P-Diagram Driven Robust PFMEA Development

Abstract

In this paper, we introduce a systematic approach for developing Process Failure Mode Effects Analysis (PFMEA) by incorporating p-diagrams. P-diagrams have proven to be successful in the development of Design Failure Mode Effects Analysis (DFMEA). PFMEA is a crucial tool for minimizing process risks, and we believe that leveraging p-diagrams can greatly enhance its effectiveness. There is a noticeable gap in the literature regarding the application of p-diagrams in PFMEA development. To address this gap, our proposed approach aims to provide a comprehensive guide for developing p-diagram driven robust PFMEA development. By utilizing p-diagrams, we aim to improve the accuracy and robustness of the PFMEA process. We start by analyzing the process through the creation of a process flow diagram. This diagram provides a visual representation of the interconnected steps involved in the process. Following that, we develop p-diagrams for each focus item (any potentially critical step or cell in the manufacturing process) we have identified as a potential risk using Change Point Analysis or any other risk assessment methodology. These p-diagrams help us gain a better understanding of the relationships between manufacturing parameters, manufacturing failure modes, noise factors or potential causes(6M), and required functions of the focus area. This understanding is essential for a structured transition into Process Failure Mode and Effects Analysis (PFMEA). To further enhance our analysis, we leverage the 6M (Ishikawa) approach as a proactive input into p-diagram as potential causes that drive any potential manufacturing failure modes. This categorizes potential sources of noise factors into six main categories (Man, Machine, Materials, Methods, Measurement, Mother Nature), allowing us to identify and address various noise factors that may impact the process requirements. Subsequently, the PFMEA team proceeds to complete the remaining columns, such as failure consequences, and evaluates the Risk Priority Numbers (RPNs). This systematic process thoroughly examines all functions and potential noise factors that could lead to deviations from ideal functionality. By doing so, it enhances efficiency and reduces the likelihood of overlooking important causes of failures.