Machine Learning Approach for Early Assembly Design Cost Estimation: A Case from Make-to-Order Manufacturing Industry

Abstract

Estimating production costs is a challenging process for the Make-To-Order (MTO) industry because of the product varieties, which leads to inaccurate cost estimation. The product engineering process requires accurate assembly cost estimation to take strategic decisions, specifically during the early design phase. Therefore, an intelligent machine learning-based approach, namely Multi-linear Regression, Random Forest, and Gradient Boosting, is proposed to estimate the assembly design cost. This estimation is done by identifying the assembly features of the 3D CAD model. The validation results showed that mate and assembly features, as well as the number of parts, are effective cost drives, while Random Forest outperformed other models. The proposed methodology is then implemented in a cost estimation program and applied in the MTO industry. The proposed estimation method deviated an average of 17.4% from the actual assembly design cost, considered acceptable during the early design phase. In conclusion, the proposed model and cost estimation program efficiently help the MTO industry predict assembly design costs.

Keywords

• 3d cad
• assembly design
• assembly features
• cost estimation
• machine learning