首页|Findings in the Area of Artificial Intelligence Reported from University of Shef field (Ai-based Optimisation of Total Machining Performance: a Review)
Findings in the Area of Artificial Intelligence Reported from University of Shef field (Ai-based Optimisation of Total Machining Performance: a Review)
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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.”
SheffieldUnited KingdomEuropeArtif icial IntelligenceCyborgsEmerging TechnologiesMachine LearningUniversity of Sheffield
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