Quality optimisation for laser machining under transient conditions |
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| Affiliation: | 1. School of Mechanical and Manufacturing Engineering, The University of New South Wales, PO. Box 1, Kensington, NSW 2033, Australia;2. Department of Mechanical Engineering, Columbia University 220 S.W. Mudd Building, New York, NY 10027, USA;1. Department of Engineering Science, Faculty of Tabriz, Tabriz Branch, Technical and Vocational University (TVU), Tabriz, Iran;2. Department of Mechanical Engineering, Faculty of Engineering, Malayer University, Malayer, Iran;3. Laser Materials Processing Research Center, Malayer University, Malayer, Iran;4. Department of Mechanical Engineering, École de Technologie Supérieure, Canada 1100 Notre-Dame West, Montreal, QC, H3C 1K3, Canada;1. DISMI – Department of Sciences and Methods for Engineeering, University of Modena and Reggio Emilia, Italy;2. Institute of Scientific Instruments – ISI, Brno, Czech Republic;1. State Key Laboratory of Rare Earth Resources Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China;2. Key Laboratory of Inorganic Coating Materials, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 200050, China;3. College of Physics and Electronic Engineering, and Zhejiang Provincial Key Laboratory for Cutting Tools, Taizhou University, Taizhou 318000, China |
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| Abstract: | Quality improvements in laser machining have been achieved by a newly developed model-based optimisation strategy. The specific aims of such efforts are to assure machining quality right up to boundaries or pre-machined sections, which are inherent in intricate part geometry. Such boundaries frustrate heat-transfer and result in bulk heating of the workpiece. This in turn leads to a degradation of the machining quality. In order to achieve such optimisation, transient heat-transfer is modeled. Close inspection of the laser–workpiece interaction zone reveals that the machining front exhibits dynamic behaviour, and such mobility plays a significant role in temperature determination. Non-linear parameter adaption profiles are generated via the optimisation strategy in order to stabilise the machining front temperatures. Currently, trial-and-error based experimentation is needed in order to improve machining quality in such regions. Thus model-based optimisation has the added benefit of reducing this step whilst leading to an optimal solution. Experimental results are presented and it is demonstrated that such process manipulation can lead to significant quality improvements. |
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