Aiming at the contradiction between large stroke and high precision characteristics of precision positioning table, a coaxial integrated macro-micro composite actuators (MMCA) with large stroke and high precision characteristics is proposed by combining voice coil motor (VCM), and giant magnetostrictive actuator(GMA). In order to increase macro-motion stability and reduce fretting error, its structural parameters are optimized by finite element analysis, and the optimal combination mode is designed by using orthogonal experimental scheme. Finally, a model machine is developed and an experimental platform is built for performance testing. The results show that: macro external yoke(α), macro inner yoke(β), and Wall thickness of magnetic isolator(γ) it has different effects on macro-motion stability and fretting error of MMCA, When α=12mm, β=5mm, γ=12mm, the output performance of the MMCA is the best. The maximum macro-motion force of the model machine can reach 294.45N, and the maximum micro error is only 4nm. This shows that the proposed design scheme of coaxial integrated MMCA is reasonable and feasible, which lays a theoretical and technical foundation for further optimization and development of precision positioning worktable with, large stroke and high-precision characteristics.

Aiming at the problem that the friction heating phenomenon of rigid piston in traditional shock absorber has not been well treated, based on the idea of variable piston volume, a variable piston is invented, and its structure and throttling characteristics are studied. On this basis, a variable piston shock absorber is designed for vehicle. In the variable piston shock absorber, the flow resistance structures and paths of oil flowing through are different under various working conditions. According to the flow equivalent relationship between the choke structures, the pressure difference models at both ends of the choke structures are established, and further integrate into the overall damping force model of the shock absorber. Then, the damping characteristics of the variable piston shock absorber are simulated and analyzed, and the force-displacement characteristics and speed characteristics are verified on a test bench. The simulation and experimental results show that the variable piston shock absorber has good force-displacement and speed characteristics without direct contact and friction on structure, which can meet the requirements of vehicle operation.

The milling force of hollow thin-walled welded structural parts varies greatly at each position, and the analytical model was less studied, which affects the prediction accuracy of milling force. In order to construct an analytical model of the milling force, a model was constructed to calculate the cutting depth of the tool slot milling based on the contact relationship between the cutter and the workpiece. Moreover, based on the structural properties of the hollow thin-walled welded structural parts, the effective cutting depth function at different locations was derived by using infinitesimal element integration method, and the dynamic milling force prediction model was solved at each location. Furthermore, the differences between the theoretical and experimental values were compared and analyzed from the viewpoint of the peak value and the trend of the milling force, and the chip thickness was verified. The results show that the milling force is minimal in all directions at the position of the upper and lower skin zone, and maximal at the position of the inclined rib zone. What’s more, due to the large cutting depth at the position of the vertical plate zone, the milling force in X direction increases first and then decreases, and continue increases at the position of the vertical plate zone, besides, the milling force in Y and Z directions increases first and then decreases. What’s more, the prediction errors of the forces in X, Y and Z directions at the position of the upper and lower skin plate zone are 7.01%, 2.04% and 8.24%, respectively, the prediction errors of the forces in X, Y and Z directions at the position of the inclined rib plate zone are 3.22%、8.76% and 7.77%, respectively, and the prediction errors of the forces in X, Y and Z directions at the position of the vertical plate zone are 7.01%, 2.04% and 8.24%, respectively.

This study focused on demonstrating how to design riding behavior-based collision avoidance systems (RB-CAWS). Statistical and machine learning algorithms were adopted and analyzed previously collected data from the riding simulator-based experiments. The experiments involved 23 participants who completed a 20 km course under four driving conditions, including high and low arousal and positive and negative valence emotional states. Our analysis started by implementing structural equation modeling (SEM) to examine the relationship between riding behaviors measured by speed variability and lateral instability and how these factors influence riding performance. Results revealed that lateral instability strongly affects riding performance. The average lateral deviation from the center of the lane (DCavg) and rolling entropy (RE) were clarified as more influential riding behaviors. Next, using the decision tree (DT) algorithm, significant thresholds for DCavg and RE were determined as 0.302m and 0.019, respectively. The neural network (NN) algorithm was also adopted and defined 0.5 as the optimal threshold value at a combined risk level. We compared the models based on their collision prediction accuracy and found that the DT model performed better with an accuracy rate of 80% compared to the NN model's accuracy rate of 76%. Using the identified thresholds, we highlighted potential warning systems and provided a framework for developing an RB-CAWS that integrates SEM and DT analysis techniques. This study can benefit researchers of motorcyclists' riding behavior and designers of advanced rider assistance systems (ARAS).

Because of the non-obvious periodic impulses interfered by noise, harmonics and unexpected pulses, fault character extraction of rolling bearing is a difficult problem. Maximum correlated kurtosis deconvolution (MCKD) needs a tight choice of parameters, and any improper choice may greatly reduce the fault diagnosis performance of MCKD. To improve the fault diagnosis performance of MCKD, an adaptive MCKD method optimized by salp swarm algorithm based on mixed strategy (MSSSA) named MSSSA-MCKD is proposed. MSSSA is an improved salp swarm algorithm (SSA) based on mixed strategy to remedy the defects that SSA is apt to trap in local optimum and converges slowly. Through the analysis of impact of different improved strategies on the performance of SSA, performance comparison with other optimization algorithms and performance comparison with other improved SSA, the following conclusion can be reached: The optimization accuracy and convergence speed of MSSSA are superior. Whether the parameters are reasonable directly determines the performance of MCKD. The filter length and displacement number of MCKD are adaptively selected by MSSSA. Then the simulated fault signal and experimental fault signals of rolling bearing were processed by MSSSA-MCKD. The validity of MSSSA-MCKD was proved by the comparison of spectral analysis, MCKD with random parameters, empirical mode decomposition (EMD) and MSSSA-MCKD. Finally, the following conclusion can be reached: MSSSA-MCKD can precisely extract fault characters of rolling bearing.

Recently, owing to uncertain business environments, the supply chain has become more prone to disruption. In this paper, we model a closed-loop supply chain comprising suppliers, backup suppliers, factories, markets, collection centers, and disposal centers. We propose a robust optimization model for the design of a closed-loop supply chain network that is flexible and robust to disruptions. The proposed mathematical model considers, in the before-disruption stage, the location of the manufacturing factory, and contraction with the backup supplier in addition to under the contracting supplier, possibility of disruption of transshipment, and, in the reactive stage, depression of productivity at the facility. We aim to determine the location of the manufacturing facility, select the supplier, finalize the backup supplier contract, allocate stock at the before-disruption stage, and determine the quantities for production, transportation, and collection at the reactive stage of each scenario. The purpose is to minimize the total cost of the supply chain and regret value, which is the value that could reduce shortage or the delaying effect across different disruption scenarios. The proposed bi-objective mathematical model is solved using an optimization solver by weighting each objective function into a single objective problem. Computational results demonstrate the significant impact of considering disruptive events on the selected supply base and relieve of disruption effects of each scenario based on the decision of contracting backup suppliers.

Holm's contacts theory is a theory experimentally obtained for the contact electrical resistance value of metal-to-metal contacts. It has been reported that the value of this contact electrical resistance changes with the area of the contact surface if the contact pressure does not change. On the other hand, in cutting, as the flank wear of the tool progresses, the contact area between the tool and the work material increases. The authors have focused on the increase in the contact area between the tool and the work material due to the progress of tool wear and devised an in-process measurement method for the contact electrical resistance between the tool and the work material in intermittent cutting process. And it was found that there is a good relationship between the progress of tool flank wear and the change in contact electrical resistance between the tool and the work material. In this study, Holm's contact theory was used as a method to identify the flank wear width from the change in the contact electrical resistance value. Considering combinations of cutting tool materials, with or without of coating, use of cutting fluid, and work material grades, etc., the number of conditions to be verified is extremely large. As a result, although the cutting conditions were very limited, however good identification results were obtained in areas where the depth of cut was small.

In order to explore the aging-friendly design features of the facial expression interface of the elderly intelligent robot and reduce the cognitive burden of the elderly users, an optimal design method for the facial expression interface of the elderly intelligent service robot based on the user's emotional experience is proposed. Firstly, based on the PAD (Pleasure-Arousal-Dominance) emotion model, the recognition rate and satisfaction of the elderly users on the expression design style of the elderly intelligent service robot are tested, so as to explore the rules that affect the acceptance and emotional satisfaction of the elderly to the facial expression interaction. Secondly, in view of the PAD scale scores of 8 expression design schemes, the emotional tendency of the expression design scheme is obtained through the calculation of space distance. Finally, through the iteration of the design scheme, the improvement of the expression design scheme is completed, and these design solution is also validated by emotional tendency measure based on the PAD model. According to the experimental results, the PAD emotion model can effectively explore the characteristics and rules of the expression interaction design of intelligent service robots suitable for elderly, so as to accurately map the real emotional experience of the elderly. Furthermore, the PAD model can effectively guide designers to quickly and conveniently design and optimize the design scheme that meet user demands.

With the improvement of technology and living standard, nursing beds have become more automatic and intelligent, while gradually moving from the ward into the home. In addition to functional technology, the emotional experience has become the dominant factor influencing user purchases. Therefore, it is particularly important to consider user emotional requirements and performance metrics such as usability, ease of use in a coordinated way while maintaining high design efficiency. This paper presents a user-centered development model (UCDM) for products that covers the entire design process. The UCDM successfully complements the generic user-centered design (UCD) approach to effectively and efficiently complete product design. It is validated with the example of an automated nursing bed, which ensures the company's profitability while satisfying users' emotional requirements. Firstly, the Kano model is integrated with Quality of Functional Deployment (QFD) to help designers better understand user needs and locate key design areas and key technical issues for new product innovation. Then, Theory of Creative Problem Solving (TRIZ) is applied to efficiently propose available alternatives to technical issues. And later with the help of Kansei evaluation (KE) methods, the alternatives are scientifically evaluated and decided while providing a reference basis for subsequent optimization. Finally, all the new nursing bed alternatives designed according to the proposed method have been highly evaluated and satisfied by users. The results demonstrate that the proposed design method can help designers to accurately complete user-centered product innovation, scientifically guide the whole design process, and verify the scientific and effective performance of the UCDM.

As a critical part of modern industry, assembly line plays an important role in mass-producers because of its great advantages in production efficiency and construction cost. With the increasing demand for customized production, the fixed processing flow of assembly line is becoming more and more difficult to adapt to the changes. The introduction of automated guided vehicles (AGVs) as the material handling equipment improve the flexibility of assembly line, which also derives a complex AGV scheduling problem. In the recent years, because of the limitation in place, no-buffer assembly line appears in some plant, which derives a new problem, processing sequence conflicts (PSC). This paper builds a Petri net model to deal with the AGV scheduling problem and processing sequence conflicts in the no-buffer assembly line. A genetic algorithm based look-ahead scheduling algorithm (LASA) with processing sequence conflict-free is developed to minimize the total processing makespan in a no-buffer assembly line by introducing prospective tasks. Chain task strategy is integrated to further improve the performance of LASA in the case of AGVs deficiency. Two groups of simulation experiments are carried out to test the performance of the proposed algorithms in different scenarios. The results show the proposed algorithm has advantages in processing makespan, AGVs’ efficiency and machines’ efficiency. A robot experiment is carried out in a real no-buffer assembly line. The processing makespan of experiment is close to the theoretical lower bound.

Full automation of manufacturing is strongly desired to improve the productivity. Autonomous optimization of the cutting conditions in the end milling operation is one of the challenges in achieving this goal. This paper proposes a system for optimization of the cutting conditions based on Deep Q-Network (DQN), which is a kind of deep reinforcement learning. An end mill is used as an agent and the end milling simulation is employed to provide the environment in the proposed system. Geometric information of interference state between tool and workpiece in the simulation is considered as the state of the environment and acceleration of feed rate is the action for the agent to take. The action is optimized by DQN to maximize the accumulated reward given from the environment, which evaluates how good the scenario of action is. Therefore, the cutting conditions can be optimized according to the defined reward function. We performed three case studies to verify our proposed method, in which the cutting torque is controlled to be a specified value. The objective was successfully achieved regardless of differences in the end milling scenario. The obtained results strongly suggested a fact that the reinforcement learning is a promising solution to autonomous optimization of the cutting conditions.