Abstract
Predicting defects is a challenge in many processing steps during manufacturing because there is a great number of variables involved in the process. In this paper, we take a machine learning perspective to choose the best model for defects prediction of sheet metal forming processes. An empirical study is presented with the objective to choose the best machine learning algorithm that will be able to perform accurately this task. For building the model, three distinct datasets were created using numerical simulation for three mild steel materials: mild steel, DH600, HSLA340. The numerical simulation was performed on the basis of sixteen input features representing characteristics of the materials. Moreover, two kinds of defects, springback and maximum thinning, each one is binary with 1 (defects) and 0 (non-defects) were considered in the simulator. The experimental setup consists of running MLP, CART, NB, RF and SVM algorithms using cross-validation for correctly choosing model parameters. The results were averaged in 30 runs and the standard deviations recorded. The initial conclusion is that the learning algorithm scores differently depending on the type of defect and conditions of the experiment. Although the preliminary results show good performance of the algorithms in simulated environment, a further study with real data will be addressed in future work.
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Acknowledgements
This work was funded by the Portuguese National Innovation Agency (ANI), for the support under the project SAFEFORMING - Sistema Inteligente de Preveno de Defeitos em Componentes Estampados a Frio, co-funded by FEDER, through the program Portugal-2020 (PT2020) and by POCI, with reference POCI-01-0247-FEDER-017762. Pedro Prates was supported by a grant for scientific research from the Portuguese Foundation for Science and Technology (ref. SFRH/BPD/101465/2014). All supports are gratefully acknowledged.
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Dib, M., Ribeiro, B., Prates, P. (2018). Model Prediction of Defects in Sheet Metal Forming Processes. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_14
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DOI: https://doi.org/10.1007/978-3-319-98204-5_14
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