Data-Driven Coupling Coordination Development of Regional Innovation EROB Composite System: An Integrated Model Perspective

To promote coupling coordination development for regional innovation environment-resource-output-benefit (EROB) composite systems, we propose a data-driven integrated model method for measurement, evaluation, and identification. First, we construct an evaluation indicator system of coupling coordination development of regional innovation EROB composite systems. Second, we apply the entropy method to measure indicator weights and comprehensive development indices of regional innovation composite systems. The coupling coordination degree model is used to calculate and evaluate four subsystems’ coupling coordination development levels. The obstacle degree model is used to identify the main obstacle factors affecting coupling coordination development. Finally, using panel data of the Yangtze River Delta region (three provinces and one city) between 2014–2019 as a case study, we test the integrated model method. The results show that the comprehensive development level of the regional innovation EROB composite system in the Yangtze River Delta region maintained a stable growth trend; the coupling coordination development level among four subsystems continuously improved, with the main obstacle being the innovation resource subsystem. Accordingly, targeted policy suggestions are put forward. This study not only provides theoretical and methodological support for evaluating and optimizing regional innovation composite systems but also provides decision-making support for sustainable and high-quality development of regional economies

Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes

Drilling processes, as some of the most widely used machining processes in the manufacturing industry, play an important role in manufacturing process energy-saving programs. However, research focus on energy modeling of drilling processes, especially for the modeling of material-drilling power, are really scarce. To bridge this gap, an improved material-drilling power model is proposed in this paper. The obtained material-drilling power model can improve the accuracy of the material-drilling power and lay a good foundation for energy modeling and optimization of drilling processes. Finally, experimental studies were carried out on an XHK-714F CNC machining center (Hangzhou HangJi Machine Tool Co., Ltd., Hangzhou, China) and a JTVM6540 CNC milling machine (Jinan Third Machine Tool Co., Ltd., Jinan, China). The results showed that predictive accuracies with the proposed model are generally higher than 96% for all the test cases