reliability analysis (ETRA). It transforms the conventional ETRA problem into the classification of three classes of joint focal elements (JFEs) and then solves the classification problem effectively through a deep learning approach. The core of solving an ETRA problem is to determine whether the joint focal element is located in the reliable region, failure region, or intersected with the LSF. A spatial position feature reduction and arrangement method is proposed to classify the JFEs, which can effectively reduce the feature dimension and take into account the integrity and correlation of features. The stacked autoencoders model is then constructed and updated by extracting the spatial position features of the sampled JFEs to achieve high-accuracy classification of the remaining JFEs, and the reliability interval is calculated efficiently according to the classification results. Finally, the effectiveness of the proposed method is demonstrated using several numerical examples. [ABSTRACT FROM AUTHOR]

Planetary gear set (PGS) has been one of the best components to constitute a transmission configuration, including the dedicated hybrid transmission (DHT). Using different synthesis approaches, the DHT configurations can be obtained through algorithms. However, different synthesis results correspond to different connection states of the planetary gear system. There are a certain number of results that violate the motion requirements of the mechanical principal need to be detected and removed. Therefore, this paper presents a novel modeling method to systematically remove the interference structures, with graph theory in structural synthesis. Based on the original graph theory, this paper proposes an equivalent replacement modeling method to convert the motor graph model into a brake-like graph model. Based on the conversion, avoid the appearance of the hanging points in the graph model. By applying the proposed approach, a DHT structure proves the feasibility of the method. The proposed detection approach can systematically detect all the PGS-based transmission with multi-PGSs, multi-shifting elements, and multi-power sources. [ABSTRACT FROM AUTHOR]

Increasingly strict emission and fuel economy standards stimulate the researches on hybrid electric vehicle techniques in the automobile industry and one of the most important techniques is the design of powertrain configurations. In this paper, a theoretical design methodology for hybrid electric vehicle powertrain configurations is proposed to find the configurations with excellent performance in a large pool of configurations. There are two main parts in a powertrain configuration, power/coupling devices (engine, electric machine, wheel, and planetary gear set) and mechanical connections between these devices. Different connections will lead to configurations having different performances. This paper divides all connections in configurations into three categories and a novel matrix representation method is developed to express these kinds of connections to reflect system dynamics and physical structure of configurations. With the support of the matrix representation method, configuration selections from large pools can automatically be completed by computer and manual calculation and comparison can be avoided, which saves much energy and time. Finally, the proposed method is vigorously verified by simulations. [ABSTRACT FROM AUTHOR]

The automation and intelligence highlighted in Industry 4.0 put forward higher requirements for reasonable trade-offs between humans and machines for decision-making governance. However, in the context of Industry 4.0, the vision of decision support for design engineering is still unclear. Additionally, the corresponding methods and system architectures are lacking to support the realization of value-chain-centric complex engineered systems design lifecycles. Hence, we identify decision support demands for complex engineered systems designs in the Industry 4.0 era, representing the integrated design problems at various stages of the product value chain. As a response, in this paper, the architecture of a Knowledge-Based Design Guidance System (KBDGS) for cloud-based decision support (CBDS) is presented that highlights the integrated management of complexity, uncertainty, and knowledge in designing decision workflows, as well as systematic design guidance to find satisfying solutions with the iterative process "formulation-refinement-exploration-improvement" (FREI). The KBDGS facilitates diverse multi-stakeholder collaborative decisions in end-to-end cloud services. Finally, two design case studies are conducted to illustrate the proposed work and the efficacy of the developed KBDGS. The contribution of this paper is to provide design guidance to facilitate knowledge discovery, capturing, and reuse in the context of decision-centric digital design, thus improving the efficiency and effectiveness of decision-making, as well as the evolution of decision support in the field of design engineering for the age of Industry 4.0 innovation paradigm. [ABSTRACT FROM AUTHOR]

Industry 4.0 is based on the digitization of manufacturing industries and has raised the prospect for substantial improvements in productivity, quality, and customer satisfaction. This digital transformation not only affects the way products are manufactured but also creates new opportunities for the design of products, processes, services, and systems. Unlike traditional design practices based on system-centric concepts, design for these new opportunities requires a holistic view of the human (stakeholder), artefact (product), and process (realization) dimensions of the design problem. In this paper we envision a "human-cyber-physical view of the systems realization ecosystem," termed "Design Engineering 4.0 (DE4.0)," to reconceptualize how cyber and physical technologies can be seamlessly integrated to identify and fulfil customer needs and garner the benefits of Industry 4.0. In this paper, we review the evolution of Engineering Design in response to advances in several strategic areas including smart and connected products, end-to-end digital integration, customization and personalization, data-driven design, digital twins and intelligent design automation, extended supply chains and agile collaboration networks, open innovation, co-creation and crowdsourcing, product servitization and anything-as-a-service, and platformization for the sharing economy. We postulate that DE 4.0 will account for drivers such as Internet of Things, Internet of People, Internet of Services, and Internet of Commerce to deliver on the promise of Industry 4.0 effectively and efficiently. Further, we identify key issues to be addressed in DE 4.0 and engage the design research community on the challenges that the future holds. [ABSTRACT FROM AUTHOR]

Precise time series prediction serves as an important role in constructing a digital twin (DT). The various internal and external interferences result in highly nonlinear and stochastic time series. Although artificial neural networks (ANNs) are often used to forecast time series because of their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on an ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components (each component is a single-frequency and stationary signal) and a residual signal. The decomposed signals are used to train the neural networks, in which the hyperparameters are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed EEMD-BO-LSTM neural networks, this paper conducts two case studies (the wind speed prediction and the wave height prediction) and implements a comprehensive comparison between the proposed method and other approaches including the persistence model, autoregressive integrated moving average (ARIMA) model, LSTM neural networks, BO-LSTM neural networks, and EEMD-LSTM neural networks. The results show an improved prediction accuracy using the proposed method by multiple accuracy metrics. [ABSTRACT FROM AUTHOR]

Prior research suggests that excess (purposeful inclusion of margin beyond what is required for known system uncertainties) can limit change propagation and reduce system modifications. Reducing change costs increases system flexibility, permitting adaptions that satisfy uncertain future requirements. The benefits of excess, however, must be traded against higher costs of the initial system and likely performance decreases. Assessing the benefits and costs of excess requires evaluating what forms, locations, and magnitudes of excess inclusion are optimal. This paper improves the state-of-the-art in two ways. First, prior research has generally assessed excess in system-level properties (aggregating component properties into a single metric). The approach presented in this paper extends excess assessment to the component level so that the effects of excess on change propagation may be explicitly captured. Second, this approach holistically assesses the value of excess by evaluating both its costs and benefits. The approach borrows from Decision-Based Design and Model Based System Engineering (MBSE) in creating a generic modeling method capable of excess valuation. A desktop computer example is used for demonstrating how excess is valued in a system and the potential gains associated with excess inclusion when mining cryptocurrency. A single component optimization of the power supply capacity for the desktop is assessed to be 750 W, which balances the initial cost against the future flexibility. A system-level optimization then demonstrates the identification of critical change propagation pathways and illuminates both where and how excess may be included to inhibit change propagation. This key component was identified as the motherboard-central processing unit (CPU) slot in the tested systems. [ABSTRACT FROM AUTHOR]

In this paper, a new efficient reliability-based topology optimization (RBTO) method is proposed for structures, in which the double-loop optimization is equivalently decoupled into a sequential process based on a performance shift strategy. First, a volume minimization RBTO formulation is built for structures considering displacement or compliance reliability constraints. Second, an efficient decoupling scheme is proposed to turn the double-loop RBTO into a series of deterministic topology optimization and reliability analysis. For the reliability analysis, the reliability probability is calculated based on the probability distribution function of the performance function, and the probability distribution function can be solved by the maximum entropy method based on the raw moments calculated by the multiplicative dimension reduction method. A performance shift strategy is then applied to build an equivalence between probabilistic constraint and deterministic constraint to formulate the deterministic topology optimization. Thirdly, the adjoint variable method is applied to obtain the sensitivity information for topological design variables, and a gradient-based optimization algorithm is used to update the design variables. Finally, four typical numerical examples are used to verify the advantage of the proposed method. Compared with the traditional sequential optimization and reliability assessment (SORA) method, the proposed RBTO method results in a design with a 5.4% smaller volume value to reach the same reliability index requirement for the cantilever beam, and the function calls are 354 times less than that of the SORA method. [ABSTRACT FROM AUTHOR]

This paper demonstrates the mechanical design, analysis, and evaluation of a knee-ankle orthosis (KAO) for lower limb rehabilitation, including shape memory alloy (SMA) actuators and DC brushless motor actuators. First, the mechanical structure of the KAO is detailed, including the actuator system, transmission mechanism, monitoring device, and processing method of SMA actuators. Next, the dynamic model of SMA spring actuators in the phase transition process is established based on the thermal constitutive model of SMA. In addition, the dynamic output response of the knee joint under three working states is analyzed, and the rotation angle of SMA soft actuator during bending is described by pseudo rigid body model. Finally, the output of SMA actuator is preliminarily evaluated through experiments. The experimental results show that the maximum displacement of SMA spring actuator is 54.36 mm, the maximum restoring force during phase transformation is 4.14 N, and the maximum rotation angle of SMA soft actuator is 43.18 deg. The experimental results are consistent with the theoretical model. [ABSTRACT FROM AUTHOR]

Counterbalance valves (CBVs) are broadly applied in hydraulic manipulators, which is essential to ensure safe operation while moving a heavy load. They inevitably introduce additional energy consumption and poor dynamic performance. For the manipulator joint in tunnel boring machines, smooth and precise motion control has to be realized. However, for the forward design considerations, in this case, no systematic work has been done to determine the component parameters based on an energy-saving approach and to derive the natural frequency of the joint at the hydraulic level. To fill this gap, this paper proposes a forward design approach for the proportional valve-controlled hydraulic cylinder system containing CBVs, which is a basic hydraulic configuration adopted for the manipulator joint. Specifically, the optimization model aiming at the minimum energy consumption is defined and improved to solve the key parameters, including the pilot ratio of the CBV and the area ratio of the cylinder. By considering constraints, the other component parameters could be further calculated. After that, the theoretical derivation of natural frequency is given to evaluate the response capability of the system. The overall design procedure is presented to determine the cylinder, CBV, control valve, and constant pressure supply. Based on the validated numerical model, the energy-based design principle is proven effective and could ensure that energy saving is not less than 5% and not more than 36% on average. Furthermore, the natural frequency obtained by the sweep test is verified to be consistent with the theoretical value, and the impact of this parameter on control performance is analyzed. The results indicate that the proposed approach has an excellent guide for the forward design of such systems. [ABSTRACT FROM AUTHOR]

Designers' empathy plays an important role in supporting socially conscious design. Although widely studied in engineering design, a majority of the research on empathy focuses on designers' ability to empathize with primary users; little attention is given to empathizing with those affected in a secondary or tertiary capacity--an important consideration when evaluating the broader impacts of design decisions in the context of environmental sustainability. Moreover, little research has investigated the influence of designers' internal traits (e.g., beliefs, attitudes, and intentions) on their emphasis on environmental sustainability. Such an investigation could reveal the role of developing internal and interpersonal traits in sustainable design education. In this paper, we explore this gap by introducing student designers to a hands-on sustainable design workshop. We compared changes in their trait empathy and their beliefs, attitudes, and intentions toward sustainability from before to after the workshop. We also compared the relationship between these individual differences and participants' self-evaluated sustainability of their solutions. We see that participants reported an increase in their beliefs and intentions toward sustainability and a decrease in personal distress from before to after the workshop. Furthermore, participants who received the sustainable design lecture reported that their solutions were more sustainable in terms of disposal, salvaging, and recycling. Finally, trait empathy significantly predicted the self-evaluated sustainability of solutions on the following dimensions of sustainable design: disposal, recycling, and finding wholesome alternatives. These findings call for future work into the influence of designers' individual differences on their approach to sustainable design. [ABSTRACT FROM AUTHOR]

Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate the creation of higher performing and more complex products. This paper explores how the use of these generative design tools may impact the design process, designer behavior, and overall outcomes. Six in-depth interviews were conducted with practicing and student designers from different disciplines who use commercial generative design tools, detailing the design processes they followed. From a grounded theory-based analysis of the interviews, a provisional process diagram for generative design and its uses in the early-stage design process is proposed. The early stages of defining tool inputs bring about a constraint-driven process in which designers focus on the abstraction of the design problem. Designers will iterate through the inputs to improve both quantitative and qualitative metrics. The learning through iteration allows designers to gain a thorough understanding of the design problem and solution space. This can bring about creative applications of generative design tools in early-stage design to provide guidance for traditionally designed products. [ABSTRACT FROM AUTHOR]

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely generative pre-trained transformer 3 (GPT-3), is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance. [ABSTRACT FROM AUTHOR]

User-driven customization is a particular design paradigm where customers act as co-designers to configure products based on their needs. However, due to insufficient product usage experience, customers may design a product incompatible with their environment and needs. Such incompatibility can negatively affect the performance of some customized features or even cause product failure. As a result, customers may hesitate to customize products because additional complexities and uncertainties are perceived. Product usage context (PUC), as all the environment and application factors that affect customer needs and product performance, can be used to facilitate customer co-design in user-driven customization. Identifying individual customer's PUC can help customers foresee potential design failures, make more holistic design decisions, and be confident with their designs. Against the background, this paper proposes a PUC knowledge graph (PUCKG) construction method using user-generated content (UGC). The proposed method can convert crowdsourced corner cases into structured PUCKG to support personal PUC prediction, summarization, and reasoning. A case study of robot vacuum cleaners is conducted to validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Product evaluation throughout the design process is a fundamental task for product success, which also helps to reduce design-related costs. Physical prototyping is a common method to assess design alternatives, but often requires significant amounts of time and money. Extended reality (XR) technologies are changing how products are presented to the user, making virtual prototyping an effective tool for product evaluation. However, it is generally assumed that our perceptual and emotional responses to a product viewed in an XR modality are comparable to those elicited by the physical product. This paper reports the results of a study where a group of participants evaluated three designs of a product (i.e., umbrella stands) when viewed in a real setting, virtual reality (VR), and VR with passive haptics. Our goal was to observe the influence of visual media in product perception, and how the use of a complementary item (i.e., a physical umbrella) for interaction as well as user design expertise influence product assessment. Results show that the Jordan's psycho-pleasure category of assessment was the most affected by the presentation medium, whereas the ideo-pleasure category was the only category not influenced by the medium. We also highlight that the use of VR with passive haptics could be an effective tool for product evaluation, as illustrated by the study of umbrella stands and young consumers. Our study also shows that the user's background does not influence the level of confidence in their responses, but it can influence the assessment of certain product features. Finally, the use of a complementary item for interaction may have a significant effect on product perception. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting. [ABSTRACT FROM AUTHOR]

This paper presents a novel tensegrity structure derived from the tensegrity triplex (also called the simplex or regular triangular prism) by using the linkage-truss transformation. In this paper, the tensegrity triplex is first transformed into a 6R linkage with vertical members as revolute joints and is coined the triplex linkage. With this, a novel 6R linkage was derived, whose joint axes are perpendicular to the joint axes of the triplex linkage and is coined the ortho-triplex linkage. Rigidity analysis based on screw theory demonstrates that both obtained linkages with infinitesimal mobility are prestress stable. Finally, transforming the ortho-triplex linkage to a truss, by using cables for tensional members and struts for compressional members, leads to a novel tensegrity that is coined ortho-triplex tensegrity. A non-dimensional quadratic form is further provided to analyze the sensitivity of prestress-stability in terms of the structural parameters. The process of derivation of this novel tensegrity presents a new way of designing tensegrity structures with prestress-stability analysis based on screw theory. [ABSTRACT FROM AUTHOR]

Networking complex sociotechnical systems into larger Systems of Systems (SoS) typically results in improved performance characteristics including sustainability, efficiency, and productivity. The response, or lack thereof, of many SoS to unexpected constituent system failures undermines their effectiveness in many cases. SoS performance after faults can be improved by improving the SoS's hard (physical design) or soft (human intervention) resilience. The current approaches to increase resilience are limited due to the cost and necessary of human response increasing non-linearly with SoS scale. The limitations of current approaches require a novel design approach to improve SoS network resilience. We hypothesize that biologically inspired network design can improve SoS resilience. To illustrate this, a systems dynamics model of a Forestry Industry is presented and an optimization search over potential hard and soft resilience approaches is compared to a biologically inspired network improvement. SoS network resilience is measured through the newly developed System of System Resilience Measurement (SoSRM). Our first result provides evidence that biologically inspired network design provides an approach to increase SoS resilience beyond hard and soft resilience improvements alone. Second, this work provides evidence that having a SoS constituent fulfill the ecosystem role of detrital actor increases resilience. Third, this paper documents the first case study using the new SoSRM metric to justify a design decision. Finally, this case study provides a counter-example to the theory that increased sustainability always results in increased resilience. By comparing biologically inspired network redesign and optimized traditional resilience improvements, this paper provides evidence that biologically inspired intervention may be the needed strategy to increase sociotechnical SoS network resilience, improve SoS performance, and overcome the limitations of traditional resilience improvement approaches. [ABSTRACT FROM AUTHOR]

Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define a monotone submodular objective function that combines the diversity and quality of a team and proposes an algorithm to maximize the objective while satisfying multiple constraints. This allows us to balance both how diverse the team is and how well it can perform the task at hand. Using crowd experiments, we show that, in practice, the algorithm leads to large gains in team diversity. Using simulations, we show how to quantify the additional cost of forming diverse teams and how to address the problem of simultaneously maximizing diversity for several attributes (e.g., country of origin and gender). Our method has applications in collaborative work ranging from team formation, the assignment of workers to teams in crowdsourcing, and reviewer allocation to journal papers arriving sequentially. Our code is publicly accessible for further research. [ABSTRACT FROM AUTHOR]

In this paper, we report on our investigation of human-AI collaboration for mind-mapping. We specifically focus on problem exploration in pre-conceptualization stages of early design. Our approach leverages the notion of query expansion--the process of refining a given search query for improving information retrieval. Assuming a mind-map as a network of nodes, we reformulate its construction process as a sequential interaction workflow wherein a human user and an intelligent agent take turns to add one node to the network at a time. Our contribution is the design, implementation, and evaluation of algorithm that powers the intelligent agent (IA). This paper is an extension of our prior work (Chen et al., 2019, "Mini-Map: Mixed-Initiative Mind-Mapping Via Contextual Query Expansion," AIAA Scitech 2020 Forum, p. 2347) wherein we developed this algorithm, dubbed Mini-Map, and implemented a web-based workflow enabled by ConceptNet (a large graph-based representation of "commonsense" knowledge). In this paper, we extend our prior work through a comprehensive comparison between human-AI collaboration and human-human collaboration for mind-mapping. We specifically extend our prior work by: (a) expanding on our previous quantitative analysis using established metrics and semantic studies, (b) presenting a new detailed video protocol analysis of the mind-mapping process, and (c) providing design implications for digital mind-mapping tools. [ABSTRACT FROM AUTHOR]

Ultrasonic consolidation (USC) of thermoplastic composites is a highly attractive and promising method to manufacture high-performance composites. This work focuses on USC of dry carbon fiber (CF) fabrics with high-temperature polyphenylene sulfide (PPS) films. Experimental trials to assess feasibility of the process are time-consuming. Consequently, a predictive thermal model would facilitate process parameters selection to reduce expensive trial-and-error approaches. This paper presents a 2D finite element model of samples under consolidation, incorporating equations for viscoelastic heating, matrix phase change, and material properties. Theoretical temperature profiles for nodes of interest were compared to the corresponding experimental temperature curves for various control parameters (i.e., weld time and vertical displacement of sonotrode) and showed good agreement during heating phase. It was found that welding time values below 1750 ms were insufficient to reach melting temperature, whereas weld times above 3000 ms led to the lowest average void content (2.43 ± 0.81%). More specifically, the time the material spent above melting temperature, i.e., residence time, was established as a parameter that could estimate cases resulting in better consolidation and lower void content (time above 2600 ms for void content below 2.5%). X-ray diffraction (XRD) characterization revealed that the USC process led to mostly amorphous PPS, due to the high cooling rates (70 °C/s to 108 °C/s). Overall, the thermal model and micro-structural outcomes confirmed the feasibility of the USC process for layered composites made from dry fabric and high-temperature thermoplastic films. [ABSTRACT FROM AUTHOR]

Disassembly is an essential process for the recovery of end-of-life (EOL) electronics in remanufacturing sites. Nevertheless, the process remains labor-intensive due to EOL electronics' high degree of uncertainty and complexity. The robotic technology can assist in improving disassembly efficiency; however, the characteristics of EOL electronics pose difficulties for robot operation, such as removing small components. For such tasks, detecting small objects is critical for robotic disassembly systems. Screws are widely used as fasteners in ordinary electronic products while having small sizes and varying shapes in a scene. To enable robotic systems to disassemble screws, the location information and the required tools need to be predicted. This paper proposes a computer vision framework for detecting screws and recommending related tools for disassembly. First, a YOLOv4 algorithm is used to detect screw targets in EOL electronic devices and a screw image extraction mechanism is executed based on the position coordinates predicted by YOLOv4. Second, after obtaining the screw images, the EfficientNetv2 algorithm is applied for screw shape classification. In addition to proposing a framework for automatic small-object detection, we explore how to modify the object detection algorithm to improve its performance and discuss the sensitivity of tool recommendations to the detection predictions. A case study of three different types of screws in EOL electronics is used to evaluate the performance of the proposed framework. [ABSTRACT FROM AUTHOR]

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects' patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

Three-dimensional sheet metal forming is one of the biggest challenges in the shipbuilding industry. The forming quality of the traditionally applied line heating method depends entirely on the skills of the technicians. Moreover, this approach is inefficient and error-prone. This calls for a need to develop new forming technologies that are easy to control and more efficient. In this paper, a novel forming method called heat-assisted incremental bending is presented. The minimum energy loading path of bidirectional curvature sheet along the direction of Gaussian curvature is adapted to deform the sheet metals with a convex shape surface. Besides, the auxiliary heating method and the auxiliary supporting are exclusively included in the forming process to improve the formability and accuracy. In this study, the convex-shaped sheet metal with large curvature is obtained along the two loading trajectories in two principal curvature directions, respectively. Results proved that using the minimum energy loading trajectory and including the auxiliary supports and heating can reduce the forming force to the greatest extent and effectively reduce the number of stamping points, thus improving the sheet metal forming efficiency and forming accuracy. [ABSTRACT FROM AUTHOR]

Laser Powder Bed Fusion (LPBF) manufactured Al-5024 alloy has gained worldwide interest due to its ability to fabricate high-performance complex components. This work focuses on quantitative characterization and synergic optimization of the microhardness, tensile strength, and corrosion resistance of an LPBF manufactured Al-5024 alloy by optimization of heat treatment parameters. The effect of the isothermal heat treatment (IHT) process on the microstructure evolution, mechanical properties, and electrochemical properties of an LPBF-processed Al-4.2Mg-0.4Sc-0.2Zr alloy was systematically revealed. Results showed that superior tensile strength of 506.7 ± 10.4 MPa combined with inferior corrosion resistance was simultaneously obtained at a peak-aging condition. Based on microstructure observations by electron microscopy in backscattered mode (BSE) and transmission electron microscopy (TEM), the enhanced mechanical properties were attributed to the generation of a high number density (3.8 × 109/mm²) of grain interior precipitates, while the reduced corrosion resistance was related to the massive Al3(Sc,Zr) precipitates generated along grain boundaries. As aging time further increased, the size and spacing of the precipitates were increased, which blocked the corrosion path along grain boundaries and led to a reduction of mechanical properties and an enhancement of corrosion resistance. Unlike the expected synergistic improvement in mechanical properties and corrosion resistance, an opposite evolution tendency of mechanical properties and corrosion resistance of LPBF-processed Al-5024 alloy during heat treatment was revealed in this paper, and its intrinsic mechanism is further analyzed based on microstructure characterization. [ABSTRACT FROM AUTHOR]

Additive/subtractive hybrid manufacturing (ASHM) combines the selective laser melting (SLM) with precision milling for a better surface finish as well as geometric and dimensional accuracies of a part. In the SLM-based ASHM, the inherent characteristic of the powder bed determines that the AM and milling process are both three-axis. To produce a complex part with overhang features, alternations between SLM and milling are needed. The alternating sequence is of significance in the overall process: a large slab height of continuous SLM may cause tool/part collision; while a small slab height could reduce the fabrication efficiency. In addition, the remelting of the material during SLM causes the distortion of substantial machined surface, and thus deteriorates the surface finish of a part. However, there have little published reports on process planning for three-axis SLM-based ASHM with the consideration of alternation planning and surface finish improvement. This paper proposed a new adaptive process planning method, in which maximum slab heights of continuous SLM were calculated adaptively, and the shielding height was also employed to protect the machined surface. The overhang angles at different locations of a typical complex part were calculated by a triangular facet method. A collision detection method to justify the interference of the tool and as-built part was proposed, and the maximum slab heights were then determined. ASHM experiment was conducted to fabricate small samples with different overhang angles, and the shielding height was determined based on the surface quality. A propeller with overhang features was manufactured using this new planning method. The result shows that the proposed method improved the fabrication efficiency, reduced the number of process alternations as well as improved the surface finish. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) has gained prominence in the manufacturing industry for its ability to produce lightweight components. As the raw material used is in powder form, the stochastic nature of the powder distribution influences the powder layer thickness and affects the final build quality. In this paper, a multi-layer multi-track simulation study is conducted to investigate the effect of stochastic powder distribution on the layer thickness and plastic strain in a printed geometry. A faster simulation approach is employed to simulate multiple layers. First, the powder distribution and the melt layer thickness of the first layer are obtained from discrete element method (DEM) and computational fluid dynamics (CFD) simulations respectively. Next, the melt layer thickness of the first layer is used as an input to the finite element (FE) based structural mechanics solver to predict the deformation and layer thickness of subsequent layers. Two nominal layer thicknesses 67.4 µm and 20 µm were considered. Two particle size distribution (PSD) configurations and two scanning strategies were tested. The results showed that variation in PSD and scanning strategy leads to variation in layer thickness which in turn leads to variation in the plastic strain that is known to drive the deformation. However, the nominal layer thickness of 20 µm was found to be less influenced by the PSD configuration. The proposed simulation approach and the insights achieved can be used as inputs in the part-scale simulations for geometric robustness evaluation in the early design stages of SLM products. [ABSTRACT FROM AUTHOR]

The desire for thin-film silicon is motivated by the growing needs for flexible electronics, compact packaging, and advanced solar power. In previous work we have presented exfoliation as means to a cost effective way to achieve thin-film silicon and described an open loop prototype exfoliation tool that could be used to produce improved films compared to previous methods. However, controllable film thickness, film uniformity, and surface roughness were all challenges with the open loop setup. This paper describes the design, construction, and testing of an improved controlled exfoliation tool with load compensation and inline metrology for closed loop control of the exfoliation process. The exfoliation performance results are compared to those from the proof-of-concept tool and show 53% improvement in silicon uniformity and 67% improvement in average surface roughness. These improvements can be attributed to the addition of load compensation and the improvement in the precision motion of the stage, respectively. [ABSTRACT FROM AUTHOR]

This paper studied the effects of two welding modes, i.e., keyhole penetration and full penetration, on laser welding of two zinc-coated steel stack-ups of the same total sheet thickness but different sheet thickness combinations. The effects of welding modes on keyhole and spatter behavior were studied. It was found that keyhole penetration welding led to little spatter and mass loss for a thick-gage stack-up of the same top and bottom sheet thickness (two 1.5 mm zinc-coated steel sheets, viz., Stack-up S). This was confirmed by numerically calculated low Zout values which indicate a low potential of spatter due to zinc outgassing insufficiency. For a stack-up of thin top and thick bottom sheet combination (1.1 mm/1.9 mm, viz., Stack-up D), full penetration mode is more preferred generating less spatter than the keyhole penetration mode. This was attributed to an enlarged keyhole size at the faying interface in the full penetration mode and the relatively thinner top sheet (1.1 mm thick) compared to the bottom sheet (1.9 mm thick). It was confirmed by the low average and maximum values of Zout. In summary, to reduce the spatter in laser welding of zinc-coated steel, the keyhole penetration mode welding is preferred for the stack-up with the top and bottom sheets of similar thickness, and the full penetration mode is more suitable for the stack-up having a much thinner top sheet than the bottom sheet. [ABSTRACT FROM AUTHOR]

This paper designed a unique field shaper, which can crimp multiple end tubes in single discharge energy. This design will surely provide an advantage of crimping different diameter tube-tube-tube and tube-tube-rod in single discharge energy. The design of the field shaper not only shows the feasibility of the multi-tube-tube and multi-tube-rod crimping for different electrical and fluid transfer applications but also explores the idea that multi-joining is also possible if carried out at the correct machine and tool parameters. The feasibility of the new process was proven by doing numerical analysis using the electromagnetic (EM) module of the ls-dyna software. Discussions on variables like current density, magnetic field, effective stress, impact velocity, and slit change are carried out. The work was validated using the tube deformation, the cross-sectional analysis of the crimped samples, and the impact velocity of the flyer tube that was measured using the PDV system. Post-processing of the samples was also carried out like a pull-out test, which justifies the numerical data interpreted. Some new important observation of the pressure waves generated during the process is discussed. Those intense pressure waves still exist after the peak current value is achieved during the current first cycle. As predicted, it may also be one of the reasons affecting the lifecycle of the field shaper. The design of the field shaper will provide a unique method for carrying out the multiple samples crimping in the single discharge energy and enhancing the production rate. [ABSTRACT FROM AUTHOR]

With the increasing performance and appearance requirements of electronic products, higher standards have been put forward for the machining accuracy and quality of the precision copper electrode for product molds. The precision copper electrode is large in material plasticity and small in size. Burrs are easily generated during the cutting process, and subsequent cleaning is difficult. In this paper, according to the geometric features of the copper electrode, the distribution type and formation mechanism of burrs are deeply analyzed. The influence of cutting-edge geometry on burrs lateral dimension is investigated by simulation. Further, the new left-handed fillet milling cutter is designed that takes into account the simultaneous cutting of the end edge and the circumferential edge. Finally, based on the new milling cutter, burrs suppression machining process adapted to different geometric features is planned. Experiments show that the new cutter and process can greatly reduce the size of burrs and basically realize burrs-free machining compared with the conventional process of the ordinary right-handed milling cutter, and the service life of the new cutter is longer, which can meet the actual machining needs. [ABSTRACT FROM AUTHOR]

Cellular materials are critical elements for mechanical metamaterials design and fabrication. Tailoring the internal cellular material structural pattern can achieve a much broader range of bulk properties than the constituent materials, thus enabling the metamaterial design with extraordinary properties. Studying cellular materials' mechanical properties is critical for understanding metamaterials' structural design, and macroscale performances. This paper investigates and validates the mechanical properties of two classes of smooth cellular structures defined by deterministic and stochastic functions, respectively. A mechanical profile is proposed to depict the effective mechanical properties of these smooth cellular structures. We developed such profiles numerically based on computational homogenization and validated them by simulations and physical tests. In physical tests, we printed the generated structures on a fused deposition modeling (FDM) printer and conducted compression tests to verify the numerical homogenization and simulation results. Through the comparison studies, we summarize these cellular materials' mechanical profiles defined by distinct principles. Based on the experimental results, several cellular structural design guidelines are derived for mechanical metamaterials development, which provides foundations for cellular materials database establishment and sheds light on future exotic metamaterials fabrication. [ABSTRACT FROM AUTHOR]

Eighteen percent Ni maraging steels are high performance Fe-Ni martensite-based alloys with ultra-high strength and good toughness. They find applications in strategic sectors, joining of thick sections often coming into picture. Welding of thick section involves a longer processing time, more passes, and a higher heat-input. Double-pulsed gas metal arc welding (DP-GMAW) is an emerging welding technique, well suited for joining thick sections. DP-GMAW is capable of controlling the solidification parameters, weld pool geometry, and cooling rate at a reduced heat-input. The major concern regarding the welding of maraging steel is the formation of the reverted austenite (RA) phase in the fusion zone (FZ). The formation of RA deteriorates the mechanical performance of welded joints. The presence of RA can be supressed by the usage of suitable welding techniques and proper post-weld heat treatments (PWHTs). DP-GMAW process was employed to carry out the welding; studies on the joints produced are reported in this research paper. The studies also included the effect of various PWHTs on the metallurgical and mechanical properties of the maraging steel weldments. The research used three distinct PWHTs: direct aging (DA), solutionizing + aging (SA), and homogenizing + solutionizing + aging (HSA). The FZ microstructures under DA and SA conditions show that there is RA at the cell boundaries. However, there was no evidence of RA in FZ following HSA. The energy dispersive spectra (EDS) analysis of the as-welded FZ showed segregation along the grain boundaries (GBs). This led to the premature formation of RA upon subsequent aging. The SA treatments proved inadequate to totally eliminate RA in the microstructure. On the other hand, the HSA treatments were effective in evening out concentration differences and preventing formation of RA. This study demonstrates that DP-GMAW combined with HSA treatment has the best mechanical properties. [ABSTRACT FROM AUTHOR]

Resistance spot welding of aluminum alloys (Al RSW) is known to be very challenging, in part due to the undesirable and hard-to-predict asymmetric growth of the weld nugget. In this paper, two 1.2 mm thick AA6022-T4 aluminum alloy sheets were joined by RSW using multi-ring domed (MRD) electrodes to experimentally investigate the effect of sheet surface condition, electrode surface morphology, electrode material, weld schedule, and electrode tip condition on asymmetric nugget growth, as well as the resulting weld microstructure and coach peel strength. It was found that asymmetric nugget growth was most strongly influenced by sheet surface conditions and electrode materials at the positive polarity electrode side. The asymmetric nuggets exhibited a smaller nugget angle in relation to the faying surface and a wider partially melted zone (PMZ) in the sheet adjacent to the negative electrode which reduced weld mechanical strength and toughness. In addition, the asymmetric nugget growth also caused asymmetric electrode/workpiece interaction. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the traditional trajectory planning method cannot flexibly control the velocities and the arrival time of critical points in the trajectory, this paper proposes a non-uniform rational B-spline interpolator based on double-step signal and finite impulse response filters. The interpolator consists of two modules: a pre-processing module and a real-time interpolation module. To reduce the contour error, first, the pre-processing module scans the curve for critical points and sets their velocities according to the chord error. Then, the curve is split, and for each segment, the length and inverse length functions are calculated to eliminate the length error and enhance the real-time performance. Moreover, to control the arrival time and smooth the velocity, the real-time interpolation module generates the trajectory by processing the double-step signal with three finite impulse response filters, which satisfies the constraints of velocity, acceleration, jerk, and displacement. Simulation results indicate that the proposed interpolator can accurately reach the critical points at the set arrival time with smooth velocity and low chord error, and experimental results on a SCARA robot show that the proposed interpolator is feasible and effective. [ABSTRACT FROM AUTHOR]

Manufacturing or repairing parts on-site, high geometric freedom, and feedstock efficiency have long been regarded as nascent capabilities of additive manufacturing (AM) technologies. Researchers aim to adopt these capabilities for the future of space exploration, and polymer AM demonstrations in space were achieved in 2014; however, methods to process metals and other materials are needed. This paper provides a comprehensive review of AM research tested on reduced-gravity platforms from academia and industry across the globe. In addition, complementary processes and technologies under development are summarized. Reports from the literature are categorized by established AM process terminology and processed material. Lastly, alternatives to enabling metal AM in space are discussed, and knowledge gaps are presented. [ABSTRACT FROM AUTHOR]

The increasing demand for customized products and rapid prototyping triggers the development of dieless forming process where the need of part-specific dies is eliminated compared to conventional forming processes. This paper presents a heat-assisted three-roll incremental rolling system capable of producing rods and tubes of various diameters without the need for die/tool replacement. The system incorporates two possible heating mechanisms, namely, an induction heater and a high-current power supply for electrically assisted forming. Due to its high flexibility and hot forming ability, this rolling system is ideal for low volume production or scientific research. The newly fabricated rolling machine was initially evaluated using low carbon steel rods. The experiments have shown that the machine can produce metallic rods down to 6 mm in diameter without apparent spiral marks on the outer surface of the rolled product using multiple passes. The finite element method (FEM) was used to analyze the reaction moment, depth of the spiral marks, and the effect of axial feeding speed on the spiral marks. The simulation model predicted the results of the experiments with less than a 10% error. The work established a flexible rolling platform for possible future research, which can include the study of interface behavior in the thermo-mechanical rolling processes, and the study of material behavior in this multi-axial multi-physics environment. [ABSTRACT FROM AUTHOR]

Inspired by porous morphology in nature, such as bone and lung tissues, synthetic porous materials are widely adopted in engineering applications that require lightweight, thermal resistance, energy absorption, and structural flexibility. One of the main challenges in the current porous material manufacturing techniques is their limited control over individual pore size, connectivity, and distribution. This paper presents a novel additive manufacturing process to fabricate porosity-embedded structures by integrating stereolithography and inkjet printing using a sacrificial liquid-water. A solenoid-based inkjet nozzle dispenses water droplets onto a layer of liquid photopolymer resin. Then the resin layer is photocured by a mask image projection device using a digital light processing device. The photocuring process defines the layer profile and captures the deposited water droplets in the solidified layer. The refilled fresh resin will further embed water droplets and form a new layer for the subsequent water droplet deposition. Three-dimensional (3D) structures with embedded water droplets can be printed layer-by-layer. The captured water will evaporate when heated, leaving an air-filled porous 3D structure. By selectively depositing water droplets and varying inkjet printing parameters, including pressure, nozzle opening time, and jetting frequency, the micropores whose sizes from 100 µm to 500 µm and distributions within the 3D-printed part can be modulated. This hybrid process can fabricate 3D structures with homogenously distributed pores and graded polymer structures with varying porosities. The elastic modulus of 3D-printed foam structures in different pore distributions has been tested and compared. [ABSTRACT FROM AUTHOR]

Precision medicine is an emerging healthcare delivery approach that considers variability between patients, such as genetic makeups, in contrast to the current one-size-fits-all approach that is designed to treat the average patient. The White House launched the Precision Medicine Initiative in 2015, starting an endeavor to reshape healthcare delivery. To translate the concept of precision medicine from the bench to practice, advanced manufacturing will play an integral part, including the fabrication of personalized drugs and drug delivery devices and drug screening platforms. These products are highly customized and require robust yet flexible manufacturing systems. The advanced manufacturing field has rapidly evolved in the past five years. In this state-of-the-art review, products manufactured for precision medicine will be introduced, followed by a brief review of processing materials and their characteristics. A review on different manufacturing processes applicable to those aforementioned products is provided. The current status of the development of regulatory submission and quality control considerations are also discussed. Finally, this paper presents a future outlook on manufacturing processes used for precision medicine. [ABSTRACT FROM AUTHOR]

In many origami-based applications, a device needs to be maintained in one or more fold states. The origami stability integration method (OSIM) presented in this paper provides an approach for graphically combining various techniques to achieve stability. Existing stability techniques are also categorized into four groups based on whether they are intrinsic or extrinsic to the origami pattern and whether they exhibit gradual or non-gradual energy storage behaviors. These categorizations can help designers select appropriate techniques for their application. The paper also contains design considerations and resources for achieving stability. Finally, two case studies are presented which use the OSIM and the technique categorization to conceptualize stability in origami-based devices. [ABSTRACT FROM AUTHOR]