首页|Reports from Nanyang Technological University Provide New Insights into Machine Learning (Recent Progress of Sensing and Machine Learning Technologies for Proce ss Monitoring and Defects Detection In Wire Arc Additive Manufacturing)
Reports from Nanyang Technological University Provide New Insights into Machine Learning (Recent Progress of Sensing and Machine Learning Technologies for Proce ss Monitoring and Defects Detection In Wire Arc Additive Manufacturing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Wire Arc Additive Manufa cturing possesses advantages of high deposition rate and low cost compared with other metal additive manufacturing processes. However, potential defects may occ ur during the process, such as pores, cracks, lack of fusion, inclusions, delami nation, and geometrical deviations.” Financial support for this research came from Majestic Rock Resources Pte. Ltd.. Our news editors obtained a quote from the research from Nanyang Technological U niversity, “These defects are undesirable and have negative effects. To optimize the performance of the as-built components, and to reduce the potential defects , a feasible solution is to conduct in-process sensing and provide feedback to t he control system. This article aims to give a comprehensive review of recent pr ogress on sensing technologies, such as optical, acoustic, vision, thermal, and multiple signals-based sensing technologies, and the application of machine lear ning to enhance the ability to extract the needed feedback from the inprocess m onitoring raw data. Effective monitoring of different types of defects typically requires different sensing technologies, focus points, and attentions. Multi- s ensor-based sensing systems may thus be needed to provide full-scale information . These necessities include the need for in-time data fusion and more complex da ta processing.”
SingaporeSingaporeAsiaCyborgsEme rging TechnologiesMachine LearningTechnologyNanyang Technological Universi ty
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