Practical Usefulness of the Monte-Carlo Method in Processes of the Automotive Industry

• Published in: Land Forces Academy review Vol. 30; no. 1; pp. 141 - 147
• Main Author: Hugyi, Milán
• Format: Journal Article
• Language: English
• Published: Sibiu Sciendo 01.03.2025; Nicolae Balcescu
• Subjects: Alloy steels; Automobile industry; automotive industry; Chromium alloys; Chromium base alloys; Data analysis; Energy levels; Engine valves; Gasoline engines; Hardness; Hypotheses; Induction hardening; Manufacturing; Methods; Monte Carlo simulation; Monte-Carlo method; Optimization; Pandemics; Probability; process development; Quality management; Random sampling; Raw materials; Regression analysis; Regression models; Rejection rate; scrap-reduction; Statistical analysis; Valves; Variance analysis; Wear resistance
• ISSN: 2247-840X; 1582-6384; 2247-840X
• DOI: 10.2478/raft-2025-0014

Post-pandemic (COVID-19) and amidst raw material shortages (due to war of Russia-Ukraine), there’s a push to analyze stored data to enhance quality management through scientific methods, leading to better forecasting and reduced waste. Understanding manufacturing processes and machine operations is crucial for meaningful data analysis. The study reviews relevant literature, presents research results, and demonstrates the method’s practicality in manufacturing, concluding with a discussion of findings. In the Otto engines of subject of research, camshafts control valves to manage air for shafts of the intake and exhaust. The cams, made from wear-resistant chromium alloy steel, are crucial for valve operation. ANOVA (F-test) and regression analysis were used with Minitab to test hypotheses and analyze data, confirming significant differences between batches and the impact of energy levels on hardness depth. The Monte-Carlo method, involving random sampling, was also applied to model outcomes and probabilities. Regression analysis showed differences in data series averages, while the Monte-Carlo method helped determine optimal energy levels for induction hardening. The study found that adjusting energy levels ensure all products meet the required hardness depth, reducing the rejection rate significantly. This highlights the methods’ value in optimizing production processes and minimizing scraps.