A Methodological Approach to Assembly Time Standard Estimation Based on Incomplete Characteristics of the Production Process and Using Small Databases
1
Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala
Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
2
Department of Machine Tools and Mechanical Technologies, Wroclaw University of Science and Technology, Poland
Submission date: 2024-04-22
Final revision date: 2024-06-20
Acceptance date: 2024-06-21
Online publication date: 2024-08-27
Corresponding author
Izabela Kutschenreiter-Praszkiewicz
Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
KEYWORDS
TOPICS
ABSTRACT
The problem solved in this article concerns assembly planning, which is time-consuming, but crucial in the development of mechanical products. At the product design stage there is no complete information about the manufacturing process, so it is necessary to develop an approach to help process the uncertain and incomplete information. In order to compare different product variants, the assembly time standard has to be estimated on the basis of the incomplete product and production process characteristics. This paper presents a method for estimating the assembly time standard using time classes, decision tree and evidence theory.
REFERENCES (27)
1.
BONINO B., RAFFAELI R., MONTI M., GIANNINI F., 2021, A Heuristic Approach to Detect CAD Assembly Clusters, Procedia CIRP, 100, 463–468, doi.org/10.1016/j.procir.2021.05.105.
2.
EZPELETA I., PUJANA U., ISASA I., AYERBE J., JUSTEL D., 2021, New Design for Assembly (Dfa) Methodology for Large and Heavy Parts Assembled on Site, Procedia CIRP, 100, 145–150, doi.org/10.1016/ j.procir.2021.05.078.
3.
GROOVER M., 2016, Work Systems, the Methods, Measurements and Management of Work, Pearson.
4.
CHEN J., ZHOU D., KANG L., MA L., GE H., 2020, A Maintenance Time Estimation Method Based on Virtual Simulation and Improved Modular Arrangement of Predetermined Time Standards, International Journal of Industrial Ergonomics, 80,103042, doi.org/10.1016/j.ergon.2020.103042.
5.
KIM J., GOLABCHI A., HAN S., LEE D., 2021, Manual Operation Simulation Using Motion-Time Analysis Toward Labour Productivity Estimation: a Case Study of Concrete Pouring Operations, Automation in Construction, 126, 103669, doi.org/10.1016/j.autcon.2021.103669.
6.
BENTAHA M.L., BATTAIA O., DOLGUI A., HU S.J., 2014, Dealing With Uncertainty in Disassembly Line Design, CIRP Annals – Manufacturing Technology, 63, 21–24, doi.org/10.1016/j.cirp.2014.03.004.
7.
SOMALA S., KARTHIKEYAN K., MANGALATHU S., 2021, Time Period Estimation of Masonry Infilled RC Frames Using Machine Learning Techniques, Structures, 34, 1560–1566, doi.org/10.1016/j.istruc.2021.08.088.
8.
ZHANG J., LIU H., CHANG Q., WANG L., GAO R.X., 2020, Recurrent Neural Network for Motion Trajectory Prediction in Human Robot, Collaborative Assembly, CIRP Annals – Manufacturing Technology, 69, 9–12, doi.org/10.1016/j.cirp.2020.04.077.
9.
KWON N., AHN Y., SON B., MOON H., 2021, Developing a Machine Learning-Based Building Repair Time Estimation Model Considering Weight Assigning Methods, Journal of Building Engineering, 43, 102627, doi.org/10.1016/j.jobe.2021.102627.
10.
SALMI A., DAVID P., BLANCO E., SUMMERS J., 2015, Assembly Modelling and Time Estimating During the Early Phase of Assembly Systems Design, IFAC-Papers On Line, 48/3, 81–87, doi.org/10.1016/j.ifacol.2015.06.062.
11.
GENG X., LIANG Y., JIAO L., 2021, ARC-SL: Association Rule-Based Classification with Soft Labels, Knowledge-Based Systems, 225, 107116, doi.org/10.1016/j.knosys.2021.107116.
12.
RAZAVI-FAR R., CHENG B., SAIF M., AHMADI M., 2020, Similarity-Learning Information-Fusion Schemes for Missing Data Imputation, Knowledge-Based Systems, 187, 104805, doi.org/10.1016/j.knosys.2019.06.013.
13.
MA L., DENOEUX T., 2021, Partial Classification in the Belief Function Framework, Knowledge-Based Systems, 214, 106742, doi.org/10.1016/j.knosys.2021.106742.
14.
GUO K., ZHANG L., 2021, Multi-Source Information Fusion for Safety Risk Assessment in Underground Tunnels, Knowledge-Based Systems, 227:107210, doi.org/10.1016/j.knosys.2021.107210.
15.
DENG Z., WANG J., 2020, A Novel Decision Probability Transformation Method Based on Belief Interval, Knowledge-Based Systems, 208, 106427, doi.org/10.1016/j.knosys.2020.106427.
16.
DU Y., ZHONG J., 2020, Group Inference Method of Attribution Theory Based on Dempster-Shafer Theory of Evidence, Knowledge-Based Systems, 188, 104985, doi.org/10.1016/j.knosys.2019.104985.
17.
YU H., CHEN L., YAO J., 2021, A Three-Way Density Peak Clustering Method Based on Evidence Theory, Knowledge-Based Systems, 211, 106532, doi.org/10.1016/j.knosys.2020.106532.
18.
STRAT T., 1990, Decision Analysis Using Belief Functions, International Journal of Approximate Reasoning, 4, 391–417.
19.
KRIST K., SIEVERS T., ONKEN A., KODJO Y., TRACHT K., 2020, Application of Derivative Products for Integrating Expert Knowledge Into Assembly Process Planning, Procedia CIRP, 88, 88–93, doi.org/10.1016/ j.procir.2020.05.016.
20.
PIMMINGER S., KURSCHL W., PANHOLZER L., NEUMAYR T., AUGSTEIN M., ALTMANN J., HEINZELREITER J., 2020, Assembly Task Analysis Using the General Assembly Task Model (GATM) on the Shop Floor, Procedia CIRP, 93, 1109–1114, doi.org/10.1016/j.procir.2020.04.007.
21.
GHADGE K., CHAKRABARTI A., 2020, A Framework for Knowledge Management in Manual Assembly Processes, Procedia CIRP, 88, 94–97, doi.org/10.1016/j.procir.2020.05.017.
22.
KUTSCHENREITER-PRASZKIEWICZ I., MATUSZNY M., 2022, Knowledge Base Development for Assembly Planning Using Evidence Theory, Journal of Machine Engineering, 22, 1–17, doi.org/10.36897/jme/149185.
23.
WOLK R., STRZELECKI T., 1993, Study of Methods and Standardization of Work, (in Polish), Warsaw University of Technology Publishing House, Warsaw.
24.
WITTEN I., FRANK E., HALL M., 2011, Data Mining Practical Machine Learning Tools and Techniques, Morgan Kaufmann.
25.
BEECHEY M., KYRIAKOPOULOS K., LAMBOTHARAN S., 2021, Evidential Classification and Feature Selection for Cyber-Threat Hunting, Knowledge-Based Systems 226 107120, doi.org/10.1016/j.knosys. 2021.107120.
26.
MAROPOULOS P.G., CEGLAREK D., 2010, Design Verification and Validation in Product Lifecycle, CIRP Annals – Manufacturing Technology, doi.org/10.1016/j.cirp.2010.05.005.
27.
EBRAHIMI ARAGHIZAD A, TEHRANIZADEH F., KILIC K., BUDAK E., 2023, Smart Tool-Related Faults Monitoring System Using Process Simulation-Based Machine Learning Algorithms, Journal of Machine Engineering, 23, 4, 18–32, https://doi.org/10.36897/jme/1....
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| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
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