Electromechanical Products Research Articles

Exploring the Performance Impact of Neural Network Optimization on Energy Analysis of Biosensor

With the popularization of new energy vehicles, lithium battery systems, as the main components of new energy vehicles, have the characteristics of short life cycles and harmful substances inside. The green treatment of lithium battery systems has become a research hotspot. Disassembly and recycling are essential means of reusing waste in lithium battery systems. Due to the wide variety of lithium battery systems, the lack of unified design standards, and the high flexibility requirements for disassembly, manual disassembly is currently the primary method used. However, this method can cause health hazards to oneself when dismantling some harmful components. The optimization of the dismantling process route for lithium batteries is a crucial step before dismantling, which directly determines the economic benefits of dismantling. However, unlike general electromechanical products, lithium batteries have prominent safety issues during the dismantling process, so the safety requirements for their dismantling process route are relatively high. Given the substantial absence of parametric evaluation and modification in prior research, this work investigates the influence of the most significant factors on the power density of biosensors. A conduction-based framework was employed to ascertain these variables, and the calculations were performed utilizing a neural network. The neural network was developed with Particle Swarm Optimization (PSO). Based on this, this article considers studying the optimization method of the lithium battery safety disassembly process to maximize safety and economic benefits comprehensively. Based on the essential characteristics of lithium-ion battery systems, an analysis is conducted on the allocation method of difficulty level for human-machine cooperation tasks and the impact indicators of task allocation. Then, a product disassembly hybrid diagram is established, and on this basis, multiple sets of human-machine cooperation disassembly sequences are generated. Finally, a multi-objective optimization model for disassembly cost, difficulty, and time is established. Finally, taking the Tesla Model 1sPBS waste lithium battery as an example, the safety prediction model for dismantling the waste lithium battery and the optimization model for the safety dismantling process route were solved to verify the effectiveness of the above optimization method.

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

A Novel Polynomial-Time Algorithm for Automatic Layout of Branching Cables in a Fixed Topology

Designing the layout for complex electromechanical products involves the challenging task of automatically laying out cables. This challenge is particularly pronounced in the case of branch cables, which are more intricate due to their multiple connection terminals and branches. This paper presents a polynomial-time wiring algorithm based on dynamic programming to determine branching point locations in the layout design of cables, given the electrical definition of the wire harness. The method considers various engineering constraints, including obstacle avoidance, wall adherence, minimum bend radius, and gray areas. To validate our method, we implemented a branch cable auto-layout system through secondary development based on the UG platform. The experimental results indicate the effectiveness of our approach, demonstrating promising performance in terms of time and high-quality layouts. This showcases its potential for practical application in cable layout design for complex electromechanical systems.

Dynamic Scheduling of Multi-agent Electromechanical Production Lines based on Iterative Algorithms

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

Analysis of the Impact of the United States' Decoupling of Trade with China on the Global Value Chain Division of China's Mechanical and Electrical Industry

This article matches the cases of China US trade frictions to various sub sectors of the electromechanical industry, considers the comparative advantage index of heterogeneous product quality, and uses regression analysis to simulate different scenarios of tariffs. It directly affects the participation of Chinese electromechanical products in the global value chain division of labor, and provides reasonable suggestions for maintaining the comparative advantage position of the electromechanical industry in the global division of labor. The conclusion of this article is as follows: 1. The decoupling and chain breaking measures taken by the United States towards China have had an impact on China's global value chain division of labor in the electromechanical industry, and overall have suppressed its comparative advantage position; 2. The decoupling and chain breaking measures have a more significant impact on the global value chain division of labor in the electromechanical industry in recent times; 3. It is suggested to timely promote measures to extend, strengthen, and supplement the supply chain, and build a unified national factor market to mitigate the negative impact of the United States' decoupling and block chain measures.

Application of deep learning in noise detection of electromechanical products

In recent years, machine learning (ML) techniques have been widely integrated into manufacturing processes. This study explores supervised learning-based machine learning models designed for noise detection. The approach involves feature engineering, extracting relevant features from signals to provide meaningful inputs for machine learning models. Various algorithms, including convolutional neural networks (CNN) and other deep learning architectures, are employed to capture complex dependencies within the data. Training processes involve optimization techniques to enhance model performance and generalizability. Performance evaluation on benchmark datasets compares the proposed model with existing noise detection methods. Results demonstrate the outstanding accuracy and robustness of the generated machine learning model in distinguishing noise from clear signals. Additionally, the study explores the transferability of these models to new scenarios, highlighting their adaptability in practical applications. Findings from this research contribute to advancing noise detection methods, showcasing the effectiveness of machine learning in addressing this crucial challenge. The proposed solution is cost-effective, structurally simple, and holds vast potential for widespread implementation in factory settings in the future.

A Markerless AR Guidance Method for Large-Scale Wire and Cable Laying of Electromechanical Products

A Markerless AR Guidance Method for Large-Scale Wire and Cable Laying of Electromechanical Products

The material selection method for functional packaging of electromechanical products based on low carbon and cost constraint

Abstract Packaging is an important but easily overlooked part of the study of low-carbon lifecycle of electromechanical products, but with the significant growth of the express delivery business, the carbon emissions caused by packaging are increasingly noteworthy. In order to explore the carbon emissions of packaging lifecycle, the electromechanical products are used as the packaging object to discuss the connotation of functional packaging, low-carbon design boundary and research methods of packaging lifecycle and establish the calculation models of carbon footprint and cost of packaging lifecycle and the evaluation method of material selection. The case study of gasoline engines shows that material selection plays a decisive role in the packaging of electromechanical products based on low carbon and cost constraints.

Research on the design course of intelligent electromechanical products

Research on the design course of intelligent electromechanical products

A Geometric Digital Twin Modeling Method for Thin-Walled Microstructures Considering Laterally Distributed Error

Precision microstructures are the core devices of electromechanical products, and their accuracy directly affects the product’s overall performance. Laterally thin-walled structures such as precision tooth plates and silicon arms are the most common types of precision microstructures. Due to the characteristics of their tiny size and complex shape, the laterally thin-walled surface is challenging to measure and model, which in turn makes it impossible to realize the accurate prediction and quantitative control of its assembly accuracy. To address this problem, this paper proposes a geometric digital twin modeling method considering the laterally distributed error of thin-walled microstructures. A geometric digital twin model that accurately represents the geometric distributed error of the structure’s lateral surfaces is built by applying an image measuring instrument to measure the point clouds of the upper and lower surface profiles, followed by point cloud filtering and merging based on a common datum, and finally surface reconstruction and integration with an ideal 3D model. The method is effectively validated on the silicon arm, a vital component of the thermal mechanical package. Its geometric modeling error is less than 1/10 of its tolerance, and the relative error between the model-based stiffness simulation calculation results and the experimental data is less than 10%. The method can effectively analyze the influence of the manufacturing errors of thin-walled lateral surfaces on the assembly accuracy and provides an adequate model basis for the control of the geometrical and physical performances of precision microstructures.

An Empirical Study on the Impact of Digital Economy Innovation Development on the Export Quality of Chinese Electromechanical Products

This study measures the export quality of electromechanical products using data from China’s electromechanical products from 2011 to 2021, empirically investigates the effects of the level of innovation and the growth of the digital economy (DE) on the export quality of electromechanical products, and assesses potential industry heterogeneity. With an erratic upward trend, it is observed that the export quality of Chinese electromechanical products has suffered twice. China’s DE has demonstrated a rising trend in innovation and growth year over year. The creation and growth of the DE throughout the study period had an inverted “U” dynamic effect on the export quality of electromechanical goods, which was initially boosted and later decreased. It is possible to raise the export quality of Chinese mechanical and electrical products by expanding firm size and raising human resource levels. The export quality of Chinese mechanical and electrical products is not much impacted by innovation, R&D capacity, or degree of openness. The inverted “U”-shaped dynamic impacts of the DE’s creation and development on the export quality of electromechanical products are still present after endogeneity analysis and robustness testing, which initially promote and then suppress the rate. The inverted “U”-shaped relationship between DE innovation development and the quality of electromechanical product exports of the manufacture of computers and other electronic equipment, as well as general equipment manufacturing, is still significant, according to the results of sub-industry tests. The study presented in this article offers policy recommendations for facilitating the innovative growth of the DE and raising the standard of electromechanical exports from China.

Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions

Drastic changes in the random load of an electromechanical system bring about a reliability problem for the proportional solenoid valve based on a thermal effect. In order to accurately and effectively express the thermal load of a proportional solenoid valve under random load conditions and to meet the requirements of online acquisition, adaptive anomaly detection, and the missing substitution of thermal load data, a thermal load prediction model based on the Kalman filter algorithm is proposed. Taking the compound operation process of an excavator as the object and based on the field testing of an excavator and the independent testing experiment of a proportional solenoid valve in a non-installed state, a method of obtaining historical samples of the proportional solenoid valve’s power and thermal load is given. The k-means clustering algorithm is used to cluster the historical samples of the power and thermal load corresponding to the working posture of a multi-tool excavator. The Grubbs criterion is used to eliminate the outliers in the clustering samples, and unbiased estimation is performed on the clustering samples to obtain the prediction model. The results show that the cross-validation of the sample data under the specific sample characteristics of the thermal load model was carried out. Compared with other methods, the prediction accuracy of the thermal load model based on the Kalman filter is higher, the adaptability is strong, and the maximum prediction deviation percentage is stable within 5%. This study has value as a reference for random cycle thermal load analyses of low-frequency electromechanical products.

Research on kitting evaluation method of complex electromechanical products

Precise and efficient product kitting evaluations are vital for boosting assembly performance and customer contentment for varied, small-batch, and intricate electromechanical products. However, current enterprise resource planning systems (ERP) provide inadequate support for analyzing the kitting of products. How to evaluate the degree to which customers' demands for a complete set of multiple products can be met when adjusting the importance level of customer demands without changing the customer demand date in the production system and affecting the scheduled customer orders has become one of the urgent problems that enterprises need to solve. In response to the above issues, this article presents a method for evaluating product kitting, which takes the maximum quantity of complete sets of products as the optimization objective, considers multiple constraints such as material inventory data and in process business data, and designs a multi-level BOM decomposition algorithm. Finally, a case study of a company producing core components for standard high-speed trains demonstrates the practicality and efficacy of the proposed method.

Flexible stretchable tribo-negative films with exceptional output performance for high-temperature energy harvesting and self-powered sensor

High temperature is a significant cause of failure of electromechanical products, achieving effective collection of triboelectric energy at high-temperatures is a major research emphasis. Improving the electrical outputs from triboelectric nanogenerator (TENG) in extreme environments is crucial for its large-scale application and remains challenging. Further considering the major potential application for TENG is wearable electronics, a series of SEBS/fatty acid (SEBS/FA) skin-like flexible films were fabricated, which exhibit adjustable surface triboelectricity and enhanced triboelectric outputs at high-temperatures. The SEBS/FA based TENGs possess remarkable durability of over 10,000 contact-separation cycles, and exhibit exceptional electrical outputs in the heat. Thereinto, the output efficiency of SEBS-OA (octanoic acid) is up to 302.32 % at 110 °C and still above 100 % at 150 °C. The SEBS/FA based TENGs can efficiently harvest the mechanical energy in high-temperature environments to power commercial capacitors and LEDs. Notably, the SEBS/FA films still show enhanced electrical outputs even under extreme conditions with working ambient temperatures up to 90 °C and significant deformation (λ ≤ 4). The excellent electromechanical performances enable SEBS/FA based TENGs to be served as flexible energy collector and self-powered touch sensor for motion capture. The real-time tactile trajectory can be accurately mapped by a 5 × 5 sensing array. The SEBS/FA films exhibit great potential in energy harvesting and active real-time tactile sensing system in extreme environments.

Multi-domain Mapping design Method of Automatic Disassembly Equipment for Small Electromechanical Products

The design of automatic disassembly equipment for small end-of-life (EOL) electromechanical products is difficult and time-consuming. To deal with these problems, a novel automatic disassembly equipment design method for small EOL electromechanical products based on axiomatic design and modular design is presented. To realize the mapping from disassembly knowledge to the functional requirements of automatic disassembly equipment, a transfer mapping model from the structure to disassembly methods of small EOL electromechanical products is constructed based on polychromatic sets, which are regarded as the key information of the customer domain. Furthermore, a multi-domain mapping model of the automatic disassembly equipment is constructed through axiomatic design, and the mapping from the customer domain to the functional domain and then to the structure domain in the design process of automatic disassembly equipment is gradually realized. Based on the information in the structure domain, the related modules of the automatic disassembly equipment are divided and designed. Finally, taking a power strip and a set-top box as the experimental objects, the proposed method is applied to the design of automatic disassembly equipment. The results prove that the proposed method improves the design efficiency of automatic disassembly equipment and has strong universality.

A reliability evaluation method of complex electromechanical products based on the multi-stress coupling acceleration model

This paper proposes a reliability evaluation method based on a multi-stress coupling acceleration model. It can be used for testing complex electromechanical products that have high reliability, long lifetimes and small samples. Considering complex and diverse failure modes, as well as the coupled competitive characteristics of electromechanical products, a coupling competition physics of failure (PoF) model based on a Copula function was constructed, together with a proposed solution. Regarding the complex stress types and coupling relations that occur during the service of electromechanical products, a multi-stress coupling quantitative method based on a coupling matrix is proposed to analyse multi-stress coupling failure mechanisms. Moreover, the developed model is a multi-stress coupling acceleration model, which is solved using a maximum-likelihood function and covariance matrix. Finally, the reliability of a servo system was evaluated and the results compared with those of a traditional method without consideration of coupling competition of failure modes and multi-stress coupling. The proposed method is superior in terms of evaluation accuracy and application range.

Screening Critical Design Parameters for Electromechanical Products Performance Matching Based on Energy and Causal Model

The design parameters are the most momentous factors in carrying out performance matching. For complex electromechanical products with a large number of design parameters, determining a set of critical design parameters which have a great influence on the performance is the premise of performance matching. In this paper, from a systematic perspective, a screening method of critical design parameters based on energy and a causal model is proposed. Since energy is the driving force of the product operation to achieve performance, the design parameters affect the performance through energy flow in the product. Therefore, the causal model among design parameters, characteristic energy, and performance is established, where its path coefficients are determined based on the quantitative calculation of the energy flow simulation model. Then, the performance pertinence is defined and calculated to describe the comprehensive influence of the design parameters on performance and to screen the critical parameters. Finally, the performance matching process is presented to support the performance matching. With a refrigerator as an example, 5 parameters were screened from 11 variable design parameters, and day power consumption decreased by 6.85%, which verifies the effectiveness of the method.

Petri Nets-Based Modeling Solution for Cyber–Physical Product Control Considering Scheduling, Deployment, and Data-Driven Monitoring

For a complex electromechanical product that is a cyber–physical system (CPS), its dynamic behaviors are embodied in the closed-loop control between the logic process in its cyber component and actual actuators/sensors in its physical component, and thus, a well-defined model of the control is important to create a digital twin that acts as much like the real machine as possible. This article proposes a Petri nets (PNs)-based modeling solution that employs hybrid PNs (HPNs) for physics and system of sequential systems with shared resources (S4R) nets for logic in building a hierarchical control model. We also present PNs technologies for implementing a smooth transition and bidirectional mapping from the virtual prototype to the real machine. These technologies involve a PNs integration of a reinforcement learning (RL) method for generating a workflow scheduling agent in design, an extension of PNs definitions that is compatible with the microcontroller for easy deployment in manufacturing, and an architecture of PNs execution recording for data-driven monitoring in service. A software kit is provided for the solution that includes an integrated development environment of PNs, tools for quickly building a virtual prototype, and a monitor server for remote data-driven monitoring. This solution is successfully applied in the development of a typical cyber–physical product case, namely, the chemiluminescence immunoassay (CLIA) analyzer.

Multi-Objective Surrogate Modeling Through Transfer Learning for Telescopic Boom Forklift

Simulation and optimization methods have been widely used in forklift design due to their cost-effectiveness. However, this type of method involves challenges such as the accuracy of the simulation model and the simulation solution time. These challenges reduce the stability and precision of the surrogate model and hence generate further optimization errors. In this paper, a multi-objective surrogate modeling (MSM) method for telescopic boom forklifts based on closed-loop transfer learning is proposed in order to solve these challenges. The MSM consists of the following two steps: to pre-train an initial deep neural network model (deep model) with a large amount of existing simulation data from the same type of forklift and to transfer the model with a small amount of measurement data collected on the current forklift. A general framework for deep neural network (DNN) training is introduced to improve the approximation ability of the initial model. Moreover, a novel uncertainty-analysis-based sampling method is suggested for measurement data development, and combined with transfer learning to form a closed-loop mode to improve the stability of the final model. The superiority of MSM is demonstrated through comparative studies with the fine-tuning method on a telescopic boom forklift with two objectives. The experimental results show that the Correlation coefficient (R) of the deep model can reach 0.9971 by using only 80 sets of training data. In addition, it can also achieve an improvement of at least a 13.25% reduction in Root Mean Squared Error (RMSE) and a 9.19% reduction on average in Maximum Absolute Error (MAE), as well as stronger robustness compared to the benchmarks. Furthermore, it will provide a valuable reference for the simulation optimization of complex electromechanical products.

Research on Unified Modeling Method of Knowledge Graph towards the Full Life Cycle of Decommissioned Electromechanical Products

Research on Unified Modeling Method of Knowledge Graph towards the Full Life Cycle of Decommissioned Electromechanical Products