首页|Study Findings on Machine Learning Reported by Researchers at University of Stra thclyde (Machine-learning-enhanced femtosecond-laser machining: Towards an effic ient and deterministic process control)
Study Findings on Machine Learning Reported by Researchers at University of Stra thclyde (Machine-learning-enhanced femtosecond-laser machining: Towards an effic ient and deterministic process control)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of the University of Stra thclyde by NewsRx editors, research stated, “Femtosecond laser nanomachining rep resents a frontier in precision manufacturing, excelling in micro-and nanopatter ning across diverse materials.” Our news reporters obtained a quote from the research from University of Strathc lyde: “However, its wider adoption is hindered by unintended surface damage or m odifications stemming from complex nonlinear laser-material interactions. Moreo ver, traditional effective process optimisation effort to mitigate these issues typically necessitate extensive and time-consuming trial-and-error testing. In t his scenario, machine learning (ML) has emerged as a powerful solution to addres s these challenges. This paper provides an overview of ML’s contributions to mak ing femtosecond laser machining a more deterministic and efficient technique. Le veraging data from laser parameters and both in-situ and ex-situ imaging of proc essing outcomes, ML techniques-spanning supervised learning, unsupervised learni ng, and reinforcement learning-can significantly enhance process monitoring, pro cess modeling and prediction, parameter optimisation, and autonomous beam path p lanning.”
University of StrathclydeCyborgsEmer ging TechnologiesMachine Learning
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