Research Based on sample size calculation and Monte Carlo simulation

Yao Wang

doi:10.54097/vbzzs425

Authors

Yao Wang

DOI:

https://doi.org/10.54097/vbzzs425

Keywords:

Sample size calculation, Monte Carlo simulation, Optimal detection.

Abstract

This paper focuses on the inspection of parts and components and the quality control of finished products in the production process of electronic products, and focuses on how to reduce the number of inspections as much as possible to ensure that the defective rate of parts and components meets the requirements of enterprises and how to find the optimal production strategy in different situations. We used the sample size calculation formula and Monte Carlo simulation method to determine the minimum sample size under the reliability of 95% and 90% respectively, and simulated the detection process under the condition of different defective rates for enterprises to make reasonable decisions according to different situations. In addition, we designed the optimal detection and decision strategy for different production stages. The research results provide a basis for enterprises to formulate reasonable quality inspection and control programs, and have significant practical value.

Downloads

Download data is not yet available.

References

[1] Fang X, Luo Q, Zhou B, et al. Research progress of automated visual surface defect detection for industrial metal planar materials[J]. Sensors, 2020, 20(18): 5136.

[2] Julious S A. Sample sizes for clinical trials[M]. chapman and hall/CRC, 2023.

[3] Luo C, Keshtegar B, Zhu S P, et al. Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 388: 114218.

[4] Cao P, Shen X, Duan G, et al. Quality-integrated diagnostic platform for aerospace complex product assembly processes[J]. Computers & Industrial Engineering, 2024, 189: 109796.

[5] Harris R S. Inspection models for automotive parts remanufacture[J]. 2020.

[6] Rivera-Gómez H, Gharbi A, Kenné J P, et al. Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system[J]. Computers & industrial engineering, 2020, 140: 106273.

[7] Menzies T, Milton Z, Turhan B, et al. Defect prediction from static code features: current results, limitations, new approaches[J]. Automated Software Engineering, 2010, 17: 375-407.

[8] Stehman S V, Foody G M. Key issues in rigorous accuracy assessment of land cover products[J]. Remote Sensing of Environment, 2019, 231: 111199.

[9] Wang L, **ao T, Liu S, et al. Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation[J]. Gondwana Research, 2023, 123: 27-40.

[10] Pellissetti M F, Schuëller G I. Scalable uncertainty and reliability analysis by integration of advanced Monte Carlo simulation and generic finite element solvers[J]. Computers & Structures, 2009, 87(13-14): 930-947.