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The purpose of this paper is to improve the problem of kinematics incompatibility of human–exoskeleton in the existing rigid lower-limb exoskeleton (LLE).

In this paper, following an introduction, the motion characteristics of the human knee joint and the design method of the exoskeleton were introduced. A kinematics model of the LLE based on cross-four-bar linkage was obtained. The structural parameters of the LLE mechanism were optimized by the particle swarm optimization algorithm. The predefined trajectories used in the optimization process were derived from the ankle joint, not the instantaneous center of rotation of the knee joint. Finally, the motion deviation of the optimization result was simulated, and the human–exoskeleton coordination experiment was designed to compare with the traditional single-axis knee joint in terms of comfort and coordination.

The lower limb exoskeleton mechanism obtained in this paper has a good tracking effect on human movement and has been improved in terms of comfort and coordination compared with the traditional single-axis knee joint.

The customized exoskeleton design method introduced in this paper is relatively simple, and the obtained exoskeleton has better movement coordination than the traditional exoskeleton. It can provide a reference for the design of lower limb exoskeleton and lower limb orthosis.

This study aims to investigate the reliability issues of microvoid cracks in solder joint packages exposed to thermal cycling fatigue.

The specimens are subjected to JEDEC preconditioning level 1 (85 °C/85%RH/168 h) with five times reflow at 270°C. This is followed by thermal cycling from 0°C to 100°C, per IPC-7351B standards. The specimens' cross-sections are inspected for crack growth and propagation under backscattered scanning electronic microscopy. The decoupled thermomechanical simulation technique is applied to investigate the thermal fatigue behavior. The impacts of crack length on the stress and fatigue behavior of the package are investigated.

Cracks are initiated from the ball grid array corner of the solder joint, propagating through the transverse section of the solder ball. The crack growth increases continuously up to 0.25-mm crack length, then slows down afterward. The J-integral and stress intensity factor (SIF) values at the crack tip decrease with increased crack length. Before 0.15-mm crack length, J-integral and SIF reduce slightly with crack length and are comparatively higher, resulting in a rapid increase in crack mouth opening displacement (CMOD). Beyond 0.25-mm crack length, the values significantly decline, that there is not much possibility of crack growth, resulting in a negligible change in CMOD value. This explains the crack growth arrest obtained after 0.25-mm crack length.

This work's contribution is expected to reduce the additional manufacturing cost and lead time incurred in investigating reliability issues in solder joints.

The work investigates crack propagation mechanisms of microvoid cracks in solder joints exposed to moisture and thermal fatigue, which is still limited in the literature. The parametric variation of the crack length on stress and fatigue characteristics of solder joints, which has never been conducted, is also studied.

As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.

The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.

Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.

Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

To solve the problems caused by using precise molds for copper column positioning in the current column grid array package, this paper aims to optimize the proposed friction plunge micro-welding (FPMW) technology without mold assistance, to overcome the problems of low interfacial bonding strength, shrinkage cavities and flash defects caused by the low hold-tight force of solder on the copper column.

A pressurizing device installed under the drill chuck of the friction welding machine is designed, which is used to apply a static constraint to the solder ball obliquely downward to increase the hold-tight force of the peripheral solder on the copper column during welding and promote the friction metallurgical connection between them.

The results show that the application of static constraint during welding can increase the compactness of the solder near the friction interface and effectively inhibit occurrences of flash, shrinkage cavities and crystal defects such as vacancies. Therefore, compared with the unconstrained (UC) FPMW, the average strength of the statically constrained (SC) FPMW joints and aged SC-FPMW joints can be increased by 51.1% and 122.6%, and the problem of the excessive growth of the interfacial connection layer in the UC-FPMW joints during aging can be effectively avoided.

The application of static constraint effectively inhibits the occurrence of defects such as shrinkage cavities, vacancies and flash in FPMW joints, and the welding quality is significantly improved.

Micro-texture is processed on the surface to reduce the friction of the contact surface, and its application is more and more extensive. The purpose of this paper is to create a texture function model to study the influence of surface parameters on the accuracy of the simulated surface so that it can more accurately reflect the characteristics of the real micro-textured surface.

The microstructure function model of rough surfaces is established based on fractal geometry and polar coordinate theory. The offset angle θ is introduced into the fractal geometry function to make the surface asperity normal perpendicular to the tangent of the surface. The 2D and 3D contour surfaces of the surface groove texture are analyzed by MATLAB simulation. The effects of fractal parameters (D and G) and texture parameter h on the curvature of the surface micro-texture model were studied.

This paper more accurately characterizes the textured 3D curved surface, especially the surface curvature. The scale coefficient G significantly affects curvature, and the influence of fractal dimension D and texture parameters on curvature can be ignored.

The micro-texture model of the rough surface was successfully established, and the range of fractal parameters was determined. It provides a new method for the study of surface micro-texture tribology.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0298/

The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

This study examines how the characteristics of innovation knowledge exchanged among affiliate firms affect the ownership strategies adopted for their foreign subsidiaries.

This study employs a cross-classified multilevel model to examine a sample of 185 Korean manufacturing affiliates derived from 49 Chaebols engaged in international diversification, along with their 1,110 foreign manufacturing subsidiaries.

While exploratory innovation knowledge exchange lowers the affiliate's level of ownership in its foreign subsidiary, exploitative innovation knowledge exchange rather increases the affiliate's level of ownership in its foreign subsidiary.

This study advances the literature on intrafirm knowledge exchange by highlighting it as a determinant of ownership strategies. The study further shows that the characteristics of knowledge exchanged at the affiliate level not only determine the ownership structure but also have the potential to shape the direction in which the subsidiary develops its competencies.

This study has practical implications for the managers of business group affiliates. The results suggest that managers should adapt their ownership strategies according to the type of knowledge exchanged at the affiliate level to achieve a balanced and synergistic effect on intraorganizational knowledge exchange.

Previous studies have extensively explored the performance implications related to knowledge exchange. However, there is a notable gap in understanding the mechanisms through which the value of knowledge transferred within an affiliate is realized. To address this gap, this study focuses on ownership strategy as a crucial factor and empirically examines how the characteristics of innovation knowledge exchanged among affiliate firms influence the ownership strategies adopted for their foreign subsidiaries. By investigating this relationship, this study provides valuable insights into the complex dynamics of knowledge exchange and its effect on ownership decisions within business group affiliates.

Joint advertising is an emerging strategy where marketers promote both brands in the same marketing communication. This research determines how the domestic, foreign, or global nature of the partner impacts important brand-related outcomes and identifies underlying psychological process mechanisms and contextual variables that affect this relationship.

Across three experiments, we investigate how the type of joint advertising partner impacts consumer attitudes and behaviors. We establish the number of similarities between the partners and perceived cognitive fit as the mediating process mechanisms underlying this relationship, with holistic processing moderating the effect.

We find that when consumers are exposed to joint advertisements between domestic or global [foreign] brands, they will be able to generate more [fewer] similarities between the partners and perceive a stronger [weaker] cognitive fit. Moreover, these similarities interact with consumer cultural traits related to holistic processing style to differentially influence perceived cognitive fit and downstream consumer attitudes and behaviors.

Partnering for mutually beneficial, joint advertisements is a growing phenomenon that redefines traditional thinking about advertising, but the success of the joint advertisement is contingent upon the characteristics and compatibility of the partners.