effective control of process defects occurrence number for pfmea car generation using manual and automated methods

Abstract

Failure Mode and Effect Analysis (FMEA) has been a cornerstone in risk assessment newlinefor decades, aiding organizations in comprehensively evaluating potential breakdowns. newlineHowever, the traditional FMEA approach has limitations, particularly in the subjectivity newlineassociated with assigning values for intensity, frequency, and recognition. This paper addresses newlinethis concern by proposing a more realistic method for determining the occurrence value, newlinethereby bridging the research gap between manual and systematic approaches to document newlinemaintenance. In this study, the risk priority number is computed using trends in body machining newlinedata, providing a quantitative foundation for risk assessment. The analysis delves into various newlinecharacteristics, including workstations, process inspection, quality checking, and sales returns. newlineQuantitative data is presented, revealing a correlation coefficient of 0.3, indicating a weak newlinerelationship between occurrences exceeding FMEA goal values and the number of Corrective newlineAction Reports generated. This empirical approach adds a valuable layer to risk assessment, newlinemoving beyond the reliance on expert opinions. Furthermore, the paper explores the drawbacks newlineof the conventional FMEA approach, emphasizing the challenges associated with quantifying newlinerisk using the Risk Priority Number (RPN). To address these limitations, a Life Expense newlinePFMEA is introduced, offering a quantitative assessment of risks based on cost and time factors newlinerelated to machinability issues. Inner diameter variations, collar thickness, and depth oversize newlineare examined, and quantitative data is presented to illustrate the associated risks. The proposed newlinetechnique is applied to a case study of a large-scale particle collider, demonstrating its efficacy newlinein projecting product lifecycle breakdown costs, quantifying risks, and strategically scheduling newlinepreventative maintenance activities. The study goes beyond theoretical discussions, presenting newlinetangible quantitative data that enhances the understanding of risk factors in t