Guojun Zhang, Fenglei Ni, Hong Liu, Zainan Jiang, Guocai Yang and Chongyang Li
The purpose of this paper is to transfer the impedance regulation of manual belt grinding to robot belt grinding control.
Abstract
Purpose
The purpose of this paper is to transfer the impedance regulation of manual belt grinding to robot belt grinding control.
Design/methodology/approach
This paper presents a novel methodology for transmitting human impedance regulation skills to robot control in robot belt grinding. First, according to the human grinding experimental data, the skilled worker’s arm impedance regulation is calculated. Next, the human skills are encapsulated as the statistical learning model where the kernel parameters are learned from the demonstration data by Gaussian process regression (GPR) algorithms. The desired profiles of robot are generated by the task planner based on the learned skill knowledge model. Lastly, the learned skill knowledge model is integrated with an adaptive hybrid position-force controller over the trajectory and force of end-effector in robot belt grinding task.
Findings
Manual grinding skills are represented and transferred to robot belt grinding for higher grinding quality of the workpiece.
Originality/value
The impedance of the manual grinding is estimated by k-means++ algorithm at different grinding phases. Manual grinding skills (e.g. trajectory, impedance regulation) are represented and modeled by GMM and GPR algorithms. The desired trajectory, force and impedance of robot are generated by the planner based on the learned skills knowledge model. An adaptive hybrid position-force controller is designed based on learned skill knowledge model. This paper proposes a torque-tracking controller to suppress the vibration in robot belt grinding process.
Details
Keywords
Dong Liu, Yongchuan Bao and Guocai Wang
The purpose of this study is to examine how formal contracts affect alliance innovation performance. To understand the mechanism underlying the impact, this study tests whether…
Abstract
Purpose
The purpose of this study is to examine how formal contracts affect alliance innovation performance. To understand the mechanism underlying the impact, this study tests whether relationship learning mediates the impact of formal contracts on alliance innovation performance and how guanxi moderates the mediating effect.
Design/methodology/approach
This study is conducted with a sample of 225 manufacturers in China. This paper used hierarchical regression analysis to test the hypotheses and used the PROCESS method to test the mediating effect of relationship learning.
Findings
Formal contracts positively affect relationship learning, which facilitates alliance innovation performance. Guanxi positively moderates the effect of formal contracts on alliance innovation performance. Relationship learning mediates the relationship between formal contracts and alliance innovation performance. Moreover, guanxi positively moderates the mediating effect.
Research limitations/implications
Future research could investigate factors moderating the effect of guanxi on alliance innovation performance and moderating the effect of relationship learning on alliance innovation performance. Future research can also use secondary data to measure alliance innovation performance. Future researchers can examine how guanxi as a relational mechanism governance affects relationship learning.
Practical implications
Managers should conduct relationship learning in the process of alliance innovation and realize that reducing opportunism does not mean improving innovation performance. Moreover, managers should know that guanxi could contribute to alliance innovation performance with the help of formal contracts.
Originality/value
Prior studies have mainly focused on the fundamental requirement of governing knowledge exchange in alliances. Little is known about the mediating effect of relationship learning on the relationship between formal contracts and outcomes of innovation alliances. This study contributes to the literature by filling the gap.