Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. 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 and implemented. The state-of-the-art in application of various optimisation and artificial 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 researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical 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. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP.

中文翻译：

---

基于人工智能的总体加工性能优化：综述

近年来，先进的建模和优化技术已被广泛应用，以实现智能制造和制造过程的数字化。在此背景下，人工智能在加工中的集成为提高操作效率和生产部件的质量提供了绝佳的机会。机器学习方法已被应用于优化加工过程特性、刀具磨损或产品质量等各种单独目标。然而，加工工艺的整体改进需要多目标优化方法，但很少考虑和实施。本文介绍了在加工操作（包括铣削、车削、钻孔和磨削）中应用各种优化和人工智能方法进行流程优化的最新技术。人们发现铣削过程和深度学习分别是研究最广泛的操作和实施的机器学习技术。事实证明，表面粗糙度是最关键的质量指标。对人工智能应用中的不同优化目标进行了详细阐述和分析，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，本文确定并讨论了成功提高总体加工性能的关键因素。在 CIRP 发起的 IMPACT 项目框架内对人工智能方法进行了研究和分析。