Applications of Neural Networks in Engineer-to-order Environment

Machine tool selection and quotation costing have a low level of automation in today's engineer-to-order environments. The decision-making process is based on imprecise human judgment even if all final product characteristics are known. To improve precision and save time, we propose to apply artificial neural networks that are trained on data of already produced products. In particular, we address the selection of several grinding wheels to produce a milling cutter, which is a multiple-criteria decision, using multi-label classification. The quotation costing is a single output regression problem. In both tasks, our results on real-world data show high accuracy.