Shuyou Zhang

Disassemblability modeling technology of configurable product based on disassembly constraint relation weighted design structure matrix(DSM)

The current research of configurable product disassemblability focuses on disassemblability evaluation and disassembly sequence planning. Little work has been done on quantitative analysis of configurable product disassemblability. The disassemblability modeling technology for configurable product based on disassembly constraint relation weighted design structure matrix (DSM) is proposed. Major factors affecting the disassemblability of configurable product are analyzed, and the disassembling degrees between components in configurable product are obtained by calculating disassembly entropies such as joint type, joint quantity, disassembly path, disassembly accessibility and material compatibility. The disassembly constraint relation weighted DSM of configurable product is constructed and configuration modules are formed by matrix decomposition and tearing operations. The disassembly constraint relation in configuration modules is strong coupling, and the disassembly constraint relation between modules is weak coupling, and the disassemblability configuration model is constructed based on configuration module. Finally, taking a hydraulic forging press as an example, the decomposed weak coupling components are used as configuration modules alone, components with a strong coupling are aggregated into configuration modules, and the disassembly sequence of components inside configuration modules is optimized by tearing operation. A disassemblability configuration model of the hydraulic forging press is constructed. By researching the disassemblability modeling technology of product configuration design based on disassembly constraint relation weighted DSM, the disassembly property in maintenance, recycling and reuse of configurable product are optimized.

Interruption performance design of variable freedom mechanism triggered by electro-mechanical-magnetic coupling

Coupling mechanism plays an important role in transmitting, motivating and actuating mechanical functions. However, it is difficult to obtain the transient dynamics performance of mechanism with variable degree of freedom precisely. Therefore, an interruption performance design method of variable freedom mechanism triggered by electro-magneto-thermo coupling is put forward. The Euler-Lagrange partial differential equations of variable freedom mechanism are built using generalized coordinates. Degree of freedom reduction rules are proposed to merge transformation or rotation constraints and obtain the total degrees of freedom of variable freedom mechanism at each transient status. Bivariate interpolating is employed to determine the electro-mechanical-magnetic coupled Lorentz force. Dynamics performance is simulated by iteration of linear algebraic equations using implicit predictor-corrector integration method. The design parameters such as stiffness and pre-tightening force of trigger spring, permissible dimension deviations and hole-shaft fit tolerance are determined and improved using the sensitivity analysis of simulation results. The pneumatic mechanical endurance and thermal infrared temperature rise experiments are accomplished to determine the infrared radiation energy distribution and transient working status of components. It gives an auxiliary thermo-visual approach for transient performance design of coupling mechanism.

A Research Review on the Key Technologies of Intelligent Design for Customized Products

Abstract 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.

Energy-Efficient Enhancement for Viscoelastic Injection Rheomolding Using Hierarchy Orthogonal Optimization

The viscoelastic injection molding involves multidisciplinary thermoplastic rheomolding parameters which is a complex mathematical problem. Particularly for rheomolding of complex parts with thin-walled structure, boss, and grooves, the increasing higher requirements on energy efficiency and rheomolding quality are put forward. Therefore, an energy-efficient enhancement method for viscoelastic injection molding using hierarchy orthogonal optimization (HOO) is proposed. Based on the thermoplastic rheomolding theory and considering the viscoelastic effects in injection molding, a set of partial differential equations (PDE) describing the physical coupling behavior of the mold-melt-injection molding machine is established. The fuzzy sliding mode control (FSMC) is used to reduce the energy consumption in the control system of the injection molding machine’s clamping force. Then, the HOO model of viscoelastic injection rheomolding is built in terms of thermoplastic rheomolding parameters and injection machine parameters. In initial hierarchy, through Taguchi orthogonal experiment and Analysis of Variance (ANOVA), the amount of gate, melt temperature, mold temperature, and packing pressure are extracted as the significant influence parameters. In periodical hierarchy, the multiobjective optimization model takes the forming time, warping deformation, and energy consumption of injection molding as the multiple objectives. The NSGA-II (Nondominated Sorting Genetic Algorithm II) optimization is employed to obtain the optimal solution through the global Pareto front. In ultimate hierarchy, three candidate schemes are compared on multiple objectives to determine the final energy-efficient enhancement scheme. A typical temperature controller part is analyzed and the energy consumption of injection molding is reduced by 41.85%. Through the physical experiment of injection process, the proposed method is further verified.

A knowledge push technology based on applicable probability matching and multidimensional context driving

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push’, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

An EEG Study of a Confusing State Induced by Information Insufficiency during Mathematical Problem-Solving and Reasoning

Confusion is a complex cognitive state that is prevalent during learning and problem-solving. The aim of this study is to explore the brain activity reflected by electroencephalography (EEG) during a confusing state induced by two kinds of information insufficiencies during mathematical problem-solving, namely, an explicit situation that clearly lacked information and an implicit situation in which the missing information was hidden in the problem itself, and whether there is an EEG difference between these two situations. Two experimental tasks and three control tasks were created. Short time Fourier transformation (STFT) was used for time-frequency analysis; then the alpha task-related-power (TRP) changes and distributions, which are closely related to cognitive processing, were calculated, and repeated measures ANOVA were performed to find the significant difference between task conditions. The results showed that the alpha power decreased significantly in the regions related to calculation when the participants encountered both explicit and implicit information insufficiency tasks compared to the control tasks, suggesting that confusion can cause more brain activity in the cortical regions related to the tasks that induce confusion. In addition, the implicit information insufficiency task elicited more activity in the parietal and right temporal regions, whereas the explicit information insufficiency task elicited additional activity in the frontal lobe, which revealed that the frontal region is related to the processing of novel or unfamiliar information and the parietal-temporal regions are involved in sustained attention or reorientation during confusing states induced by information insufficiency. In conclusion, this study has preliminarily investigated the EEG characteristics of confusion states, suggests that EEG is a promising methodology to detect confusion, and provides a basis for future studies aiming to achieve automatic recognition of confusing states.

An improved geometric error analysis method considering the variety of sensitivities over working space

Machine tool accuracy analysis has become increasingly important since accuracy as the major parameter of a machine is to a large extent determined by geometric accuracy design. In order to improve the comprehensiveness and veracity of geometric accuracy design, this article proposes an improved geometric error analysis method considering the variety of sensitivities over working space. A multi-rigid-body model which includes cutting tool’s wear-out error and workpiece’s clamping error is established to represent the position relationship of machine tool’s working components. The expression of geometric error is converted from matrix form to screw form through the screw mapping theory, so that rotational error can be expressed and calculated directly like the translational error. Considering motion errors along axes over the whole working space instead of at a fixed position, an improved sensitivity analysis algorithm is conducted to identify, among 38 components of errors increased the variety with tool wear and clamping errors, which of them have a significant impact on four different types of machine errors. Finally, the proposed method was implemented and validated on a horizontal boring machine, and the sensitivity analysis results over working space would offer vital evidence for the machine’s geometric accuracy design.