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Abstract This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

Abstract With the increasingly severe energy supply and environmental pressures, high-end equipment is gradually adopted to reduce the carbon emissions of manufacturing industry which makes its low-carbon structural design a critical research hotspot. The best structural scheme can be got by multi-attribute decision-making (MADM) with design requirements. However, the decision-making attributes in the structural design of high-end equipment are too many at first and low-carbon attributes are seldom fully considered. Moreover, there are a large amount of related data with linguistic vagueness, interval uncertainty, and information incompleteness, which fail to be handled simultaneously. There, this paper proposes an integrated MADM method of low-carbon structural design for high-end equipment based on attribute reduction considering incomplete interval uncertainties. First, distribution reduction of low-carbon structural design is carried out to obtain the minimum attribute set and encompass low-carbon attributes comprehensively. Second, a collaborative filtering algorithm is utilized to complete the missing data in the subsequent design process. Third, interval rough numbers (IRNs) are integrated into DEMATEL-ANP (DANP) and multi-attribute border approximation area comparison (MABAC) to quickly rank the alternative schemes for high-end equipment and determine which is the best. The rationality and robustness of the proposed method are verified through the case study and comparative analysis of a hydraulic forming machine.

Abstract Blank holder force (BHF) is a crucial parameter in deep drawing, having close relation with the forming quality of sheet metal. However, there are different BHFs maintaining the best forming effect in different stages of deep drawing. The variable blank holder force (VBHF) varying with the drawing stage can overcome this problem at an extent. The optimization of VBHF is to determine the optimal BHF in every deep drawing stage. In this paper, a new heuristic optimization algorithm named Jaya is introduced to solve the optimization efficiently. An improved “Quasi-oppositional” strategy is added to Jaya algorithm for improving population diversity. Meanwhile, an innovated stop criterion is added for better convergence. Firstly, the quality evaluation criteria for wrinkling and tearing are built. Secondly, the Kriging models are developed to approximate and quantify the relation between VBHF and forming defects under random sampling. Finally, the optimization models are established and solved by the improved QO-Jaya algorithm. A VBHF optimization example of component with complicated shape and thin wall is studied to prove the effectiveness of the improved Jaya algorithm. The optimization results are compared with that obtained by other algorithms based on the TOPSIS method.

Abstract With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efficient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difficult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identification is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identification models. The experimental results proved the superiority of the HDT framework for human locomotion mode identification.

Sulfide organics and phenols are ubiquitous in freshwater lakes all over the world. As two taste and odor (T and O) compounds, they are harmful to the environment and human body. The existing detection methods for T and O compounds mainly include sensory analysis and gas-phase mass spectrometry, which are cumbersome and time-consuming. Herein, a method for the simultaneous and rapid detection of two T and O compounds (methyl sulfide and 2,4-di-tert-butylphenol) based on surface-enhanced Raman spectroscopy (SERS) is firstly developed. The SERS substrate was prepared by coating Ag nanoparticles (Ag NPs), layered double hydroxide (LDH), and graphene oxide (GO) on the surface of an Ag-coated Au nanoparticle (Au@Ag NP) substrate. Under optimal conditions, this SERS substrate possessed low detection limits of 1.53 ppm for methyl sulfide and 0.39 ppm for 2,4-di-tert-butylphenol. In addition, it took only 20 min to complete the detection using this method, without complex sample pretreatment. Furthermore, it was successfully applied to simultaneously detect methyl sulfide and 2,4-di-tert-butylphenol in actual water samples and had good application prospects for the rapid detection of T and O compounds in water.

The topology optimization design of complex products can significantly improve material and power savings, and reduce inertial forces and mechanical vibrations effectively. In this study, a large-tonnage hydraulic press was chosen as a typically complex product to present the optimization method. We propose a new reliability topology optimization method based on the reliability-and-optimization decoupled model and teaching–learning-based optimization (TLBO) algorithm. The supports formed by the plate structure are considered as topology optimization objects, characterized by light weight and stability. The reliability optimization under certain uncertainties and structural topology optimization are processed collaboratively. First, the uncertain parameters in the optimization problem are modified into deterministic parameters using the finite difference method. Then, the complex nesting of the uncertainty reliability analysis and topology optimization are decoupled. Finally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parameters and a fast solution. The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial stage and performing finer searches in the later stages. A numerical example of the hydraulic press base plate structure is presented to underline the effectiveness of the proposed method.

Abstract Network function virtualization (NFV) is an appealing solution that transforms complex network functions from dedicated hardware to software instances running in a virtualized environment. However, some new challenges will arise when deploying virtual network functions to meet the needs of NFV and edge‐computing (EC) enabled 5G networks. In this paper, we focus on the service function chain (SFC) orchestration problem for EC‐enabled networks to maximize the profit of network service providers. First, the mathematical model of SFC orchestration in NFV and EC‐enabled networks is defined. Then, a two‐stage heuristic algorithm is proposed to optimize the total revenue. Finally, the performance of the method is evaluated by simulation experiments and the results show its effectiveness.

Abstract This study presents a robustness optimization method for rapid prototyping (RP) of functional artifacts based on visualized computing digital twins (VCDT). A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built, where thermal, structural, and multidisciplinary knowledge could be integrated for visualization. To implement visualized computing, the membership function of fuzzy decision-making was optimized using a genetic algorithm. Transient thermodynamic, structural statics, and flow field analyses were conducted, especially for glass fiber composite materials, which have the characteristics of high strength, corrosion resistance, temperature resistance, dimensional stability, and electrical insulation. An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP. Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution. A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT. Moreover, manufacturability was verified based on a thermal-solid coupled finite element analysis. The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.

Abstract Self-spiraling actuators are widely found in nature and have high research and actuator-application value in self-lock and self-assembly. Four-dimensional (4D) printing is a new generation additive manufacturing of smart materials and has shown great potential for the fabrication of multi-functional and customized structures. The microarchitecture design of a bilayer actuator could bring flexible and diversified self-spiraling behaviors and more possibilities for practical application by combing 4D printing. This work investigates the stimuli effects of fiber patterns and fabrication parameters on self-spiraling behaviors of the bilayer actuator via both experimental and theoretical methods. This work may potentially provide pattern design guidance for 4D-printed self-spiraling actuators to meet different application requirements.

Soft grippers based on stimuli‐responsive materials show great promise to perform safe interaction and adaptive functions in unstructured environments. Hence, improving flexibility and designability of the stimuli‐responsive soft grippers for better grasping ability are highly desired. Inspired by the biological structure of octopuses, a class of temperature‐driven polylactic acid grippers is fabricated by 4D printing in this work, which consists of two types of bilayer structures, separately imitating the web and tentacles of an octopus. Compared with the traditional pure‐bending soft grippers, the integrated structure results in a 1.5 times wider reachable area and enhanced flexibility. The complex grasping behaviors are predicted with the 4D printing parameters (i.e., printing paths and printing speeds) using the reduced bilayer plate theory. Moreover, a method for determining the printing parameters of the gripper is provided to realize the desired grasping behavior depending on different objects. The feasibility of the design method is experimentally verified by gasping three distinctive objects, including an egg, a weight, and a Metatron's cube, which is in good agreement with the simulation results. Herein, a promising strategy is provided to achieve the easy‐fabricated, low‐cost, and versatile soft graspers using smart materials.

The key to detecting neutral hydrogen during the epoch of reionization (EoR) is to separate the cosmological signal from the dominating foreground radiation. We developed direct optimal mapping (DOM) to map interferometric visibilities; it contains only linear operations, with full knowledge of point spread functions from visibilities to images. Here, we demonstrate a fast Fourier transform-based image power spectrum and its window functions computed from the DOM images. We use noiseless simulation, based on the Hydrogen Epoch of Reionization Array Phase I configuration, to study the image power spectrum properties. The window functions show

Abstract Design is a high-level and complex thinking activity of human beings, using existing knowledge and technology to solve problems and create new things. With the rise and development of intelligent manufacturing, design has increasingly reflected its importance in the product life cycle. Firstly, the concept and connotation of complex product design is expounded systematically, and the different types of design are discussed. The four schools of design theory are introduced, including universal design, axiomatic design, TRIZ and general design. Then the research status of complex product design is analyzed, such as innovative design, digital design, modular design, reliability optimization design, etc. Finally, three key scientific issues worthy of research in the future are indicated, and five research trends of “newer, better, smarter, faster, and greener” are summarized, aiming to provide references for the equipment design and manufacturing industry.

Abstract Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.

Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

Reversible deformations of the 4D-printed structures are attractive and promising for various application fields. In this study, the principle of reversible deformations for the bilayer structure consisting of SMP and elastic material is illustrated. By exploring the influence of printing parameters on deformation and resistance, a low-cost reversible bilayer structure with rational resistance distribution is designed to realize reversible deformation. Subsequently, the bilayer structure is employed to design a soft crawling robot with asymmetrical variable friction coefficient feet. By revealing the principle of locomotion by force analysis and deformation process analysis, a wave-like strategy is proposed to actuate the robot. Experiments verify the effectiveness of the designed structures.

This paper presents an energy and exergy co-optimization method of integrated gasification combined cycle (IGCC) based on Fuzzy Supervisory Predictive Control (FSPC). Firstly, a green IGCC process is proposed which contains three principle couplings: air separation unit (ASU), heat recovery steam generator (HRSG) and CO2 capture/storage unit (CCS). From law of thermodynamics, using substance thermophysical parameters, the energy efficiency and exergy efficiency of IGCC are successively defined. The IGCC power station has features such as closed coupling, large time lag and non-linearity, however, faster response speed and lower overshoot are always the unremitting pursuits. Therefore, the Fuzzy Supervisory Predictive Control (FSPC) method is proposed to implement robust control under complex disturbances by pre-considering unmeasurable disturbance and measurable disturbance. The fuzzy rules extracted from historical bigdata are employed in supervisory layer to make the precise control decisions. Finally, the energy and exergy co-optimization model is built and solved for higher efficiency and economic effectiveness. Taking the large-scale (300MW) IGCC for example, after using FSPC, the efficiency of water recovery is increased from 40.7% to 62.1% with the ratio of 52.6% because of waste water recovery (WWR) system. The net efficiency of proposed IGCC system is increased from 37.6% to 41.7% with the ratio of 10.9%. The exergy efficiency of IGCC system is increased from 36.5% to 39.2% with the ratio of 7.4%. The proposed method has great significance for the energy-saving and Near-zero emissions (NZEC) IGCC with high safety and robust control under supercritical (SC) or ultra-super critical (USC) state.

With the rapid expansion of Industrial Internet of Things, cloud computing and artificial intelligence, many intelligent information services have been developed in smart factories. One of the most important applications is helping factory managers predict the quality of assembled products. Traditional prediction methods of assembly quality mainly focus on building classification or regression models with high accuracy. However, less attention is paid to high-dimensional and imbalanced data, which is a special but common scenario at real-life assembly quality prediction. In this paper, we first use random forest to reduce dimension and analyze critical-to-quality characteristics. Then, a SMOTE-Adaboost method with jointly optimized hyperparameters is proposed for imbalanced data classification in assembly quality prediction. In addition, edge computing is introduced to improve the efficiency and flexibility of quality prediction. Finally, the practicality and effectiveness of the proposed method are verified by a case study of wheel bearing assembly line, and the experimental results show that the proposed method is superior to other classification methods in assembly quality prediction.

Most of the existing methods for 3D hand pose estimation are performed from a single depth map. In that case, the depth missing challenges from input frames caused by hand self-occlusions and imaging quality lead to multi-valued mapping phenomenon and sub-optimal model. In this paper, we proposed a novel recurrent architecture named Attention-based Pose Sequence Machine (APSM) to alleviate challenges by introducing temporal consistency. As for recurrent unit (RU), we extend traditional Gated Recurrent Unit (GRU) with 3D convolutional neural networks (CNNs) to handle voxelized inputs and features, and a novel RU named Deep Gated Recurrent Unit (DGRU) was proposed by rebuilding deeper gates based on GRU. To improve the model performance, a novel spatial attention mechanism denoted as Attention Model (AM) was proposed. Ablation experiments are designed to validate each contribution of our work, and experiments on two publicly available dataset show that our work outperforms state-of-the-art on hand pose estimation.

As an important link to guarantee normal industrial production, equipment maintenance plays an increasingly key role in enhancing the competitiveness of enterprises and supporting green smart manufacturing. This paper aims to promote the implementation of predictive maintenance for complex equipment and improve the green performance of the maintenance service process. A structural framework of information sharing and service network is introduced to build a ubiquitous state data awareness environment for predictive maintenance service. Subsequently, an integrated mathematical problem model that consists of carbon emission objective and extended maintenance cost objective is constructed. Then an improved NSGA-II algorithm is utilized to solve this complicated two-objective optimization problem. In response to deal with the uncertainties of maintenance service environment and inaccuracy of prediction, a data-driven dynamic adjustment strategy is applied. A grinding roll fault case of a large vertical is used to demonstrate the effectiveness of this proposed approach.

We report the most sensitive upper limits to date on the 21 cm epoch of reionization power spectrum using 94 nights of observing with Phase I of the Hydrogen Epoch of Reionization Array (HERA). Using similar analysis techniques as in previously reported limits, we find at 95% confidence that Δ ^2 ( k = 0.34 h Mpc ^−1 ) ≤ 457 mK ^2 at z = 7.9 and that Δ ^2 ( k = 0.36 h Mpc ^−1 ) ≤ 3496 mK ^2 at z = 10.4, an improvement by a factor of 2.1 and 2.6, respectively. These limits are mostly consistent with thermal noise over a wide range of k after our data quality cuts, despite performing a relatively conservative analysis designed to minimize signal loss. Our results are validated with both statistical tests on the data and end-to-end pipeline simulations. We also report updated constraints on the astrophysics of reionization and the cosmic dawn. Using multiple independent modeling and inference techniques previously employed by HERA Collaboration, we find that the intergalactic medium must have been heated above the adiabatic cooling limit at least as early as z = 10.4, ruling out a broad set of so-called “cold reionization” scenarios. If this heating is due to high-mass X-ray binaries during the cosmic dawn, as is generally believed, our result’s 99% credible interval excludes the local relationship between soft X-ray luminosity and star formation and thus requires heating driven by evolved low-metallicity stars.

Product assembly simulation is considered as one of the key technologies in the process of complex product design and manufacturing. Virtual assembly realizes the assembly process design, verification, and optimization of complex products in the virtual environment, which plays an active and effective role in improving the assembly quality and efficiency of complex products. In recent years, augmented reality (AR) and digital twin (DT) technology have brought new opportunities and challenges to the digital assembly of complex products owing to their characteristics of virtual reality fusion and interactive control. This paper expounds the concept and connotation of AR, enumerates a typical AR assembly system structure, analyzes the key technologies and applications of AR in digital assembly, and notes that DT technology is the future development trend of intelligent assembly research. Keywords: Augmented reality, Digital twin, Digital assembly, Virtual simulation, Assembly analysis

Structural optimization of complex mechatronic components may involve heterogeneous competing performance indices, including the cost, fixation, benefit, and deviation ones. However, such optimization problems with heterogeneous objectives have not been investigated so far. In this paper, a novel interval heterogeneous-objective robust optimization approach is proposed for complex mechatronic components. First, a unified interval heterogeneous-objective robust optimization model is constructed for mechatronic components with the uncertainties described as interval variables. Subsequently, a new interval robust equilibrium optimization algorithm is proposed to solve the interval heterogeneous-objective robust optimization model. Specifically, the unified formulas for assessing the robustness of interval heterogeneous-objective performance indices and the robust equilibrium among them are derived at first. Then, the preferential guidelines considering the robust equilibrium among all the objective and constraint performance indices are proposed for the direct ranking of various design vectors, and finally, the heterogeneous-objective robust equilibrium optimization of complex mechatronic components is realized by integrating the Kriging technique and the nested genetic algorithm. The feasibility and effectiveness of the proposed heterogeneous-objective robust optimization approach are verified by a numerical example and a case study.

The problem of characterizing the sensitivity indices of structural performance considering random and interval uncertainty variables simultaneously is analyzed. Data-driven polynomial chaos expansion (PCE) is established to treat random variables with arbitrary input uncertainty information, and subsequently extended to consider interval uncertainty variables simultaneously. An adaptive-sparse polynomial chaos expansion calculation algorithm is proposed to determine the appropriate polynomial coefficients based on the available uncertainty information of random and interval uncertainty variables. Subsequently, the local and total sensitivity indices of random and interval uncertainty variables are calculated directly using the constructed PCE model. Two numerical and engineering examples are presented to demonstrate the effectiveness of the proposed approach. Compared with the results of the Monte Carlo simulation method, the proposed approach yields excellent results with significantly reduced computational effort in the three examples.

In this paper, an efficient hybrid reliability analysis (HRA) method and a hybrid reliability-based design optimization (HRBDO) approach are proposed for realistic complex engineering structures with random and interval uncertainties. First, the HRBDO model for complex engineering structures is constructed with its objective and performance functions described as the implicit functions of design variables and random and interval parameters. Then, an efficient HRA method based on adaptive step size (ASS-HRA) is put forward to calculate the minimum reliability of the structure's performance function under the influences of both random and interval uncertainties, the computational efficiency and accuracy of which are verified by a benchmark test. Subsequently, an efficient HRBDO approach integrating the proposed ASS-HRA method with the polynomial response surface model (PRSM) is developed for solving the HRBDO problems of complex engineering structures, the effectiveness of which is demonstrated by a numerical example. Finally, the HRBDO of a high-speed press slider demonstrates the efficiency, effectiveness, and versatility of the proposed HRBDO approach based on the ASS-HRA in the design of realistic complex engineering structures.

Based on the in-depth study of the technical connotation of the integration of manufacturing industry and Internet,the technical bottleneck of the integration was put forward from four perspectives of the concept,method,goal and achievement of the deep integration.In view of different fields,ten key technologies of deep integration of manufacturing industry and Internet were analyzed in detail.And the different scenarios of key technology application were given.

Sandwich structures are important innovative multifunctional structures with the advantages of low density and high performance. Creative design for sandwich structures is a design process based on sandwich core structure evolution mechanisms, material design method, and panel (including core structure and facing sheets) performance prediction model. The review outlines recent research efforts on creative design for sandwich structures with different core constructions such as corrugated core, honeycomb core, foam core, truss core, and folded cores. The topics discussed in this review article include aspects of sandwich core structure design, material design, and mechanical properties, and panel performance and damage. In addition, examples of engineering applications of sandwich structures are discussed. Further research directions and potential applications are summarized.

With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.

Three-point bending fatigue test of 5083 and 5A06 aluminum alloy T-welded joints is carried out, and the fatigue life of the specimens with different influencing factors are obtained. Finite element model of the T-welded joint is established and the nodal force based structural stress is calculated. Neighborhood rough set theory is used for analysis of the factors which influence the fatigue life of the aluminum alloy welded joints. Key influencing factors are studied and the fatigue characteristic domains are determined. The master S-N curve characterized by the nodal force based structural stress range and cycles to failure on semi log coordinate as well as S-N curves corresponding to the fatigue characteristic domain is fitted. A case study of fatigue life prediction of 5A06 aluminum alloy welded joint indicates the effectiveness of the fatigue life prediction method based on the fatigue characteristic domain.

Currently, human motion analysis using three‐dimensional (3D) data creates closer awareness in computer vision with the introduction of cost‐effective Kinect or other depth cameras. This study attempts to segment a continuous 3D skeletal sequence into several disjointed sub‐sequences, each of which is corresponding to a complete action. To address this issue, the authors propose a supervised time‐series segmentation algorithm. A bidirectional propagation search scheme is employed to reach a solution. Specifically, a human skeleton is formulated as a point in multidimensional space, and a motion trajectory is further represented as a sequence. Each training action sequence serves as an atom in a dictionary, which is adopted by an l2‐regularised collaborative representation classifier. Based on the fact that the reconstruction error of the collaborative representation measures the similarity between a test sub‐sequence and training sequences, they utilise its variation over time to capture action transition. Cut point detection and sub‐sequence recognition are simultaneously achieved. Experiments on the authors’ recorded 3D skeletal sequences demonstrate that the proposed algorithm outperforms existing human motion segmentation techniques. Their algorithm is capable of extending to segment various dimensional sequences. This extensibility is validated by synthetic signal segmentation experiments.

Nowadays, a large amount of energy is consumed by buildings in the city. Among them, industrial energy consumption accounts for a large proportion due to the high power of manufacturing devices in the job-shop. To achieve higher energy-efficiency, lots of scheduling methods in the job-shop are developed. However, most of the current scheduling method is based on the prior-experimental data and the schedule of the production cycle cannot be adjusted according to energy status of manufacturing devices. Thus a dynamic scheduling system based on the cyber-physical energy monitoring system is proposed to improve the energy efficiency of the job-shop. First, a novel process of “scheduling-monitoring-updatingoptimizing”is implemented in the proposed system. In the scheduling model, tool aging condition is considered along with the geometry information of the work-piece to estimate energy consumption of the manufacturing process. At last, a modified genetic algorithm with multi-layer coding is applied to generate an energy-efficient schedule. The proposed system is implemented in a production cycle which contains 36 operations of six work-pieces and ten machine tools to prove its validity.

Typical still action recognition methods rely on human body part detection and object detection. However, current human body part detectors and object detectors are far from perfect, leading to a negative impact on subsequent spatial relation learning of human-object interactions (HOIs). Bag-of-features (BoF)based methods go beyond such modeling paradigms, but they do not achieve the state-of-the-art accuracies. In this paper, we propose two still action recognition methods that model HOI layouts by image hierarchical representation, rather than explicitly constructing HOI relations. The first method encodes a dense set of SIFT features using Fisher vectors, where an image is divided into increasingly fine regions with the spatial pyramid. The second method takes recent pretrained deep networks as feature execrators, where an image is divided into overlapped regions. The improvement effect of the hierarchical representation is proven by extensive comparison experiments. Our methods are very simple and easy-to-use, which remarkably outperform those BoF-based methods and complicated human-centric methods. To the best of our knowledge, our methods achieve the highest accuracies to date on the Sports, PPMI, and extended PPMI data sets.

The development of technologies such as big data and cyber-physical systems (CPSs) has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi-disciplinary coupling, virtual assembly, virtual reality (VR), multi-objective optimization (MOO), and human-computer interaction. The key technologies of intelligent design for customized products include: a description and analysis of customer requirements (CRs), product family design (PFD) for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control (CNC) machine tools.

The scatter degree of the fatigue samples is reduced when the nodal force based structural method is used for steel welded joints, while it is still high for aluminum alloy welded joints. Statistical method and rough set theory is used to fatigue analysis so that fatigue characteristic domains are determined and S-N curves are fitted. Experiment results show that fatigue life of the aluminum alloy welded joints is under the influence of some key factors and the fatigue data with the same characteristics distribute in a relatively independent area. Accordingly, a novel S-N curve modeling method of aluminum alloy welded joints based on the fatigue characteristics domain is proposed. In the proposed method, the nodal force based structural stress method is used for stress calculation and neighborhood rough set theory is used for character extraction to obtain the key factors. Then fatigue characteristics domains are divided and S-N curves are fitted on each fatigue characteristics domain instead of on the whole domain so that a set of S-N curves are obtained. Statistical results show that selection of the S-N curve for the aluminum alloy welded joints according to different fatigue characteristic domain is more accurate.

Conceptual design plays an important role in a product development process. Significant development of technologies in the cyber-physical system (CPS) provides innovative approaches for product conceptual design. This paper proposes an architecture of CPS for conceptual design that realizes the acquisition of real-time physiological data from the physical world and the feedback of psychological states from the cyber world. As understanding and meeting the needs of customers have been recognized as significant aspects for conceptual design, an intelligent psycho-physiological approach that incorporates electroencephalogram (EEG) into the Kano model is adopted in this CPS for real-time customer needs analysis. The sample entropy (SampEn) extracted from EEG data is an endogenous neural indicator for customers' psychological states. A support vector machine using this SampEn as input is trained for classifying different categories of quality attributes defined in the Kano model. A case study is conducted to testify the feasibility of the approach proposed in this paper.

Energy Internet represents a critical breakthrough that allows traditional energy to be transformed into intelligent energy. In this regard, the reliability apportionment technologies for modern products within Energy Internet deserve more attention because they can ensure the reliability optimization of different functional modules of systems. And because of the growingly serious energy crisis, environmental protection has become much more urgent, which requires industrial products to be more environmental friendly. However, traditional reliability apportionment methods can not do this. First, in design phase of product, most of them tend to ignore the environmental attributes, including carbon emissions and resource efficiency. Second, they fail to process the uncertainties of reliability apportionment, which is inevitable and important efficiently. Third, they pay insufficient attention to the correlations among product subsystems, which greatly influence the different product functions. To overcome these drawbacks, a reliability apportionment model based on the interval analysis and decision-making trial and evaluation laboratory is proposed. The validity of the proposed method is demonstrated by a case study.

Cloud manufacturing (CMfg) is a new service-oriented production paradigm from the wide application of cloud computing for the manufacturing industry. The aim of this manufacturing mode is to provide resource-sharing and on-demand manufacturing mode to improve operation efficiency. Resource allocation is considered as a crucial technology to implement CMfg. Traditional resource allocation approaches in CMfg mainly focus on the optimal resource selection process, but the energy consumption for manufacturing resources is rarely considered. In response, this paper proposes a hybrid energy-aware resource allocation approach to help requestors acquire energy-efficient and satisfied manufacturing services. The problem description on energy-aware resource allocation in CMfg is first summarized. Then a local selection strategy based on fuzzy similarity degree is put forth to obtain appropriate candidate services. A multi-objective mathematical model for energy-aware service composition is established and the nondominated sorting genetic algorithm (NSGA-II) is adopted to conduct the combinatorial optimization process. Furthermore, a technique for order preference by similarity to an ideal solution is used to determine the optimal composite services. Finally, a case study is illustrated to validate the effectiveness of the proposed approach.

Aleatoric and epistemic uncertainties can be represented probabilistically in mechanical systems. However, the distribution parameters of epistemic uncertainties are also uncertain due to sparsely available or inaccurate uncertainty information. Therefore, a unified reliability measure method that considers uncertainties of input variables and their distribution parameters simultaneously is proposed. The uncertainty information for distribution parameters of epistemic uncertainties could be as a result of insufficient data or interval information, which is represented with evidence theory. The probability density function of uncertain distribution parameters is constructed through fusing insufficient data and interval information based on a Gaussian interpolation algorithm, and the epistemic uncertainties are represented using a weighted sum of probability variables based on discrete distribution parameters. The reliability index considering aleatoric and epistemic uncertainties is calculated around the most probable point. The effectiveness of the proposed algorithm is demonstrated through comparison with the Monte Carlo method in the engineering example of a crank-slider mechanism and composite laminated plate.

This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements.

Tolerance analysis is becoming increasingly important for tolerance design and optimization. When dealing with electromechanical products such as the array antenna, the form errors of the assembly must be considered. Traditional tolerance analysis with form errors relies on a double loop process, which is computationally expensive. A new tolerance analysis method is proposed in this paper, which can be achieved by a single loop process. First, a new tolerance modeling method considering form errors was proposed, it can represent the geometric error of a surface feature precisely. Then an effective sampling method was developed by introducing the variance separation method. An assembly simulation method was proposed to determine the final state of the whole assembly. Finally, the tolerance analysis was achieved based on the sufficient sample. The proposed analysis method was applied to an X-band spaceborne active-phased array antenna, numerical simulation results show the effectiveness of the method.

With the increase of the working frequency of the array antenna, tolerance design has become increasingly important. The state-of-art tolerance design methods mainly deal with the position tolerance of the discrete elements. However, the geometric errors of the whole array have resulted from two aspects: (1) the position errors of the discrete elements and (2) the form errors of the continuous reflection plate. To optimize the position tolerance and flatness simultaneously, a hybrid tolerance design method is proposed. First, the relation between the performance of the array antenna and hybrid tolerances was determined based on the second order Taylor expansion. Then the expectation and variance of the performance were derived. Finally, the hybrid tolerances were optimized and the performance of the antenna was improved. Simulation results proved the effectiveness and efficiency of the proposed hybrid tolerance design method.

The scatter degree of the fatigue samples is reduced when the nodal force based structural method is used for steel welded joints, while it is still high for aluminum alloy welded joints. Statistical method and rough set theory is used to fatigue analysis so that fatigue characteristic domains are determined and S-N curves are fitted. Experiment results show that fatigue life of the aluminum alloy welded joints is under the influence of some key factors and the fatigue data with the same characteristics distribute in a relatively independent area. Accordingly, a novel S-N curve modeling method of aluminum alloy welded joints based on the fatigue characteristics domain is proposed. In the proposed method, the nodal force based structural stress method is used for stress calculation and neighborhood rough set theory is used for character extraction to obtain the key factors. Then fatigue characteristics domains are divided and S-N curves are fitted on each fatigue characteristics domain instead of on the whole domain so that a set of S-N curves are obtained. Statistical results show that selection of the S-N curve for the aluminum alloy welded joints according to different fatigue characteristic domain is more accurate.

Reconstruction and projection mapping enable us to bring virtual worlds into real spaces, which can give spectators an immersive augmented reality experience. Based on an interactive system with RGB-depth sensor and projector, we present a combined hardware and software solution for surface reconstruction and dynamic projection mapping in real time. In this article, a novel and adaptable calibration scheme is proposed, which is used to estimate approximate models to correct and transform raw depth data. Besides, our system allows for smooth real-time performance using an optimization framework, including denoising and stabilizing. In the entire pipeline, markers are only used in the calibration procedure, and any priors are not needed. Our approach enables us to interact with the target surface in real time, while maintaining correct illumination. It is easy and fast to develop different applications for our system, and some interesting cases are demonstrated at last.

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.

Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the entire product manufacturing process to improve product quality and production efficiency. Data-driven product development is considered as one of the critical application scenarios of industrial IoT, which is used to acquire the satisfied and robust design solution according to customer demands. Performance analysis is an effective tool to identify whether the key performance have reached the requirements in data-driven product development. The existing performance analysis approaches mainly focus on the metamodel construction, however, the uncertainty and complexity in product development process are rarely considered. In response, this paper investigates a robust performance analysis approach in industrial IoT environment to help product developers forecast the performance parameters accurately. The service-oriented layered architecture of industrial IoT for product development is first described. Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. Furthermore, the predicted performance analysis method based on confidence interval estimation is developed to deal with the uncertainty to improve the robustness of the forecasting results. Finally, a case study is given to show the feasibility and effectiveness of the proposed approach.

With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems—including real-time performance and sequence segmentation—are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset.

In order to achieve high punching precision, good operational reliability and low manufacturing cost, the structural optimization of a high-speed press in the presence of a set of available alternatives comprises a heterogeneous multiple-attribute decision-making (HMADM) problem involving deviation, fixation, cost and benefit attributes that can be described in various mathematical forms due to the existence of multi-source uncertainties. Such a HMADM problem cannot be easily resolved by existing methods. To overcome this difficulty, a new heterogeneous technique for order preference by similarity to an ideal solution (HTOPSIS) is proposed. A new approach to normalization of heterogeneous attributes is proposed by integrating the possibility degree method, relative preference relation and the attribute transformation technique. Expressions for determining positive and negative ideal solutions corresponding to heterogeneous attributes are also developed. Finally, alternative structural configurations are ranked according to their relative closeness coefficients, and the optimal structural configuration can be determined. The validity and effectiveness of the proposed HTOPSIS are demonstrated by a numerical example. The proposed HTOPSIS can also be applied to structural optimization of other complex equipment, because there is no prerequisite of independency among various attributes for its application.