We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Assembly Man‑Hour Estimation Model Based on GA‑SVM for Multi‑specification and Small‑Batch Production.
- Authors
XU Ji; ZHANG Liping; LI Lu; XU Feng; GUO Hun; CHAO Haitao; ZUO Dunwen
- Abstract
It is necessary to evaluate man-hour (MH) before receiving the order to guide the quotation and forecast the delivery date for a manufacturing contractor. As an important part of assembled MH, it has important practical significance. Aiming at the characteristics of multi-specification and small-batch production, an assembly MH estimation model based on support vector machine (SVM) is proposed. Apart from single component attributes, assembly process, and historical MH data, we also consider the average of shortest path length (ASPL). which quantifies the complexity of an assembly, as influencing factors of assembly MH. Furthermore, the auto calculating methods of these factors based on 3D models with Creo JLink are proposed. Through the comparison of several algorithms, SVM is chosen as the optimal algorithm for assembly MH modeling. Genetic algorithm (GA) is used to avoid the local solution and accelerate convergence when searching for the optimal parameters of SVM (c and g). Finally, the proposed GA-SVM model is trained and applied to predict the assembly MH of the bionic leg for the radar device. Experimental results show that GA-SVM has higher prediction accuracy than other methods in this paper and the whole predicting process only takes about 3 min.
- Subjects
SUPPORT vector machines; MANUFACTURING industries; GENETIC algorithms; PIXELS; RADAR
- Publication
Transactions of Nanjing University of Aeronautics & Astronautics, 2023, Vol 40, Issue 4, p500
- ISSN
1005-1120
- Publication type
Article
- DOI
10.16356/j.1005‑1120.2023.04.010