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The intricate structure of human hands requires personalized orthotic treatments, especially with the growing aging population's demand for accessible care. While traditional orthoses are effective, they face challenges of cost, customization time, and accessibility. Additive manufacturing, particularly material extrusion (MEX) techniques, can effectively address challenges in orthotic device production by enabling automated, complex, and cost-effective solutions. This work aims to provide engineers with a comprehensive set of design considerations for developing hand orthoses using MEX technology, focusing on applying design for additive manufacturing principles, to enhance rehabilitation outcomes. This objective is achieved by establishing design requirements for hand orthoses, reviewing design choices and methodologies across conventional and state-of-the-art MEX-based devices, and proposing an innovative approach to orthotic design. Hand orthosis design requirements were gathered through workshops with occupational therapists and categorized into engineer-, medical-, and patient-specific needs. A review of 3D-printed hand orthoses using MEX analyzes various design approaches, providing insights into existing solutions. The study introduces a modular design concept aimed at improving rehabilitation by enhancing customizability and functionality. It highlights the potential of MEX for creating personalized, cost-effective orthoses and offers recommendations for future research, to optimize designs and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes. [ABSTRACT FROM AUTHOR]

Redesigning existing parts is challenging without keen insights into capabilities afforded by AM and with the absence of systematic design methods for the early product design stage. Therefore, this study proposes a systematic design method to leverage fully AM design benefits for reconceptualizing product architecture, which leads to consolidate parts and simplify product architecture. The proposed design method consists of four steps to achieve the goal. First, a baseline product architecture should be identified as a starting point of reconceptualization. Second, candidates for reconceptualization are identified by design principles for AM. Third, AM design benefits are recommended by the developed knowledgebase and query language, which can replace functions of the candidates. AM design benefits are applied to reconceptualize existing part design and lead to more efficient product architectures. Lastly, the reconceptualized product architecture is evaluated. To demonstrate usefulness of the proposed method, a case study is performed on an electric motorcycle. [ABSTRACT FROM AUTHOR]

A joystick set for controlling different degrees of freedom was designed, materialized and characterized. The joystick was designed for 3D printing, improved through several iterations, producing multiple samples using multi-material additive manufacturing. Two different materials (one structural and another conductive) were used in the filament material extrusion process to obtain resistive-based sensing unit elements. The sensor was put into operation via a voltage divider circuit. Experimentation was implemented in a testing bed (analogic sensor), in a real context (binary mode) and in a simulation environment (multiple-state signal) for the command of a robotized wheelchair. The final design is capable to meet the requirements of reducing the material used and the printing times. The results reveal the possibility of using 3D printed resistive-based sensing units to implement binary signals as well as multiple state signals and the sensor set has been evaluated by controlling a robotized wheelchair. [ABSTRACT FROM AUTHOR]

Orthopaedic trauma is a predominant cause of patient mortality and prompt surgical interventions. The locking compression plate (LCP) is widely regarded as a gold standard in treating bone fractures. However, current fixation materials, such as 316 L stainless steel (316 L SS) and titanium (Ti) alloys induce stress shielding. A novel design approach is considered to design the next generation of bone plates to minimize stress shielding through design for additive manufacturing principles. Embedding of 316 L SS sheets within the bone plate structure has improved the mechanical properties by 200% compared with PLA-only plate, resulting in a flexural modulus of 9.3 GPa, which is closer to the cortical bone range of 10–25 GPa. In contrast, commercial plates measured 51 GPa for Ti and 112 GPa for 316 L SS. The novel plates showed to have a lower economic and environmental impact than commercial ones. Additively manufactured bone plates are more sustainable and cost-effective than current commercial ones.

Purpose: This paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying geometric constraints and classifying the building orientation (BO) of AM parts to reduce stresses and support structures (SSs). To this end, artificial intelligence (AI) networks are being developed to automate design for additive manufacturing (DfAM). Design/methodology/approach: This study considers three geometric constraints for their correction by convolutional autoencoders (CAEs) and transfer learning (TL). Furthermore, BOs of AM parts are classified using generative adversarial (GAN) and classification networks to reduce the SS. To verify the results, finite element analysis (FEA) is performed to compare the stresses of modified components with the original ones. Moreover, one sample is produced by the laser-based powder bed fusion (LB-PBF) in the BO predicted by the AI to observe its SSs. Findings: CAE and TL resulted in promoting the manufacturability of TO components. FEA demonstrated that enhancing manufacturability leads to a 50% reduction in stresses. Additionally, training GAN and pre-training the ResNet-18 resulted in 80%, 95% and 96% accuracy for training, validation and testing. The production of a sample with LB-PBF demonstrated that the predicted BO by ResNet-18 does not require SSs. Originality/value: This paper provides an automatic platform for DfAM of TO parts. Consequently, complex TO parts can be designed most feasibly and manufactured by AM technologies with minimal material usage, residual stresses and distortions. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is often used to create designs inspired by topology optimization and biological structures, yielding unique cross-sectional geometries spanning across scales. However, manufacturing defects intrinsic to AM can affect material properties, limiting the applicability of a uniform material model across diverse cross-sections. To examine this phenomenon, this paper explores the influence of specimen size and layer height on the compressive modulus of polycarbonate (PC) and thermoplastic polyurethane (TPU) specimens fabricated using fused filament fabrication (FFF). Micro-computed tomography imaging and compression testing were conducted on the printed samples. The results indicate that while variations in the modulus were statistically significant due to both layer height and size of the specimen in TPU, variations in PC were only statistically significant due to layer height. The highest elastic modulus was observed at a 0.2 mm layer height for both materials across different sizes. These findings offer valuable insights into design components for FFF, emphasizing the importance of considering mechanical property variations due to feature size, especially in TPU. Furthermore, locations with a higher probability of failure are recommended to be printed closer to the print bed, especially for TPU, because of the lower void volume fraction observed near the heated print bed. [ABSTRACT FROM AUTHOR]

In the development of innovative and high-performance products, design expertise is a critical factor. Nevertheless, novel manufacturing processes often frequently lack an accessible comprehensive knowledge base for product developers. To tackle this deficiency in the context of emerging additive manufacturing processes, substantial design knowledge has already been established. However, novel additive manufacturing processes like continuous fiber-reinforced material extrusion have often been disregarded, complicating the process's wider dissemination. The importance of design knowledge availability is paramount, as well as the need for user-friendly design knowledge preparation, standardized structure, and methodological support for accessing the accumulated knowledge with precision. In this paper, we present an approach that provides formalized opportunistic and restrictive design knowledge, ensuring both the comprehensive exploitation of process-specific potentials and the consideration of restrictive limitations in the construction of components. Opportunistic knowledge, presented as principle cards, is systematically derived, prepared, and made accessible. Moreover, an access system is developed to ensure the comprehensive utilization of process-specific potentials throughout the development process. Furthermore, we propose linking these principles through a synergy and conflict matrix, aiming to consider synergistic principles and identify potential conflicts at an early stage. Additionally, an approach to provide restrictive design knowledge in the form of a design rule catalog is proposed. The application of the knowledge system is demonstrated exemplarily using a weight-optimized component. [ABSTRACT FROM AUTHOR]

This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) tools and thus maximize potential improvements, such as weight, waste and cost reduction, strength, and part consolidation. Implicit in all studies reported in the literature is the fundamental assumption that the use of GD, irrespective of user experience or approach followed, yields high-performing and/or comparable design outputs. This work demonstrates the contrary and shows that achieving high performance with GD tools requires careful consideration of study setup and initial conditions. It is further shown that, when coupled with the inherent variability of AM parts, the potential variation in the performance of the design output can be significant, with poorer designs achieving only a fraction of that of higher-performing designs. This investigation shows how AM by Material Extrusion (MEX), which is used to manufacture components with polylactic acid (PLA), varies through different design pathways, bridging MEX and GD. Through a practical study across nine independently generated designs, the breadth of performance—due to initial GD conditions and MEX part strength unpredictability—is shown to reach 592%. This result suggest that current GD tools, including their underlying workflows and algorithms, are not sufficiently understood for users to be able to generate consistent solutions for an input case. Further, the study purports that training and consideration on GD setup are necessary to apply GD toolsets to achieve high-performing designs, particularly when applied in the context of MEX. [ABSTRACT FROM AUTHOR]

Nuclear microreactors (MRs) have attracted significant attention for their versatility in installation locations, especially with the growing demand for sustainable green energy in large data processing facilities. However, ensuring their safe operation necessitates frequent inspections, which can be challenging for practical efficient management. This study proposes a novel approach using direct energy deposition to incorporate an optical fiber sensor into MR components, enabling real-time monitoring with artificial intelligence. The embedded optical fiber generates real-time data that allows for AI-driven in-vivo thermal deformation analysis. Our smart MR component can detect structural anomalies, identify abnormal operations, and assess operational conditions through augmented reality interfaces and AI technology.

Recent technological advances in the field of Additive Manufacturing (AM) and the increasing need in Regenerative Medicine (RM) for devices that better and better mimic native tissue architecture are showing limitations in the current scaffolds fabrication techniques. A switch from the typical layer-by-layer approach is needed to achieve precise control on fibres orientation and pores dimension and morphology. In this work a new AM apparatus, the RAVEN (Robot-Assisted Volumetric ExtrusioN) system, is presented. RAVEN is based on a 7-DOF robotic arm and an extruder and allows for volumetric extrusion of polymeric filaments. The development process, namely the robotic motion optimisation, the optimisation towards small-scale trajectories, the custom-made hardware/software interfaces, and the different printing capabilities are hereby presented. The successful results are promising towards future advanced applications in which the ability of the robot to change its configuration while printing will be crucial.

The current designs of low-frequency acoustic absorption metamaterials suffer from inadequate prediction accuracy and low efficiency. Herein, by integrating impedance theory with artificial neural networks, we propose an optimisation method of unit impedance correction (UIC). Meanwhile, a mutated honeycomb structure with teardrop-shaped apertures (MHSTA) fabricated by stereolithography is proposed, which can enhance the low-frequency coupling performance through rationally arranging the relative positions of the apertures. To verify prediction ability of the UIC method, the 50 mm-thickness typical structures of MHSTA are designed for two low-frequency ranges (220–500 Hz and 230–550 Hz). Compared with the simulation, the UIC result can reach the maximum relative error of 3.9% and the maximum absolute error of 0.033, which ensures the accurate prediction commendably. The experiment also demonstrates that the acoustic absorption coefficients are highly consist with the UIC result. This work provides a novel strategy of precise design for low-frequency acoustic absorption metamaterials.

ABSTRACTThe increasingly distressing problem of noise pollution prompts the rapid research and development of lattice-based acoustic materials for practical applications. These lattices boast excellent mechanical properties and unlimited design freedom made possible using additive manufacturing technologies. Currently, there is a lack of analytical mathematical models relating the lattice geometry to the acoustic models and the absence of software dedicated to the acoustic modelling of strut lattice-based sound absorbers. This work presents LattSAC, an acoustics calculator that takes in all the geometrical parameters of the lattice structure and outputs directly the sound absorption coefficients (SAC) and their statistics. Hidden within the code is the novel Multi-layered Micropore-Cavity (MMC) model with semi-empirical mathematical models well-trained using experimental data to output the SACs with excellent accuracies. This software aims to accelerate the research and development of lattice-based acoustic absorbers for actual applications. It also functions as a valuable tool for users looking to design sound-absorbing materials using lattice structures. The software installer may be downloaded for free from the following GitHub repository: https://github.com/JunWeiChua/LattSAC. If there are any suggestions or contributions, the user may write to zhaiweigroup@gmail.com.

Additive manufacturing (AM) typically is more costly than traditional manufacturing processes, but it opens new opportunities for design that are often not fully utilized because of its price. Bonding complex additively manufactured structures to conventional parts is a way of reducing cost, while maintaining design freedom. However, not all polymers used in AM are easily bondable. These materials are typically pre-treated with plasma in order to increase adhesion at the interface. By utilizing the design freedom of AM, it becomes possible to omit this pre-treatment step by producing undercut areas that can be filled by the adhesive during bonding. Instead of adhesive bonding, forces are transmitted by positive fit of the adhesive and the AM part. By producing complex lattice structures with AM that are surrounded completely by adhesive, the strength of these bonds can further be increased. The design of such structures is highly complex, however. This publication shows that AM lattice structures can greatly improve bonding strength of low adhesive polymers and suggest a way of characterizing such structures in lap shear tests in order to separate design influence on such bonds. It could be shown that resulting shear strength is comparable to bonding after plasma pre-treatment. [ABSTRACT FROM AUTHOR]

This study investigates the synergy between additive manufacturing (AM) technologies and computational design strategies in jewellery and how that synergy can be successfully exploited to extend innovation in that field further. A case study called Ecdysis, a bioinspired jewellery collection, is presented. A dedicated computational algorithm has been developed and is described in detail. This algorithm allows for the exploitation of the shape and functional complexity dimensions allowed by AM and the control of the printability of the generated concept. Shape and functional complexity are exploited to mimic the beauty and dynamism of snakes' slithering mechanism. At the same time, starting from the developed algorithm, multiple digital models and physical prototypes have been fabricated, leveraging material extrusion, vat photopolymerisation, and powder bed fusion processes. This further development step of the collection thus confirms the versatility of both the proposed approach and AM technologies for jewellery. Therefore, the paper demonstrates how unique wearing experiences can be created and how uniqueness can be simultaneously preserved and democratised in jewellery by deepening the synergy between AM technologies and computational strategies. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates the potential of using the material extrusion (MEX) process for recycling waste tire rubber (WTR). By investigating the process parameters, mechanical behaviour and morphological characterisation of a thermoplastic polyurethane-waste tire rubber composite filament (TPU-WTR), this study aims to establish a framework for end-of-life tire (ELT) recycling using the MEX technology. Design/methodology/approach: The research assesses the impact of various process parameters on the mechanical properties of the TPU-WTR filament. Hysteresis analysis and Poisson's ratio estimation are conducted to investigate the material's behaviour. In addition, the compressive performance of diverse TPU-WTR triply periodic minimal surface lattices is explored to test the filament suitability for printing intricate structures. Findings: Results demonstrate the potential of the TPU-WTR filament in developing sustainable structures. The MEX process can, therefore, contribute to the recycling of WTR. Mechanical testing has provided insights into the influence of process parameters on the material behaviour, while investigating various lattice structures has challenged the material's capabilities in printing complex topologies. Social implications: This research holds significant social implications addressing the growing environmental sustainability and waste management concerns. Developing 3D-printed sustainable structures using recycled materials reduces resource consumption and promotes responsible production practices for a more environmentally conscious society. Originality/value: This study contributes to the field by showcasing the use of MEX technology for ELT recycling, particularly focusing on the TPU-WTR filament, presenting a novel approach to sustainable consumption and production aligned with the United Nations Sustainable Development Goal 12. [ABSTRACT FROM AUTHOR]

Biodigital Architecture and Design emerges in the intersection between nature-based design and digital technologies. This article presents the results of a project that aimed to integrate three-dimensional growth patterns from nature into a stool design series through Computational Design and Additive Manufacturing methods. The project methodology includes (1) Pattern from nature selection, (2) Generative design, (3) 3D printing, including scale models and prototypes, (4) Compression tests, and (5) Generative optimization. Findings indicate that the branching pattern was the lightest-weight pattern while showing the highest specific resistance compared to the other models evaluated. Branching pattern also took the least amount of time and material to print, these findings contribute to the decision-making process for future work. Regarding computational design method, it resulted in several design alternatives, with complex, unpredictable and efficient mechanical behavior geometries. Future work would include to a variety of patterns from nature, generative design, optimizations, and prototypes. [ABSTRACT FROM AUTHOR]

The choices made in the early design stage (EDS) will largely define the environmental impacts of a product. The purpose of this paper is to develop an eco-design method used for assessing semi-quantitatively the sustainability of an additively manufactured product since the EDS. This article presents a semi-quantitative method to support EDS-conscious environmental decisions. A novel Sustainable-Failure Mode, Effect, and Criticality Analysis (S-FMECA) tool is developed to support designers in the conceptual design phase, to guide the choices, and to provide a valuable evaluation of the future additively manufactured product. Through the integration of the environmental aspects in FMECA analysis, systematic prevention of errors, and enhancement of sustainability since the EDS would be the main advantage of this tool.

The intricate structure of human hands requires personalized orthotic treatments, especially with the growing aging population’s demand for accessible care. While traditional orthoses are effective, they face challenges of cost, customization time, and accessibility. Additive manufacturing, particularly material extrusion (MEX) techniques, can effectively address challenges in orthotic device production by enabling automated, complex, and cost-effective solutions. This work aims to provide engineers with a comprehensive set of design considerations for developing hand orthoses using MEX technology, focusing on applying design for additive manufacturing principles, to enhance rehabilitation outcomes. This objective is achieved by establishing design requirements for hand orthoses, reviewing design choices and methodologies across conventional and state-of-the-art MEX-based devices, and proposing an innovative approach to orthotic design. Hand orthosis design requirements were gathered through workshops with occupational therapists and categorized into engineer-, medical-, and patient-specific needs. A review of 3D-printed hand orthoses using MEX analyzes various design approaches, providing insights into existing solutions. The study introduces a modular design concept aimed at improving rehabilitation by enhancing customizability and functionality. It highlights the potential of MEX for creating personalized, cost-effective orthoses and offers recommendations for future research, to optimize designs and improve patient outcomes.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

The design of graded and multifunctional lattice cores is driven by the increasing demand for high-performance components in lightweight engineering. This trend benefits from significant achievements in additive manufacturing, where the lattice core and the face sheets are fabricated simultaneously in a single print job. This work systematically compares the mechanical performance of sandwich panels comprising various graded lattice cores subjected to concentrated loads. In addition to graded lattice cores, uniform lattices and conventional honeycomb cores are analyzed. To obtain an optimized graded lattice core, a fully stressed design method is applied. Stresses and displacements are determined using a linear elastic analytical model that allows grading the core properties in a layerwise manner through the core thickness. The analysis indicates the superior performance of graded lattice cores compared to homogeneous lattice cores. However, conventional honeycombs outperform graded lattice cores in terms of load-to-weight ratio and stiffness-to-weight ratio. This study provides valuable insights for the design of lattice core sandwich panels and the advantages of several design approaches. [ABSTRACT FROM AUTHOR]

Roughly 25% of all energy-related greenhouse gas emissions (GHG) are attributable to the transportation sector, which accelerates global warming. Vehicle lightweighting and GHG emissions are intrinsically correlated. This paper reviews the advances in lightweight materials, particularly advanced high-strength steel, reinforced composites, aluminum, magnesium, and titanium alloys. The challenges limiting the mass adoption of vehicle lightweighting, including material production energies, vehicle operational efficiencies, and other substitution factors such as life-cycle analysis, environmental impacts, manufacturability, cost-effectiveness, crashworthiness, and recyclability, are highlighted. Emerging state-of-the-art techniques such as design for additive manufacturing (DfAM), multi-scale modeling of materials, multi-material design, bionic-inspired designs, and Industry 4.0 in vehicle lightweighting are also explored. The paper details an experimental case study on material substitution of a steel vehicle door hinge with aluminum alloy (A6061-RAM2). A weight reduction of 66% was realized. Selective laser melting (SLM) was used to fabricate the aluminum hinge samples. The samples did not fracture when a transverse load of 4500 N and a longitudinal load of 5500 N were applied bi-directionally from the X and Y axes of the vehicle, respectively. [ABSTRACT FROM AUTHOR]

Purpose: The work aims to propose a comprehensive design framework for additive manufacturing and apply it to a case study of the height adjuster handle of a car. The aim is to provide designers and engineers with a practical example of how the framework can be used to design a complex part for additive manufacturing. The article also aims to demonstrate the potential benefits of additive manufacturing in the automotive industry, such as improved performance, reduced times, and cost savings. Additionally, the article aims to compare the developed framework with existing design for additive manufacturing (DFAM) methodologies and highlight its unique features and advantages in designing the height adjuster handle. Design/methodology/approach: The study used qualitative and quantitative data collection and analysis methods. The first phase of the study involved a systematic review of existing DfAM methodologies and a critical analysis of their strengths and weaknesses. Based on this analysis, a new DfAM framework is developed, which aims to address the limitations of existing frameworks and provide a comprehensive design approach for additive manufacturing. The second phase of the study involved applying the developed framework to a case study of a complex automotive part - a height adjuster handle. The design requirements of the height adjuster handle were identified based on the principles of DfAM, and the part was designed using computer-aided design (CAD) software and optimised topologically using ANSYS software. In the third phase, the performance of the height adjuster handle designed using the developed framework was compared with a part manufactured with injection moulding technology. The comparison was based on various performance criteria, including mechanical properties, dimensional accuracy, and production time and cost. Findings: The findings of the study demonstrate the effectiveness of the developed design framework for additive manufacturing (DfAM) in producing a complex automotive part with improved performance characteristics and reduced lead time. Research limitations/implications: Although the results of the study provide important insights into the effectiveness of the developed DfAM framework for producing a complex automotive part, there are some limitations to the research that should be considered, such as the case study involved the design of a single part, and the results may not be generalisable to other parts or applications. Further research is needed to validate the effectiveness of the DfAM framework for a broader range of automotive parts. Practical implications: The findings of the study have important practical implications for the automotive industry. The developed DfAM framework can be used in the FDM technology. It can be used as a decision aid in the manufacturing of FDM parts in order to improve the efficiency and cost-effectiveness of the production process for complex automotive parts. Originality/value: The value of the study is the development of a novel DfAM framework and the demonstration of its effectiveness in a case study. The proposed framework can be used as a reference for future research. It can also provide practical guidance for industry professionals seeking to improve the efficiency and cost-effectiveness of their additive manufacturing processes. [ABSTRACT FROM AUTHOR]

In the additive manufacturing laser powder bed fusion (L-PBF) process, the optimization of the print process parameters and the development of conduction zones in the laser power (P) and scanning speed (V) parameter spaces are critical to meeting production quality, productivity, and volume goals. In this paper, we propose the use of a machine learning approach during the process parameter development to predict the melt pool dimensions as a function of the P/V combination. This approach turns out to be useful in speeding up the identification of the printability map of the material and defining the conduction zone during the development phase. Moreover, a machine learning method allows for an accurate investigation of the most promising configurations in the P-V space, facilitating the optimization and identification of the P-V set with the highest productivity. This approach is validated by an experimental campaign carried out on samples of Inconel 718, and the effects of some additional parameters, such as the layer thickness (in the range of 30 to 90 microns) and the preheating temperature of the building platform, are evaluated. More specifically, the experimental data have been used to train supervised machine learning models for regression using the KNIME Analytics Platform (version 4.7.7). An AutoML (node for regression) tool is used to identify the most appropriate model based on the evaluation of R2 and MAE scores. The gradient boosted tree model also performs best compared to Rosenthal's analytical model. [ABSTRACT FROM AUTHOR]

The additive manufacturing industry requires effective and standardized methods for selecting design variants generated through computational tools. To address this need and overcome the current barriers in the industry, a decision support system based on quantitative metrics is necessary. This research aims to establish multiple criteria for evaluating design variations in additive manufacturing, considering both opportunistic and constraint-based approaches. The multi-criterion decision-making process integrates four distinct metrics that capture aspects such as geometric complexity, cost–benefit, and the additional cost associated with support structures. To facilitate the evaluation of design variants in metal additive manufacturing using laser powder bed fusion, a fuzzy power Maclaurin symmetric mean operator is employed for metric aggregation. The proposed approach is demonstrated by assessing topologically optimized design variants of an airplane bearing bracket and an engine bracket. The ranking and selection of design variants using this approach resulted in significant cost reductions, with a 50% reduction for the airplane bracket and a 75% reduction for the engine bracket, compared to the original designs manufactured using additive manufacturing techniques. [ABSTRACT FROM AUTHOR]

Additive manufacturing allows the creation of geometries otherwise impossible to achieve through traditional technologies in mechanical components. These geometries can be obtained using algorithms to optimize the mass distribution. Topology Optimization algorithms are one of the tools most applied in design for additive manufacturing and lightweight engineering. These optimization techniques require Finite Element Method tools to evaluate and compare the mechanical behavior of different geometrical solutions. The optimization results are closely related to boundary conditions, objectives, and constraints. Therefore, one of the issues is the necessity to evaluate different parameter settings to improve the result in terms of light weight, strength, and easy printability. This article shows a working method for using topological optimization to lighten a connecting rod. The resultant model is optimized considering Additive Manufacturing. [ABSTRACT FROM AUTHOR]

Material extrusion is one of widely applied additive manufacturing techniques to fabricate polymer parts with high geometrical complexity. Fundamental factors have been well developed for material extrusion process, which determines performance and quality of final parts, such as geometry accuracy, surface roughness, and microstructure. However, decision making for process selection heavily depends on experts' domain knowledges for supporting the decision making. Since it is challenging to collect and formalize experts' domain knowledges that are scattered, it requires knowledgebase to store and retrieve the experts' domain knowledge for reusing. This study proposes a design for additive manufacturing (DfAM) ontology to store metadata of the material extrusion process for selecting appropriate AM processes depending on manufacturing capabilities. The DfAM ontology is developed based on the metadata including material, process of fabrication, geometry information, and their relationships. The DfAM ontology significantly contributes to determining which process parameters affect the quality of final parts. To demonstrate the usefulness of the DfAM ontology, a case study was performed to select an AM machine based on the manufacturing capabilities. [ABSTRACT FROM AUTHOR]