Characterisation of the workpiece dilatation phenomenon during machining using the neural network method. Application to NC turning
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Dilatation of workpieces during machining is a major source of defects. With the current trend for re-treatment of cutting and cleaning fluids becoming compulsory, lubrication by a stream of oil and dry machining are becoming more widely used in aluminium alloy machining. Indeed, this makes it easier to recycle chippings and greatly simplifies the cleaning and grease removal phases for workpieces that are compulsory before any finishing surface treatment. However, the workpiece’s deformation during machining must be taken into account. This is especially true for NC turning of machining diameters with very tight tolerances.
Here we propose a method based on the use of a neural network intended to model changes in the workpiece’s dimensions to correct tool paths. This study covered machining of workpieces made of 2017 T4 aluminium alloy during the turning phase. We first conducted preliminary tests on a workpiece to highlight workpiece dilatation. We then implemented a neural network to predict this deformation to be able to compensate for it. The results of a first test campaign gave us knowledge of the network then a second test campaign was used to validate that network. To finish off, we machined a test workpiece in order to test and analyse network performance.
KeywordsCompensation Neural network Turning Workpiece dilatation
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- 2.Ramesh R, Mannan MA, Poo AN (2000) Compensation in machine tools–a review. Part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40(9):1235–1256Google Scholar
- 3.Ko TJ, Gim T-W, Ha J-Y (2003) Particular behavior of spindle thermal deformation by thermal bending. Int J Mach Tools Manuf 43(1):17–23Google Scholar
- 5.Sandvik-Coromant (1997) Techniques modernes d’usinage. Impr Tofters TryckeriGoogle Scholar
- 6.O’Sullivan D, Cotterell M (2001) Temperature measurement in single point turning. J Mater Process Technol 118:301–308Google Scholar
- 8.Ramesh R, Mannan MA, Poo AN (2000) Compensation in machine tools - a review: Part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40(9):1235–1256Google Scholar
- 9.Segonds S, Landon Y, Cohen G, Lagarrigue P (2002) Virtual reality – application to the turning center. 18th Conference on manufacturing research. Leeds, UK, 27–31 May 2002Google Scholar
- 12.SNNS, Stuttgart Neural Network Simulator User manual version 4.2. http://www-ra.informatik.uni-tuebingen.de/SNNS/Google Scholar