Deep learning based cost estimation of circuit boards: a case study in the automotive industry

Abstract

Early cost estimation is a decisive value driver in the product development process in manufacturing industries. Machine learning offers new intelligent methods to support traditional cost calculation processes. While traditional research on intelligent cost estimation focuses on machine learning regression or classification models, we propose a new approach based on interlocking deep learning methods. In this paper we investigate the applicability of deep learning techniques, focusing on image recognition and deep learning regression as well as autoencoding to estimate product costs of circuit boards to be purchased. We create and evaluate deep learning models using real-world data from an original equipment manufacturer (OEM). Our findings suggest that deep learning models can streamline cost calculation and estimation processes while deep learning object recognition-based cost estimation outperforms autoencoding techniques. This research is designed to be transferable to other cost estimation projects.