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The layer section of laser additive manufacturing (AM) can be rasterized. Subsequently, the rasterized layer section can be converted into sparse matrix. However, large storage space is occupied due to the high manufacturing resolution. In order to reduce the storage space, the purpose of this research is to propose a lossless compression method to compress the sparse matrix.

A lossless compression method for additive manufacturing is proposed. According to manifold and irregularity feature of the object of laser AM, a lossless compression method called continuous rows compressed storage (CRCS) based on continuous rows is innovatively proposed. In particular, the better direction strategy of compression method is selected based on the side-projected area per layer.

Take human teeth as an example, compared with compressed sparse row (CSR), the CRCS has advantage up to 98.88% in storage space. Compared with block compressed sparse row (BCSR), the CRCS has advantage up to 60.04% in storage space.

The proposed CRCS could be employed to compress the sparse matrixes of rasterized layer sections of laser AM. Compared with common lossless compression method of sparse matrix, the compression ratio of CRCS is greater. CRCS is propitious to reduce the storage space usage, thereby improving transmission efficiency.

The purpose of this paper is to verify the performance and function of the scale-up prototypes by predicting the material and energy consumption on the basis of dimension-reduced prototypes. Additive manufacturing (AM) costs determine carbon emissions in total life cycle, among which material and energy consumption are major components. Predicting material and energy consumption is fundamental to reducing costs.

This paper presents a material and energy co-optimization method for AM via multiple layers prediction (MLP). Material and energy consumption are predicted to reduce the AM costs. In particular, scalable, complex curved surface component is used to improve forecasting efficiency. Subsequently, the back pressure distribution is obtained by scale-up specimens, which can lay the foundation for the ergonomic conceptual design.

Taking evolutionary ergonomic product as an example, the relative gravity direction of backrest is calculated. The material and energy consumption are predicted with low deviation. Physical experiments were carried out to validate information. Digital and physical tests have revealed that material and energy co-optimization improves manufacturing efficiency.

The innovatively proposed MLP method predicts material and energy consumption of scale-up prototypes to reduce the costs. It is propitious to improve the carbon emission efficiency in life cycle of AM. The originality may be widely adopted alongside increasing environmental awareness.

Real-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.

Sensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.

The proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.

This research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

A biomechanical design method of lightweight full contacted insole based on structural anisotropy bespoke (SAB) is proposed, which can better redistribute the stress distribution of SAB designed personalized insole.

The reconstructed joint biomechanics are simulated using finite element analysis (FEA) to develop a lightweight full contact insole. Innovatively, the anisotropic properties of the triply periodic minimal surface (TPMS) structure, which contribute to reducing insole weight, are considered to optimize stress distribution. Additionally, porosity and manufacturing time are included as design objectives. To validate the lightweight insole design, FEA is employed to simulate the stress distribution of the ergonomic insole, which can be fabricated by additive manufacturing (AM) with TPU.

With a little 0.924% loss in porosity, the maximum stress of lightweight SAB designed insoles is extremely decreased by 19.2917%.

The biomechanical design of the lightweight full contact insole based on SAB can effectively redistribute stress, avoid stress concentration and improve the mechanical properties of the ergonomic individual insole.

This work aims to provide a rapid robust optimization design solution for parallel robots or mechanisms, thereby circumventing inefficiencies and wastage caused by empirical design, as well as numerous physical verifications, which can be employed for creating high-quality prototypes of parallel robots in a variety of applications.

A novel subregional meta-heuristic iteration (SMI) method is proposed for the optimization of parallel robots. Multiple subregional optimization objectives are established and optimization is achieved through the utilisation of an enhanced meta-heuristic optimization algorithm, which roughly employs chaotic mapping in the initialization strategy to augment the diversity of the initial solution. The non-dominated sorting method is utilised for updating strategies, thereby achieving multi-objective optimization.

The actuator error under the same trajectory is visibly reduced after SMI, with a maximum reduction of 6.81% and an average reduction of 1.46%. Meanwhile, the response speed, maximum bearing capacity and stiffness of the mechanism are enhanced by 63.83, 43.98 and 97.51%, respectively. The optimized mechanism is more robust and the optimization process is efficient.

The proposed robustness multi-objective optimization via SMI is more effective in improving the performance and precision of the parallel mechanisms in various applications. Furthermore, it provides a solution for the rapid and high-quality optimization design of parallel robots.

The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution.

In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm.

The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods.

The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts.

A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms.

A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated.

The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited.

The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications.

The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

The assembly simulation in tolerance analysis is one of the most important steps for the tolerance design of mechanical products. However, most assembly simulation methods are based on the rigid body assumption, and those assembly simulation methods considering deformation have a poor efficiency. This paper aims to propose a novel efficient and precise tolerance analysis method based on stable contact to improve the efficiency and reliability of assembly deformation simulation.

The proposed method comprehensively considers the initial rigid assembly state, the assembly deformation and the stability examination of assembly simulation to improve the reliability of tolerance analysis results. The assembly deformation of mating surfaces was first calculated based on the boundary element method with optimal initial assembly state, then the stability of assembly simulation results was assessed by the density-based spatial clustering of applications with noise algorithm to improve the reliability of tolerance analysis. Finally, combining the small displacement torsor theory, the tolerance scheme was statistically analyzed based on sufficient samples.

A case study of a guide rail model demonstrated the efficiency and effectiveness of the proposed method.

The present study only considered the form error when generating the skin model shape, and the waviness and the roughness of the matching surface were not considered.

To the best of the authors’ knowledge, the proposed method is original in the assembly simulation considering stable contact, which can effectively ensure the reliability of the assembly simulation while taking into account the computational efficiency.