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This paper proposes a novel two-stage and multi-objective optimization design method for the configuration design of complex engineering-to-order (ETO) product under imprecise matching and other uncertainties. The goal is to support selection of the optimal technical bid solutions while meeting requirements. A new two-stage configuration design framework for complex ETO products is proposed. Stage one is product architecture configuration design, supported by an engineering characteristics design method based on constraint satisfaction problems and Bayesian networks, and stage two is physical module configuration design, where a multi-objective optimal configuration model of physical modules is developed with the goals of minimum production cost, shortest delivery time, and maximum degree of matching technical requirements under imprecise matching of technical requirements and uncertainties in such as production cost and delivery time. As for the new selection method for obtaining an optimal technical bid solution scheme, it integrates a non-dominated sorting genetic algorithm (NSGA-II) and an approximate ideal solution ranking method (TOPSIS). Our approach has been applied to the design of a technical bid solution of subway's bogie. The results show that this approach enables bidders to quickly select the most interesting solution during a bidding process. The proposed approach aids the bidders to quickly create ETO product scheme designs, and then advise a new selection method for the bidders to quickly obtain a technical bid solution from the above product scheme designs, which has a minimum cost while meeting order requirements. [ABSTRACT FROM AUTHOR]

This paper presents a new method of color aesthetic evaluation based on the combination of form and color. According to the human visual physiological and psychological characteristics, this paper first proposes a new form-color field theory for the coupled form-color aesthetic evaluation based on the psychophysical field theory and the Moon and Spencer model. Second, it builds a coupled form-color topological graph for describing their interaction and develops a strength calculation algorithm for color harmony based on the new form-color field theory. Finally, it develops an aesthetic measure evaluation model for coupled form-color fields to evaluate the new theory. The experiment results show that the new coupled form-color aesthetic evaluation method is useful and can be integrated into an intelligent evaluation process for color design scheme. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) body scanning technology opens opportunities for virtual try-on and automatic made-to-measure apparel design. This paper proposes a new feature-based parametric method for modeling human body shape from scanned point clouds of a 3D body scanner [TC]^2. The human body model consists of two layers: the skeleton and the cross sections of each body part. First, a simple skeleton model from the body scanner [TC]^2 system has been improved by adding and adjusting the position of joints in order to better address some fit issues related to body shape changes such as spinal bending. Second, an automatic approach to extracting semantic features for cross sections has been developed based on the body hierarchy. For each cross section, it is described by a set of key points which can be fit with a closed cardinal spline. According to the point distribution in point clouds, an extraction method of key points on cross sections has been studied and developed. Third, this paper presents an interpolation approach to fitting the key points on a cross section to a cardinal spline, in which different tension parameters are tested and optimized to represent simple deformations of body shape. Finally, a connection approach of body parts is proposed by sharing a boundary curve. The proposed method has been tested with the developed virtual human model (VHM) system which is robust and easier to use. The model can also be imported in a CAD environment for other applications. [ABSTRACT FROM PUBLISHER]

This paper describes a new method for recognizing overtraced strokes to 2D geometric primitives, which are further interpreted as 2D line drawings. This method can support rapid grouping and fitting of overtraced polylines or conic curves based on the classified characteristics of each stroke during its preprocessing stage. The orientation and its endpoints of a classified stroke are used in the stroke grouping process. The grouped strokes are then fitted with 2D geometry. This method can deal with overtraced sketch strokes in both solid and dash linestyles, fit grouped polylines as a whole polyline and simply fit conic strokes without computing the direction of a stroke. It avoids losing joint information due to segmentation of a polyline into line-segments. The proposed method has been tested with our freehand sketch recognition system (FSR), which is robust and easier to use by removing some limitations embedded with most existing sketching systems which only accept non-overtraced stroke drawing. The test results showed that the proposed method can support freehand sketching based conceptual design with no limitations on drawing sequence, directions and overtraced cases while achieving a satisfactory interpretation rate. [ABSTRACT FROM AUTHOR]

Sketch-based modeling is a very important step towards a complete design process digitalization. For a complete digitalization, integration of sketch-based design modeling and commercial CAD systems is being a necessity. This is because of the need of detailed modification and refinement for the 3D models created by sketching. The integration can speed up the design process and affect competition between companies. In this paper, we present a new method to achieve the integration between sketch-based interfaces and commercial CAD systems by using gesture-based sketch modeling techniques for producing 3D conceptual design models and then translating the 3D models into CAD systems with feature recognition through IGES exchange files. Gesture-based modeling method was used because it works in a similar way to feature-based design approach and it is easy to extract features from its 3D models. IGES file format was used for exchanging 3D geometric model information because it is widely available within commercial CAD systems for data exchange and it also can express features and hierarchy information. 3D models were created with a gesture-based sketch interface for modeling and then information was extracted from it. After that, information was translated into IGES entities which were organized in a specific order in an IGES file. This information can be inputted into commercial CAD systems and recognized as a feature-based model with feature recognition embedded within CAD systems. To evaluate the proposed method, we conducted four case studies. In these case studies, we created 3D models and then transferred them into Autodesk Inventor as a commercial CAD system. After the CAD system recognized the 3D models successfully, we applied some modifications on the 3D models such as changing dimensions, positions and drafting to demonstrate the effective integration. [ABSTRACT FROM AUTHOR]

This paper presents a study on spiral bevel gear (SBGs) manufacturing method based on subdivision surface modeling scheme. The explicit nominal surface descriptions for both gears and pinions have been first developed based on gear generative motions. Then a group of key points on the tooth surface are selected as seed points (starting points) for a subdivision surface. From a given manufacturing precision criteria, a subdivision surface can be generated with a limited number of iterations during a subdivision surface modeling. This new surface modeling technique can be used for manufacturing (SBGs) with a general numerical control machine and a tool cutter in a flexible manufacturing environment. The experimental evaluation has been carried out and indicated that the subdivision surface-based SBG manufacturing method has advantages in high precise SBG manufacturing and precision control. [ABSTRACT FROM AUTHOR]

In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel surface modelling scheme to construct a freeform surface progressively from unorganised curves representing the boundary and interior characteristic curves. The approach can construct a base surface model from four ordinary or composite boundary curves and support incremental surface updating from interior characteristic curves, some of which may not be on the final surface. The base surface is first constructed as a regular Coons surface and upon receiving an interior curve sketch, it is updated. With this progressive modelling scheme, a final surface with multiple sub-surfaces can be obtained from a set of unorganised curves and transferred to commercial surface modelling software for detailed modification. The approach has been tested with examples based on 3D motion sketches; it is capable of dealing with unorganised design curves for surface modelling in conceptual design. Its limitations have been discussed. [Copyright &y& Elsevier]

The cyber-physical data of equipment whole life cycle are interrelated and play an important role in decision-making for business activities. However, multi-stage cyber-physical data are numerous, heterogeneous, and massive; non-real-time information data and real-time physical data are challenging to correspond to each other because of the inconsistency in space-time and granularity. As a result, equipment multi-stage heterogeneous cyber-physical data are difficult to effectively integrate and apply. Therefore, multi-stage cyber-physical data fusion of equipment is a key problem to be solved in manufacturing. To the best of our knowledge, a practicable framework and method to effectively fuse and apply equipment multi-stage data are still missing. To overcome this gap, this study proposes a multidimensional and multilevel cyber-physical fusion method for equipment. First, a four-dimensional cyber-physical fusion framework based on time, data, model, and structure is put forward. Then, the four kinds of fusion, including data fusion, model fusion, knowledge fusion, and digital twin (DT) application fusion, are discussed detailedly. Besides, the five types of algorithms set to support the fusion process are given. Finally, equipment life cycle activity is regarded as a DT application process using dynamic association of data and models. Through cyber-physical fusion, the data of equipment multi- stages could be applied for different activities to solve problems by means of DT. As a primary verification of the feasibility of the proposed approach, a case study of metro vehicle performance evaluation has been carried out, and the results have well confirmed that the multi-stage cyber-physical fusion method is feasible. [ABSTRACT FROM AUTHOR]

In response to rapidly changing market and customer needs, product design and development (PDD) is evolving into a human-centred and data-driven design paradigm. The design environment gets more open often involving crowdsourcing and the design process becomes more complex, considering product family design along product whole lifecycle development, and needing more data support. Therefore, it is critical to effectively capture, share, and manage design-related information in such a complex design environment. From this perspective, it is a prerequisite to have a proper product design lifecycle information model (PDLIM) to guide information gathering, sharing and management. To the best of our knowledge, currently, there lacks such a PDLIM to support effective PDD, though digital twin (DT) technology shows a great potential of supporting product lifecycle information collection and management. In this paper, the overall structure of the proposed PDLIM is firstly developed to frame in all main product lifecycle stages and the corresponding key phases for structurally capturing and storing necessary data along a product lifecycle. Secondly, key design information items against the main product lifecycle stages and their corresponding key phases are explored from literature reviews and case study analyses. Thirdly, the necessity of the identified information items in the PDLIM is qualitatively evaluated by two case studies. Finally, the PDLIM is further evaluated by applying formal object-role modelling (ORM) to demonstrate how design information items are used and interacted in exemplary design interaction scenarios, and to approve that it can be formally described and managed as an information model. The evaluation results show that the PDLIM is feasible to be adapted in a crowdsourcing-combined PDD process for supporting design management, reviewing, quality control, and next round product redesign and improvement. [ABSTRACT FROM AUTHOR]

With the increasing competition of the manufacturing industry, it is essential for manufacturers to develop a new (or next)-generation product based on their prior product design, product user experience, historical performances, etc. The digital twin (DT) is believed to be a suitable technology to support product lifecycle management with excellent data capture and analysis capabilities and product family design capabilities. However, it remains a challenge to synthesize and incorporate the data and information captured from previous generational product development and stored on their individual digital twin instances (DTIs) into next-generation product design. To address this problem, this study proposes a new nominal digital twin (NDT) concept as a collective digital representation of the current generational product digital mockups (DMUs) and all individual DTIs of the built products in services for new-generational product development. NDT is first defined here as a prototypical and synthesized digital twin (or digital representation) of multiple individual digital twins corresponding to multiple physical products in use or previously used. By analyzing the data and information on their DTIs, NDT can enable the establishment and evolution of a more precise approximated model of many related family products used previously or currently in use in various application scenarios and environments in the physical world. This paper also demonstrates how a NDT model can be first established in the product design phase from various digital mockup models and enhanced later with a stochastic forest meta-model based on Bayesian optimization connected to DTIs. With this NDT model, collaborative exploration for optimal design solutions during new-generation product design and improvement can be performed on NDT through multi-objective optimization, which in turn can make new-generation product design easier and quicker. As a primary verification of the feasibility of our proposed approach, a case study has been carried out, and the results have well confirmed that the NDT-based new-generation product design approach is feasible. [ABSTRACT FROM AUTHOR]

An introduction to the article is presented in which the editor discusses 11 papers from the 2012 International Symposium on Engineering and Design Education in Higher Education (EDE12) on topics including systematic approach to design, engineering graphics curriculum and ergonomics education.

Surface roughness and cutting force are strongly affected by machining parameters. In the past few decades, many researchers have established the relationship between the surface roughness and machining parameters, but less attention has been paid to tool shape and geometry, and the number of tool flutes which affects vibrations and machining system. Therefore, this study includes the tool flutes in addition to cutting speed, depth of cut and feed rate as independent variables. A set of machining experimental work was carried out on vertical milling machine (end milling) and AA6061 was used as the work piece material to provide original data. Response surface method is adopted to establish the relationship between machining parameters to the surface roughness and cutting force using the Taguchi technique. The findings based on analysis of variance and Minitab 16, concluded that surface roughness has only two significant parameters (tool flutes and depth of cut) which affected the surface machining, while cutting force was significantly affected by all machining parameters used in this study. Linear and non-linear models, for surface roughness and cutting force are incorporated. Verifications and optimizations of the results carried out indicated suitability of the technique used in this study. [ABSTRACT FROM PUBLISHER]

Background: Arbuscular mycorrhizal fungi (AMF) are a group of important symbiotic microorganisms found in ecosystems. Maize is the second most produced food crop globally. To investigate the mechanisms by which mycorrhizal symbiosis improves maize yields, the effects of mycorrhizal symbiosis on root vigor, nutrient accumulation in various tissues, and root exudates were investigated. We propose the following hypothesis: The secretion of organic acids in root exudates has antagonistic or synergistic effects, which are related to the rhizosphere environment. AMF symbiosis will enhance this effect. Result: Rhizophagus aggreatus, Claroideoglomus etunicatum, and Funneliformis mosseae were used to inoculate maize plants separately; meanwhile, maize was inoculated with the above three fungi together for another processing. The plant tissues were sampled at five growth stages: V12 (twelve-leaf), VT (Tassel), R1 (Silking), R2 (Blister), and R4 (Dough stage). The root vigor, and nutrient content in different maize organs and organic acids in root exudates were determined in these stages. The results show that mycorrhizal symbiosis significantly improved the root vigor of maize, especially for plants inoculated with F. mosseae. AMF symbiosis significantly increased N, P, and K accumulation. Mixed inoculation with arbuscular mycorrhizal fungi significantly promoted the accumulation of N and K in maize. P accumulation was significantly promoted by C. etunicatum inoculation. Mycorrhizal symbiosis reduced the levels of protocatechuic, vanillic, citric, and ferulic acid in maize root exudates and increased the levels of p-hydroxybenzoic and caffeic acid. Except for syringic, chlorogenic and succinic acid, the levels of other organic acids in root exudates were higher in plants inoculated with F. mosseae than in other treatments. Conclusion: This study demonstrates that mycorrhizal symbiosis improves root vigor and promotes nutrient accumulation at various sites; in addition, mycorrhizal symbiosis affects the content of organic acids in root exudates. [ABSTRACT FROM AUTHOR]

Crowd intelligence innovation (CII) is an open problem-solving model, typically performs on a crowdsourcing-based online innovation platform, which is highly dependent on the engagement and high-quality work of participants. On an online platform, many demanders publish tasks, and innovators submit schemes, which forms a complex CII system. To ensure such a CII system operates sustainably, it is critical for the platform operators to know the current system's health statuses before taking proper management actions. However, the large influx of participators with varied behaviors makes it extremely difficult to evaluate the health statuses of the system and capture its characteristics. Practicable method for quantitatively evaluating the health status of a CII system is still missing. Therefore, this study proposed a new quantitative health status evaluation method from an ecosystem perspective. First, the crowd intelligence innovation ecosystem (CIIE) was established by mapping the CII system onto the natural ecosystem based on ecological theory. Second, the health status of CIIE was described by its internal health status and external health status using a mixed method. The internal health status of the CIIE was defined and evaluated based on its health characteristics in terms of stability, ascendency, development capacity, and redundancy measurements using ecological network analysis. The external health status was evaluated using the ecological entropy index system. As the primary verification of the feasibility of our proposed approach, a case study of Zhubajie was conducted. These results confirmed the feasibility of our evaluation method. With this method, the health status of CIIEs could be accurately measured, which supported the management and organization the CII systems. Future work will focus on adapting suitable measures to improve the operation of CII. [ABSTRACT FROM AUTHOR]

Many complex mechanical products can be considered as multibody systems; 3D computer-aided design (CAD), multibody dynamics, finite element-based strength and fatigue analyses, optimization, and other CAE tools, are often used to develop this kind of product. But design is difficult and challenging because of information inconsistence among different engineering domains, and isolated information model islands exist. Lots of research are attracted to design and analysis information integration, but attentions are mainly focused on integration of CAD-finite element analysis (FEA), supporting the bottom-up design principle, while multibody dynamics analysis for analyzing global performance of a mechanical system is rarely considered, which follows the top-down design principle. In this paper, a novel multilevel system representation modeling framework for supporting both bottom-up and top-down or mixed design methods is proposed. It can integrate the product design CAD models with multibody dynamics, finite element-based strength and fatigue analyses, realizing a CAD-dynamic-FEA-fatigue analysis integration. It can also support data exchange and transfer in multidomain analyses. The framework is illustrated with a case study of a load haul dump (LHD) design using currently available software tools. Furthermore, a design case study of the working unit of the LHD is given to highlight the applicability of the modeling framework for multibody mechanical systems. It has been demonstrated that the framework can describe information exchanging and integration among CAD, FEA-based strength and fatigue analyses, and multibody dynamics analysis during a multibody mechanical product design process. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an integrated geometric error modeling, identification and compensation method for machine tools. Regarding a machine tool as a rigid multi-body system (MBS), a geometric error model has been established. It supports the identification of the 21 translational geometric error parameters associated with linear-motion axes based on a laser interferometer, and 6 angular geometric error parameters for each rotation axis based on a ball-bar. Based on this model, a new identification method is proposed to recognize these geometric errors. Finally, the identified geometric errors are compensated by correcting corresponding NC codes. In order to validate our method, a prototype software system has been developed, which can be used for conducting tests on any type of CNC machine tool with not more than five axes. An experiment has been conducted on a five-axis machine center with rotary table and tilting head; the results show that the integrated geometric error modeling, identification and compensation method is effective and applicable in multi-axis machine tools. [Copyright &y& Elsevier]

Abstract: One-dimension range profile has recently been widely used as recognition feature in radar target recognition since it can be extracted easily. However, the one-dimension range profile of a target has strong dependence on the aspect-angle of the target because it is based on the model of scattering centers. On the other hand, the different classes of target samples overlap each other in the feature space, so satisfactory recognition results cannot be obtained directly for the targets under whole posture. In order to improve the recognition performance of systems based on one-dimension range profile, a method of radar target recognition based on the multi-resolution analysis theory and neural network is presented. Firstly, one-dimension range profile features under various resolutions are obtained by using multi-resolution analysis. Then, the tree-structured cascade SOM network – a hybrid neural network on the basis of the multi-resolution analysis theory and self-organizing map (SOM) – has been used as the classifier to make multi-grade judgment and recognition gradually for the one-dimension range profile features. The experimental results show that the average correct recognition rate for three kinds of targets – a propeller aircraft, a little jet aircraft and a large jet aircraft – with 0–180° posture variation in aspect-angle is up to 92.97%. [Copyright &y& Elsevier]

• A hyper-heuristic based ensemble GP framework is proposed to solve the SRCPSP. • A sequence voting mechanism is designed to achieve PRs collaborative scheduling. • The basic components of existing hyper-heuristic scheduling are modified. • The local search, mutation and renewal are designed for improving GP evolution. In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches. [ABSTRACT FROM AUTHOR]

With intense global competition, many enterprises adopt platform strategies to develop diverse products with a lower corresponding increase in cost and a shorter lead time. In the past, enormous efforts have been made in investigating product platforms. Recent researches and practices show that developing Uncertainty-Oriented Product Platform (UOPP) is a promising approach to improving the adaptability of enterprises to meet uncertain markets, customer requirements, technologies, policies, and regulations, etc., which includes flexible product platform, adaptable product platform, market-driven product platform, and sustainable product platform. This paper provides a comprehensive review in this field, including the connotation of uncertainty, the concepts of emerging UOPPs, the development process of UOPP, as well as the development technologies in each step. It also highlights the future research framework, opportunities and challenges. This literature review lays the foundation for future research of product platform design under uncertainty. [ABSTRACT FROM AUTHOR]

Multiple overtracing strokes are common drawing behaviors in freehand sketching; that is, additional strokes are often drawn repeatedly over the existing ones to add more details. This paper proposes a method based on stroke-tolerance zones to group multiple overtraced strokes which are drawn to express a 2D primitive, aiming to convert online freehand sketches into 2D line drawings, which is a base for further 3D reconstruction. Firstly, after the user inputs a new stroke, a tolerance zone around the stroke is constructed by reference to its polygonal approximation points obtained from the stroke preprocessing. Then, the input strokes are divided into stroke groups, each representing a primitive through the stroke grouping process based on the overtraced ratio of two strokes. At last, each stroke group is fitted into one or more 2D geometric primitives including line segments, polylines, ellipses, and arcs. The proposed method groups two strokes together based on their screen-space proximity directly instead of classifying and fitting them firstly, so that it can group strokes of arbitrary shapes. A sketch-recognition prototype system has been implemented to test the effectiveness of the proposed method. The results showed that the proposed method could support online multiple overtracing freehand sketching with no limitation on drawing sequence, but it only deals with strokes with relatively high overtraced ratio. [ABSTRACT FROM AUTHOR]

Non-contact R test is an instrument to measure the synchronous errors of five-axis machine tools. However, there are still some deficiencies in its researches, such as the difficult and laborious calibrations. How to systematically improve the measurement accuracy with a good balance to minimum cost is a real problem in guiding practice. This paper proposes a new systematic optimization method to solve this problem based on a comprehensive understanding of the non-contact R test in terms of structure parameters and relations. Firstly, the algorithm for sphere center coordinates is established based on the self-adaptive differential evolution algorithm to obtain the definite computational accuracy and efficiency. Secondly, the parameters of the fixture structure are optimized to maximize the measurement stability, measuring space, and non-interference space. Thirdly, the on-machine calibration is performed to replace pre-calibration and re-calibration and to establish the positional relationships between sensors, the fixture, and the machine tool simultaneously. It can reduce the difficulties of manufacture, maintenance, and application. Fourthly, the measurement accuracy can be evaluated to determine whether the iterative optimization achieves the goal. The proposed method has been verified with case studies to support the setting-up of the optimized non-contact R test, leading to a cost-effective and accurate test on five-axis machine tools. [ABSTRACT FROM AUTHOR]

Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances) parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products. [ABSTRACT FROM AUTHOR]

Conceptual design has a decisive impact on the product development time, cost and success. This paper presents a new conceptual design method for achieving rapid and effective mapping from product design specification (PDS) to concept design. This method can guide the creation of reasonable mapping among the PDS, behaviour parameters and structure parameters and to evaluate the rationality of performance parameters and structure parameters to confirm a reasonable conceptual design scheme. In this method, we establish a PDS-behaviour-structure conceptual design model to support the conceptual design of multi-disciplinary-oriented complex product system (CoPS) and develop a vector-based mapping tool in this method to support the rapid mapping, and demonstrate its feasibility and effectiveness by a case study. This method is not only supportive to realise the automation of a conceptual design process but also helpful to evaluate the conceptual design in the field of engineering design. [ABSTRACT FROM AUTHOR]

• A HH-FGP framework is proposed to solve the SRCMPSP-NPI. • The evolutionary process is divided to achieve PR sampling and generation. • A new evaluation mechanism is proposed for depth range and attributes filtering. • The search operators are improved to avoid producing PRs with illegal depth. Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness. [ABSTRACT FROM AUTHOR]

k Nearest neighbors ( kNN ) searching algorithm is widely used for finding k nearest neighbors for each point in a point cloud model for noise removal and surface curvature computation. When the number of points and their density in a point cloud model increase significantly, the efficiency of a kNN searching algorithm becomes critical to various applications, thus, a better kNN approach is needed. In order to improve the efficiency of a kNN searching algorithm, in this paper, a new strategy and the corresponding algorithm are developed for reducing the amount of target points in a given data set by extracting nearest neighbors before the search begins. The nearest neighbors of a reverse nearest neighborhood are proposed to use in extracting nearest points of a query point, avoiding repetitive Euclidean distance calculation in an extracting process for saving time and memories. For any point in the model, its initial nearest neighbors can be extracted from its reverse neighborhood using an inner product of two related vectors other than direct Euclidean distance calculations and comparisons. The initial neighbors can be its full or partial set of the all nearest neighbors. If it is a partial set, the rest can be obtained by using other fast searching algorithms, which can be integrated with the proposed approach. Experimental results show that integrating extracting algorithm proposed in this paper with other excellent algorithms provides a better performance by comparing to their performances alone. [ABSTRACT FROM AUTHOR]

A product-service system (PSS) has many engineering characteristics (ECs), their design is a critical work in PSS planning, which has an important influence on the cost and quality of PSS. How to design a reasonable PSS-ECs scheme, and evaluate its life cycle cost (LCC) is a challenging task. Aiming at the PSS-ECs design for LCC, this paper proposes a new PSS design method, it first treats and models the design of PSS-ECs as a customer requirements-based constraint satisfaction problem (CSP) for finding an initial set of satisfied PSS-ECs schemes, and then it evaluates these schemes based on Bayesian network (BN)-based LCC estimation model for finding an optimal scheme as a solution. Constructing a BN describing the uncertain relationships between PSS-ECs and LCC is the core of this research. By combining existing R&D data and expert experience, Bayesian estimation and arithmetic averaging are used to estimate the conditional probability in BN. Take a subway bogie and its maintenance service in a Chinese company as an example to verify the proposed method. The results show that the proposed method can effectively solve the problem of PSS-ECs design for LCC, it also shows that this method has positive significance in realizing engineering knowledge consolidation, assisting designers in exploring design space, and improving the rationality of design decisions. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive control method of surface waviness for 5-axis flank milling, reducing cutting force variation, and improving unstable rotation state of rotary axis to suppress low-frequency milling chatter. We propose a cutting force-sweep area model and define an average cutting force term, so that the control of cutting force turns into sweep area rate (SAR) control, realized by feed rate. The feed rate might be dynamic, adjusted to make the corresponding SAR constant. In addition, the method can improve the unstable rotation state, such as discontinuous or reciprocating rotation in micro angle, by means of decomposing the unstable rotation to more micro continuous rotation or tool axis orientation adjustments based on the geometric relationship of tool axis and machining tolerance. Finally, a series of comparison experiments were conducted on a 5-axis machine tool. From the comparison of cutting force and surface scan, the results showed that the control method could suppress low-frequency milling chatter and reduce surface waviness effectively. [ABSTRACT FROM AUTHOR]