Failure modes and effects analysis (FMEA) uses the product of three ranked factors to compute the risk priority number (RPN). Unfortunately, the RPN components have ambiguous definitions, and multiplying ranked values is not a valid operation. As a result, the RPN produces inconsistent risk priorities. In addition, FMEA uses distinct analyses for each system level and life cycle phase, making it difficult to consolidate interrelated risk information. The goal of scenario-based FMEA is to delineate and evaluate risk events more accurately. Probability and cost provide a consistent basis for risk analysis and decision making, and failure scenarios provide continuity across system levels and life cycle phases.

This paper presents the use of Advanced Failure Modes and Effects Analysis (AFMEA) as a methodology to analyze manufacturing process reliability. The proposed method applies to early process design and seeks to improve product quality, process efficiency, and time to market. The method uses behavior modeling to relate process functions, performance state variables, and physical entities. The model can be used to define process failures explicitly and provides a framework for assessing causes and effects. An example of a precision turning operation illustrates how AFMEA applies to the analysis of manufacturing processes. A pilot analysis of an ultrasonic inspection process revealed that AFMEA is comprehensive and adaptable to other processes. Ongoing work for AFMEA is developing deployment strategies for minimal time burden and links to embedded error proofing.

Failure Modes and Effects Analysis (FMEA) is a method to identify and prioritize potential failures of a product or process. The traditional FMEA uses three factors, Occurrence, Severity, and Detection, to determine the Risk Priority Number (RPN). This paper addresses two major problems with the conventional FMEA approach: 1) The Detection index does not accurately measure contribution to risk, and 2) The RPN is an inconsistent risk-prioritization technique. The authors recommend two deployment strategies to address these shortcomings: 1) Organize the FMEA around failure scenarios rather than failure modes, and 2) Evaluate risk using probability and cost. The proposed approach uses consistent and meaningful risk evaluation criteria to facilitate life cost-based decisions.

This paper presents a systematic method applicable at the early stages of design to enhance life-cycle quality of ownership: Advanced Failure Modes and Effect Analysis (AFMEA). The proposed method uses behavior modeling to simulate device operations and helps identify failure and customer dissatisfaction modes beyond component failures. The behavior model reasons about conditions that cause departures from normal operation and provides a framework for analyzing the consequences of failures. The paper shows how Advanced FMEA applies readily to the early stages of design and captures failure modes normally missed by conventional FMEA. The result is a systematic method capable of capturing a wider range of failure modes and effects early in the design cycle. An automatic ice maker from a domestic refrigerator serves as an illustrative example.

Manual assembly errors are a significant source of manufacturing defects. Therefore, an efficient method is needed to identify and alleviate potential assembly defects. Process Failure Modes and Effects Analysis (Process FMEA) is one technique used to anticipate, evaluate, and resolve potential manufacturing and assembly issues. However, performing FMEA is widely considered to be tedious and time-consuming, and not always worth the effort. In response, many researchers have attempted to automate FMEA using Artificial Intelligence (AI) to make it less arduous. Unfortunately, automated techniques are limited to systems with predictable behaviors (e.g., electronic circuits) and are rarely used on unpredictable processes such as manual assembly. “Assembly FMEA” is a novel technique developed specifically to identify manual assembly errors. Assembly defect levels are related to assembly complexity, which can be estimated using “Design for Assembly” (DFA) time penalties. Hence, Assembly FMEA uses a series of DFA-related questions to elicit potential assembly defects. The questions help to focus, standardize, and expedite the FMEA process. Assembly FMEA quickly identifies a large number of assembly errors with significantly less effort than conventional FMEA. This paper describes the Assembly FMEA procedure and illustrates its use on a conceptual design and on an existing product.