An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions
Received: 27 September 2023 | Revised: 16 October 2023 | Accepted: 20 October 2023 | Online: 5 December 2023
Corresponding author: Malinka Ivanova
Abstract
Quality control during the manufacturing process is an important factor in delivering products in electronics according to planned characteristics and properties. It concerns the capability of the chosen measurement system to perform precise and reliable measurement trials, which is evaluated mainly through the utilization of measurement system analysis. In order to reduce time effort and to partially automate these operations, a methodology for the prediction of a part of the dataset through applying the Neural Net algorithm is proposed in this paper in two scenarios: (1) when two metrology experts are involved in the measurement in three trials and the data of a third specialist are predicted and (2) when three metrology specialists collect data in two trials and the data of the third trial are predicted. The developed predictive models in these two scenarios are assessed and they are characterized by high accuracy. Gage repeatability and reproducibility analysis are used to evaluate the measurement systems based on original and partially artificial datasets as the comparative results outline the suitability of the proposed approach, due to the proximity of the obtained values.
Keywords:
machine learning, artificial dataset, measurement system, measurement system analysis, Gage Repeatability and Reproducibility, electronics manufacturing, automationDownloads
References
I. A. Memon, Q. B. Jamali, A. S. Jamali, M. K. Abbasi, N. A. Jamali, and Z. H. Jamali, "Defect Reduction with the Use of Seven Quality Control Tools for Productivity Improvement at an Automobile Company," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 4044–4047, Apr. 2019.
A. B. E. Aichouni, H. Abdullah, and F. Ramlie, "A Scientific Approach of using the DMAIC Methodology to Investigate the Effect of Cutting Tool Life on Product Quality and Process Economics: A Case Study of a Saudi Manufacturing Plant," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6799–6805, Feb. 2021.
M. Alquraish, "Modeling and Simulation of Manufacturing Processes and Systems: Overview of Tools, Challenges, and Future Opportunities," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9779–9786, Dec. 2022.
S. Kamil and B. Pawel, "Measurement system analysis for one-sided tolerance," ITM Web of Conferences, vol. 15, 2017, Art. no. 05011.
M. Diering, A. Hamrol, and A. Kujawińska, "Measurement System Analysis Combined with Shewhart’s Approach," Key Engineering Materials, vol. 637, pp. 7–11, 2015.
R. Dastoorian and L. Wells, "Gauge capability studies for high-density data: SPC Phase 0," Procedia Manufacturing, vol. 48, pp. 105–113, Jan. 2020.
RapidMiner Studio Manual. RapidMiner, 2014.
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Copyright (c) 2023 Malinka Ivanova, Valentin Tsenev, Vania Mikova
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