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

被引:5
作者
Ma'ruf, Anas [1 ]
Nasution, Ali Akbar Ramadani [1 ]
Leuveano, Raden Achmad Chairdino [2 ]
机构
[1] Bandung Inst Technol, Fac Ind Technol, Jl Ganesha 10, Bandung 40132, Indonesia
[2] Univ Pembangunan Nasl Vet Yogyakarta, Fac Ind Engn, Dept Ind Engn, Jl Babarsari 2, Yogyakarta 55281, Indonesia
关键词
3D CAD; Assembly design; Assembly features; Cost estimation; Machine learning; CLASSIFICATION; REGRESSION;
D O I
10.14716/ijtech.v15i4.5675
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:1037 / 1047
页数:11
相关论文
共 35 条
[31]  
Rakhra M., 2021, MATER TODAY-PROC
[32]   Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study [J].
Verlinden, B. ;
Duflou, J. R. ;
Collin, P. ;
Cattrysse, D. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2008, 111 (02) :484-492
[33]   SS-XGBoost: A Machine Learning Framework for Predicting Newmark Sliding Displacements of Slopes [J].
Wang, Mao-Xin ;
Huang, Duruo ;
Wang, Gang ;
Li, Dian-Qing .
JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING, 2020, 146 (09)
[34]   Mass-customisation of cross-laminated timber wall systems at early design stages [J].
Yazdi, Alireza Jalali ;
Fini, Alireza Ahmadian Fard ;
Forsythe, Perry .
AUTOMATION IN CONSTRUCTION, 2021, 132
[35]  
Yi L, 2023, PROC CIRP, V120, P21, DOI 10.1016/j.procir.2023.08.005