Abstract
Alloy additive manufacturing technology has been widely used in aerospace, medical equipment and automobile manufacturing. The purpose of this study is to explore the thermal energy optimization scheme in the process of alloy additive manufacturing based on edge computing technology, and to display the metal process products in an all-round way through visual design, so as to promote the knowledge and practice of thermal energy management in the industry. In this paper, the edge computing platform is used to monitor the heat energy data generated in additive manufacturing process in real time, and the heat energy distribution is analyzed with machine learning algorithm. By establishing thermal energy optimization model, adjusting manufacturing parameters to reduce energy consumption, improve material utilization and product quality. At the same time, using visual design technology, the optimization process and results are graphically represented, which provides intuitive decision basis for engineers. The experimental results show that the thermal energy optimization scheme based on edge computing significantly improves the thermal energy efficiency of the alloy additive manufacturing process, and significantly reduces the thermal energy loss. Through the visual design, relevant personnel can understand the thermal energy distribution and process changes more clearly, and promote the collaboration between multiple departments. This study reveals the application potential of edge computing in alloy additive manufacturing and provides an effective thermal optimization strategy.
Published Version
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