Towards Sustainable and Intelligent Machining: Energy Footprint and Tool Condition Monitoring for Media-Assisted Processes
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Department of Mechanical Engineering, University of Bath, United Kingdom
Alborz Shokrani   

Department of Mechanical Engineering, University of Bath, United Kingdom
Submission date: 2023-04-07
Final revision date: 2023-05-22
Acceptance date: 2023-05-24
Online publication date: 2023-05-24
Reducing energy consumption is a necessity in achieving the goal of net-zero manufacturing. In this keynote paper, the energy footprint of machining Ti-6Al-4V using various cooling/lubrication methods is investigated taking the embodied energy in cutting tools and cutting fluids into account. The investigations show the significance of cutting tool’s embodied energy. New cooling/lubrication methods such as WS2-oil suspension can reduce the energy footprint of machining through extending tool life. Cutting tools are commonly replaced early before reaching their end of useful life to prevent damage to the workpiece, effectively wasting a portion of the embodied energy in cutting tools. A deep learning method is trained and tested to identify when a tool change is required based on sensor signals from a wireless sensory toolholder. The results indicated that the network is capable of correctly classifying the tool condition with over 90% accuracy.
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