Machine Learning based Cost Prediction for Product Development in Mechanical Engineering

An accurate forecast of the final manufacturing costs in the early product development phase is a key element, especially in mechanical engineering. An accurate and reliable prediction allows, on the one hand, estimating the economic feasibility and, on the other hand, deriving necessary cost reductions. It is essential to evaluate and monitor compliance with the cost target initially and throughout the entire development process. Most cost estimation methods are primarily based on simple heuristic and statistical approaches that provide only limited accurate predictions in the early development phase. Sufficiently precise cost estimations, especially for customer-specific developments, are only possible in the late phase of the development process using finely parameterized models and cost rates at the component level. The supporting use of Machine Learning (ML) has not yet played a major role in cost estimation in mechanical engineering. Therefore, this article shows the general applicability of ML for predicting manufacturing costs in mechanical engineering and that even in the early phase of product development, precise predictions with little known information about the final product are possible. In addition, we present a generic feature set for ML-based cost prediction regarding machinery. Furthermore, the quality of different created ML models was evaluated based on real data of a mechanical engineering use case, and high accuracy in predicting manufacturing costs was demonstrated.