Long Li, Binyang Chen and Jiangli Yu
The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point…
Abstract
Purpose
The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.
Design/methodology/approach
Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.
Findings
By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.
Originality/value
The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.
Details
Keywords
Zhuolin She, Quan Li, Manuel London, Baiyin Yang and Bin Yang
The purpose of this paper is to examine the relationships between CEO narcissism and strategic decision-making (SDM) processes (decision comprehensiveness and decision speed), and…
Abstract
Purpose
The purpose of this paper is to examine the relationships between CEO narcissism and strategic decision-making (SDM) processes (decision comprehensiveness and decision speed), and to explore the mediating role of top management team (TMT) members’ participation in decision making and the moderating role of TMT power distance.
Design/methodology/approach
Data were collected from a multisource, time-lagged survey of 103 CEOs and their corresponding TMT members in China. Structural equation modeling was used to test the hypothesized relationships.
Findings
The results indicated that CEO narcissism was negatively related to decision comprehensiveness and positively related to decision speed. These relationships were mediated by TMT members’ participation in decision making, especially when TMT power distance was high.
Practical implications
The results show the potential negative effects of CEOs’ narcissistic personality and suggest ways to attenuate it by increasing TMT participation and decreasing TMT power distance.
Originality/value
This study is an initial attempt to empirically examine how and under what conditions CEOs’ narcissism is a barrier to more comprehensive and more deliberate (slower) SDM.