摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者在英国谢菲尔德发布的新闻报道，研究称：“近年来，先进的建模和优化技术被广泛应用，以实现智能制造和制造过程的数字化。在这方面，人工智能在机械加工中的集成为提高操作效率和所生产部件的质量提供了极好的机会。”新闻记者从舍菲尔德大学的研究中获得了一句话：“机器学习方法已经被应用于优化加工过程中涉及过程特性、刀具磨损或产品质量的各种单独目标。然而，加工过程的总体改进需要多目标优化方法，本文介绍了铣削、车削、钻削、磨削等加工操作过程优化的各种优化方法和人工智能方法的应用现状,指出铣削加工过程和深度学习是目前研究最广泛、应用最广泛的机器学习技术。最后，本文对人工智能应用中的不同优化目标进行了阐述和分析，以强调需要一种涵盖加工操作所有关键方面的整体方法。因此，确定并讨论了成功提高整体加工性能的关键因素。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Artificial Intelligence are presented in a new report. According to news reporting originating in Sheffi eld, United Kingdom, by NewsRx journalists, research stated, “Advanced modelling and optimisation techniques have been widely used in recent years to enable int elligent manufacturing and digitalisation of manufacturing processes. In this co ntext, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components.” The news reporters obtained a quote from the research from the University of She ffield, “Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered an d implemented. The state-of-the-art in application of various optimisation and a rtificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researche d operation and implemented machine learning technique, respectively. The surfac e roughness turns out to be the most critical quality measure considered. The di fferent optimisation targets in artificial intelligence applications are elabora ted and analysed to highlight the need for a holistic approach that covers all c ritical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper.”
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