Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel
Source: The International Journal of Advanced Manufacturing Technology, Volume 37, Numbers 7-8, June 2008 , pp. 641-648(8)
Abstract:The current article presents an investigation into predicting tool wear in hard machining D2 AISI steel using neural networks. An experimental investigation was carried out using ceramic cutting tools, composed approximately of Al2O3 (70%) and TiC (30%), on cold work tool steel D2 (AISI) heat treated to a hardness of 60 HRC. Two models were adjusted to predict tool wear for different values of cutting speed, feed and time, one of them based on statistical regression, and the other based on a multilayer perceptron neural network. Parameters of the design and the training process, for the neural network, have been optimised using the Taguchi method. Outcomes from the two models were analysed and compared. The neural network model has shown better capability to make accurate predictions of tool wear under the conditions studied.
Document Type: Research Article
Affiliations: 1: Department of Mechanical Engineering, University of Matanzas, Autopista a Varadero km 31/2, Matanzas, 44740, Cuba 2: Department of Mechanical Engineering, University of Aveiro Campus Santiago, 3810-193, Aveiro, Portugal 3: Department of Mechanical Engineering, University of Aveiro Campus Santiago, 3810-193, Aveiro, Portugal, Email: email@example.com
Publication date: June 1, 2008