Journal
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
Volume 35, Issue 9, Pages 951-971Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0951192X.2022.2027020
Keywords
Multilayer perceptron; virtual reality; serrated cutters; energy optimization; ensembles
Categories
Funding
- Consejeria de Empleo e Industria of the Junta de Castilla y Leon - European Union FEDER funds [INVESTUN/18/0002, INVESTUN/21/0002]
- Spanish Centro para el Desarrollo Tecnologico e Industrial (CDTI) [IDI-20191008]
- Spanish Ministry of Science and Innovation [PID2020-119894GB-I00, PDC2021-121792-I00]
- Basque Government [Elkatek KK-2021/00003]
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This study proposes a new strategy that combines experimental tests, machine-learning modelling, and Virtual Reality visualization to overcome the limitations in selecting proper cutting tools and optimizing machining performance. It uses experimental tests and machine-learning techniques to find the best cutting tool geometric parameters and models. Virtual Reality technology provides intuitive graphics to assist engineering students and process engineers in optimizing cutting conditions.
The selection of a proper cutting tool in machining operations is a critical issue. Tool geometric parameters are essential for milling performance. However, the process engineer has very limited experience of the best parameter combination, due to the high cost of cutting tool tests. The same holds true for bachelor studies on machining processes. This study proposes a new strategy that combines experimental tests, machine-learning modelling and Virtual Reality visualization to overcome these limitations. First, tools with different geometric parameters are tested. Second, the experimental data are modeled with different machine-learning techniques (regression trees, multilayer perceptrons, bagging and random forest ensembles). An in-depth analysis of the influence of each input on model accuracy is performed to reduce experimental costs. The results show that the best model with no cutting-force inputs performed worse than the best model with all the inputs. Third, the most accurate model is used to build 3D graphs of special interest to engineering students as well as process engineers, for the optimization of power consumption under different cutting conditions. Finally, a Virtual Reality environment is presented to train engineering students in the study of the best tool design and cutting parameter optimization.
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