Process Failure Mode And Effects Analysis Research Articles

Optimizing Footwear Manufacturing Through Defect Analysis and Spare Parts Optimization

This study investigates the causes of defects in the production line of inlay soles in footwear manufacturing, focusing on the impact of malfunctioning machine spare parts. Using Failure Mode and Effects Analysis (FMEA), critical machines—splitting, cutting, and moulding—are identified, along with their associated spare parts influencing defect occurrence. Process FMEA (PFMEA) prioritizes actions based on discussions with maintenance managers and quality staff. Results highlight top spare parts affecting defects, such as Fertinak, grinding stone, and V-belt for the splitting machine; hydraulic motor, hydraulic seal, and hydraulic filter for the cutting machine; and Solid State Relay, Thermocouple, and Heater for the moulding machine. These findings underscore the importance of maintaining machine performance and addressing spare part malfunctions to enhance product quality and operational efficiency in footwear manufacturing, providing actionable insights for industry practitioners. Hightligh: Critical Machines Identified: Splitting, cutting, and moulding machines analyzed for defect occurrence. Spare Parts Influence: Fertinak, Solid State Relay impact defect occurrence. Practical Industry Insights: Strategies for improving product quality and efficiency highlighted. Keywords: Defect analysis, Footwear manufacturing, Spare parts, Failure Mode and Effects Analysis (FMEA), Product quality.

New Generation FMEA Method in Automotive Industry: An Application

It is aimed to gain study substantiality by making an application for risk analysis study in the automotive industry. To bring to the literature the current point of risk analyzes used in FMEA methods applied in the automotive industry. At the same time, it is aimed to improve a problem experienced in the process of an operating in the automotive supplier industry with the risk analysis method. Failure Mode and Effects Analysis (FMEA) from risk analysis which is strong method to identifying and managing of the risks of potential failures occurring from product, process, service and system. Three methods were used in FMEA risk analysis in the study. AIAG FMEA Handbook fourth edition 2008, VDA-4 standard and new generation AIAG&VDA FMEA handbook, 2019. In this study, a Process FMEA study was prepared for the surface coating preparation process used by an automotive supplier industry. According to the results of the risk analysis, corrective actions were defined and prepared after the P-FMEA study. Afterwards, the necessary investments were made by the company management and the risks were minimized. With this investment, an innovative and technological application was integrated by ensuring the transition of the process from the human factor to automation. In addition, differences and current information about FMEA evaluation methods used in the automotive industry have been brought to the literature

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

P-Diagram을 활용한 AIAG-VDA 통합 공정 FMEA의 효과적 실행 방법

Purpose: This paper proposes a method to prevent the omission of possible failure modes or the selection of inappropriate failure modes during AIAG-VDA PFMEA implementation.BRMethods: During the development of AIAG-VDA-integrated PFMEA, we established a method for the effective determination of failure modes and causes through the creation and application of P-diagrams. To accurately identify potential failure modes, all processes illustrated in the flow diagram of the process were decomposed, and the process was decomposed into work units. In addition, the work performance factor 4M or 5M necessary to perform the decomposed work units was determined. Furthermore, the work interruption factor that interfered with each of work performance element was determined. Potential failure modes were identified based on the last found disturbance factors. Finally, a method of deriving a potential failure mode was established by applying the proposed procedure to the assembly of LCD and equipment.BRResults: By applying the proposed process FMEA to the LCD and fixture assembly process, we confirmed the effects of potential failure modes that could not be derived by conducting process FMEA without process disassembly.BRConclusion: The proposed process FMEA implemented in the actual field was effective in preventing potential failure modes.

A Modified FMEA Approach to Predict Job Shop Disturbance

Failure modes and effects analysis (FMEA) is a systematic approach that focuses on evaluating critical disturbances in a system. However, traditional FMEA has its own drawbacks, such as invalid computations and ambiguous priority definitions, which lead to many constraints in the application of complex production processes, especially in job shops with various resources. Therefore, this paper proposes an analytic disturbance prediction method for job shop with multiple resources and multiple evaluation indexes, which combines the vector computing techniques, FMEA, and fuzzy analytic hierarchy process (FAHP). In contrast to other work, this paper focuses on the establishment of FMEA mathematical model to improve the readability of multi-resource disturbance risk results. To this end, the projection of the disturbance vector is visualized to reduce repeated calculation results, triangles and trapezoids are used as membership functions to improve the accuracy of weight, and the differentiation index is used to reduce the ambiguity of priorities. The proposed method can effectively discover the critical disturbances and enable managers to undertake more assertive decisions.

Evaluating Risk of FLASH Experiments on a Clinical LINAC with Failure Mode and Effects Analysis

<h3>Purpose/Objective(s)</h3> Use of clinical linear accelerators in ultra-high dose rate (UHDR) mode can provide a conduit for wider access to UHDR FLASH effects, sparing normal tissue, but care needs to be taken in the use of such systems to ensure errors are avoided. A Failure Modes and Effects Analysis (FMEA) was conducted with a team involved in converting a LINAC between clinical use and UHDR experimental mode for more than one year, following the proposed methods of AAPM (The American Association of Physicists in Medicine) Task Group 100 report. <h3>Materials/Methods</h3> A team of 9 professionals with extensive experience outlined the process map and workflow for analysis, and developed fault trees for potential errors, as well as failure modes that would results. The team scored the categories of severity magnitude (S), occurrence likelihood (O), and detectability potential (D) on a scale of 1 to 10, so that a risk priority number (RPN=S*O*D) could be assessed for each potential failure. <h3>Results</h3> A total of 46 potential failure modes were identified, including 5 with RPN>100, all during converting and experimental use in UHDR mode and none during conversion to clinical mode. These failure modes involved 1) patient set up, 2) gating mechanisms in delivery, and 3) detector in the beam stop mechanism. Identified methods to mitigate errors included 1) use of a checklist post conversion, 2) use of robust radiation detectors, 3) automation of QA and beam consistency checks, and 4) implementation of surface guidance during beam delivery. <h3>Conclusion</h3> The FMEA process was considered critically important in this setting of a new use of a LINAC, and the expert team developed a higher level of confidence in the ability to safely move UHDR LINAC use towards expanded research access.

Post-COVID-19 era: An Enabler for the Implementation of Blended Learning in Compliance With the NEP 2020

Government of India has cleared the New Education Policy (NEP) 2020 by bringing out many reforms in the education sector with a focus on education design, delivery, and assessment. This work is an attempt toward perceiving the post-COVID-19 era as an enabler in the engineering education process for the implementation of blended learning. Causal analysis through the Ishikawa diagram is brainstormed for enlisting all the potential causal factors for the inability to restore normalcy during the COVID-19 pandemic. Process Failure Modes and Effects Analysis (PFMEA) is adopted for restoring normalcy through prioritized corrective actions. Thus, NEP 2020 is considered for leveraging blended learning.

A moving average denoise operator and grey discrete production process quality prediction model

PurposeThe purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.Design/methodology/approachThe analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the “abnormal” value in the fault data. Based on the analysis of the impact, an “offset operator” is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.FindingsIt is discovered the “offset operator” can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the “noise” in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.Practical implicationsThe proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.Originality/valueThe offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.

Towards a Model-Based Systems Engineering Approach for Robotic Manufacturing Process Modelling with Automatic FMEA Generation

AbstractThe process of generating FMEA following document-centric approach is tedious and susceptible to human error. This paper presents preliminary methodology for robotic manufacturing process modelling in MBSE environment with a scope of automating multiple steps of the modelling process using ontology. This is followed by the reasoning towards automatic generation of process FMEA from the MBSE model. The proposed methodology allows to establish robust and self-synchronising links between process-relevant information, reduce the likelihood of human error, and scale down time expenses.

Analytical Investigation of Higher Education Quality Improvement by Using Six Sigma Approach

For over two decades in India, the technical industry's unique selling proposition (USP), with its wide infrastructure of technical institutes, has been capable of supplying best-in-class engineers. But recently, this claim does not hold water. According to the All India Council for Technical Education (AICTE), about 2.6 lakh mechanical engineers graduate every year in India. But the real count of industry ready mechanical engineers is approximately 7%. Hence, there is a need to assess the quality of engineering education in India to reduce the flaws in higher education. The purpose of the paper is to identify the various defects associated with technical education and eliminate those defects using various quality tools. This research is based on the six sigma technique, which is used to assess the quality criteria proposed by the National Board of Accreditation India (NBA). The proposed model is then applied to a typical tier II Indian engineering college located in south India. Six Sigma has two main methodologies: DMAIC and DFSS. The DMAIC (Define, Measure, Analyze, Improve, and Control) methodology is implemented for existing systems, whereas DFSS (Design for Six Sigma) is for assuring quality in new products. In this project, the conclusion is driven by the DMAIC methodology. Various statistical and non-statistical tools are employed in this research. The tools used are CTS-CTQ, SIPOC, Pareto chart, normal process capability analysis, one-way ANOVA, Ishikawa diagram, FMEA, RCBD, and SPC chart. All the statistical processes are done using Minitab analytical software. From the results, it is identified that the factors that have a risk priority number (RPN) greater than 300 need improvement, such as versatility in program curriculum, laboratories and workshops, and credibility among universities. Six Sigma can be achieved by developing proper strategies for mitigating these defects. Doi: 10.28991/HIJ-2022-03-02-07 Full Text: PDF

Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

Emerging in the past few decades, Industry 4.0 has wide effects over production lines with an increasing number of novel applications. These applications implement more than one of the tools of Industry 4.0. These tools include but are not limited to internet of things (IoT), big data, cloud computing, artificial intelligence, augmented reality, virtual reality, machine to machine communication (M2M), smart robot applications, etc. The aim of these efforts is mainly to acquire smarter manufacturing systems. With spreading Industry 4.0 methodologies, the role of sensors became more important to respond to new demands. One of the most important sensor types in this sense is the camera which now has wide variants in different forms. Applying machine vision algorithms via cameras grants optimization of many critical processes. In this perspective, the quality of both product and process should be handled as a key performance indicator that may be continuously enhanced for excellence. Machine vision algorithms may be adapted to check and manage quality in designated control points on the production lines. This study focuses on the control of the quality of rotary switches that are widely used in household appliances like ovens and washing machines. Rotary switches are critical components of an appliance since they direct the flow of electricity within the product. A failure in the functionality of this component directly causes the failure of the main product. Hence, the quality rate of rotary switches should be calculated in defective parts per million (dppm) units. An intense quality control procedure is required to achieve low dppm rates during production. As a real-life application, a camera system is integrated into the rotary switch production line on a selected point. Classification algorithms are developed on a cost-effective platform to perform visual quality checks of the rotary switches and qualify as “Ok” or “Defective”. The selected point ensures a high percent check of quality criteria while enabling repair of the defective parts with minor interventions. The aim of this control is to identify a defective rotary switch as soon as possible since most of the defects are irreversible once the rotary switch is totally produced or even some processes are completed. In this case, the entire product should be set apart for scrap. &#x0D; Another originality of our study is applying both Process Failure Mode and Effect Analysis (PFMEA) and Design Failure Mode and Effect Analysis (DFMEA) together. There is almost no referenced study in the literature. &#x0D; Benchmark comparisons are conducted upon completing the integration of the new system to the production line. As a result, enhancements in the quality, cost, and production speed parameters are achieved with a cost-effective smart system. Additional capabilities are added to the system, namely data analyzing online data feeding.

DSS-Based Process Control and FMEA Studies for Different Processes in the Field of Textile

Today, in many textile firms, high defect rates and inefficiency stand out due to the intensive labor in the production processes. For this reason, to be able to detect failure modes and effects, number of defects and defect rates fast and intervene in the process just in time is vital for textile firms. Accelerating feedback by producing accurate and effective quality reports and helping senior management's decision processes will reduce appraisal costs and delivery time. In this study, Failure Mode and Effect Analysis and Statistical Process Control techniques have been integrated with a Decision Support System for different processes of textile. Thanks to the proposed integrated system in the fast fashion/textile sector, defect and/or failures will be prevented or detected on time by determining the sources of error with an effective database and monitoring system. So, process capability will increase by preventing material, operations and human-induced scraps.

The integrated PFMEA approach with interval type-2 fuzzy sets and FBWM: A case study in the automotive industry

The change of market’s demand could be predictable to a certain degree at stable conditions but it may vary due to disruptive events. This research contributes by establishing the improvement of PFMEA (Process Failure Mode and Effect Analysis) analysis in the domain of assessment and determining severity risk factor, as well as identifying of failure priority. According to the researchers’ and practitioners’ suggestions, severity needs to be considered from the multiple aspects. The risk factor severity is considered from the aspects of product importance, quality, and cost. These aspects have different relative importance, which is determined in an exact way. The relative importance of the aspects, as well as the values of the risk factors, was described by linguistic expressions that are modeled by using the Interval type-2 trapezoidal fuzzy numbers (IT2TrFNs). IT2FBWM was used to determine weight vectors of risk factors. The priority of failures is determined according to the Action priority model which proposed by AIAG &amp; VDA (Automotive Industry Action Group and German Association of the Automotive Industry). The proposed methodology is tested in a Case study where the real-life data originated from a company from the Republic of Serbia that operates as a part of an automotive supply chain.

효과적인 공정 FMEA 실행 방법

Purpose: This paper proposes the methods and procedures to effectively identify and select potential failure modes to perform a process failure modes and effects analysis (FMEA).<BR>Methods: Detailed analyses of processes are generally required to prevent possible omissions and improper utilization of potential failure modes. In this study, a process decomposition is proposed for all procedures summarized in the process-flow chart to identify potential failure modes. To provide detailed identifications of the potential failure modes, a method of decomposing the process into work units is described. In addition, the 4M+1E elements, which are required for the decomposition, are found and listed. Each of the operational performance elements determined in this step was found to interfere with the work. Further, a method of identifying potential failure modes is proposed based on the determined work obstructions.<BR>Results: The process flow for an example screw-tightening procedure was decomposed into work units based on its process FMEA. A method to predict the potential failure modes for each disassembled work unit was also established based on the obstacles to work performance.<BR>Conclusion: By applying the proposed method to a screw-fastening process, it was confirmed that a process FMEA performed without process decomposition found unexpected potential failure modes.

A Structured Approach for Synchronising the Applications of Failure Mode and Effects Analysis

Abstract Failure Mode and Effects Analysis (FMEA) is a systematic approach for evaluating the potential failure modes in a system, and is mainly employed in three distinct tasks labelled: (1) Functional FMEA – evaluating those failures associated with product functional definition, (2) Design FMEA – analysing those failures associated with design definition and (3) Process FMEA – assessing potential failures in manufacturing and assembly processes. The literature review has shown limited works on the field of synchronising these different tasks into a working model. To address this gap, this research developed a framework for integrating these tasks of FMEAs, and then qualitatively validating the proposed framework. This research adopted a semi-structured questionnaire to collect experts’ feedback and validate the proposed framework. The t-test was then employed to evaluate the collected feedback. The findings highlight that the proposed framework is applicable and could facilitate the synchronisation of the different tasks of FMEA. This research presents a methodological approach for executing and synchronising FMEAs. Therefore, the proposed framework is practically relevant as an aid for the practitioners in catching the cascading failures and reducing the relevant impact.

Evaluation of failure modes and effect analysis for routine risk assessment of lung radiotherapy at a UK center.

PurposeExplore the feasibility of adopting failure modes and effects analysis (FMEA) for risk assessment of a high volume clinical service at a UK radiotherapy center. Compare hypothetical failure modes to locally reported incidents.MethodAn FMEA for a lung radiotherapy service was conducted at a hospital that treats ~ 350 lung cancer patients annually with radical radiotherapy. A multidisciplinary team of seven people was identified including a nominated facilitator. A process map was agreed and failure modes identified and scored independently, final failure modes and scores were then agreed at a face‐to‐face meeting. Risk stratification methods were explored and staff effort recorded. Radiation incidents related to lung radiotherapy reported locally in a 2‐year period were analyzed to determine their relation to the identified failure modes. The final FMEA was therefore a combination of prospective evaluation and retrospective analysis from an incident learning system.ResultsThirty‐six failure modes were identified for the pre‐existing clinical service. The top failure modes varied according to the ranking method chosen. The process required 30 h of combined staff time. Over the 2‐year period chosen, 38 voluntarily reported incidents were identified as relating to lung radiotherapy. Of these, 13 were not predicted by the identified failure modes, with six relating to delays in the process, three issues with appointment times, one communication error, two instances of a failure to image, and one technical fault deemed unpredictable by the manufacturer. Four additional failure modes were added to the FMEA following the incident analysis.ConclusionFMEA can be effectively applied to an established high volume service as a risk assessment method. Facilitation by an individual familiar with the FMEA process can reduce resource requirement. Prospective evaluation of risks should be combined with an incident reporting and learning system to produce a more comprehensive analysis of risk.

A Revised PFMEA Approach for Reliable Design of Assembly Activities

The purpose of this paper is to propose a novel process failure mode and effect analysis (PFMEA) approach for the reliable design of assembly activities to prevent product defects due to errors during the assembly of complex products. PFMEA is approached as an integrated method that, in addition to implementing recommended actions, supports the design of worksheets, equipment, and layout of the assembly lines of complex systems, early in the design phase of the product. As a result, the innovative design-job element sheets (D-JESs), which report work instructions to the operator for assembly cycles, are defined before the design of the production and assembly process. The modification of the PFMEA structure, the implementation of proper recommended actions, and the designs of D-JESs, equipment, and assembly layout, early in the design phase of the product, are the novel contributions of the paper. The integrated method assures to effectively design the assembly process directly during the product design to avoid errors that could promote dissatisfaction of the end-users. It is practical to use and does not require large investments, implementation of new technologies, or complex additional training. Its practical application is demonstrated using a case study concerning a manufacturer of train wagons via manual assembly lines.

Capability Resurrection of DC Sputtering Machine

Nanocoatings are gaining popularity owing to their widespread applications and the physical vapour deposition constitutes an effective method of deposition of coatings onto a suitable substrate. This work comprises of capability resurrection of a newly installed DC sputtering machine through troubleshooting, calibration, and establishment of process parameter mainly in terms of critical-to-performance (CTP) characteristic identified as the sputtering voltage. This work exercises the identification of potential causes for the breakdown of the sputtering machine through Ishikawa diagram and root cause is identified through the why-why analysis. Prioritization of corrective actions through process failure modes and effects analysis (PFMEA). Correct functioning of the DC sputtering machine after taking corrective action, is validated and confirmed through experimentation. This work shall serve as a reference to the maintenance and process personnel and guide them to perform the experiments related to DC sputtering in a laboratory environment.

Causal effect analysis of logistics processes risks in manufacturing industries using sequential multi-stage fuzzy cognitive map: a case study

ABSTRACT Any shortage and deviation in the implementation of logistics processes can lead to a poor performance and losing the market share of the manufacturer. To overcome this problem, the identification of existing risks in all logistics sub-processes, and taking corrective/preventive measures, is of great importance. In this study, a risk prioritization approach is presented based on a sequential multi-stage fuzzy cognitive map (SMFCM) and process failure mode and effects analysis (PFMEA) to prioritize logistics processes risks. This approach, in addition to considering a process-oriented view, prioritizes the logistics risks according to the amount of each risk’s impact on other risks through the internal and external-stage causal relationships as well as the values of risk factors. The score obtained from this approach can create enough distinction among the priorities of different risks in comparison with conventional risk priority number (RPN). Also, the results based on this score have a lower dependency on experts’ opinions by applying the Extended Delta Rule (EDR) learning algorithm. In fact, the intelligent nature of this approach enables decision-makers to identify critical risks and examine the status of the system at any time. The results of the implementation of the SMFCM-PFMEA approach in a manufacturing company active in the automotive spare parts industry confirm the superiority of this compared with the conventional score.

DP-FMEA: An innovative Failure Mode and Effects Analysis for distributed manufacturing processes

The Failure Mode and Effects Analysis (FMEA) is a powerful tool to design and maintain reliable systems (products, services or manufacturing processes), investigating their potential failure modes from the threefold perspective of severity, occurrence and detection. The Process FMEA, or more briefly P-FMEA, is a declination of the FMEA for manufacturing processes (or parts of them). Being progressively characterized by decentralized networks of flexible manufacturing facilities, the current scenario significantly hampers the implementation of the traditional P-FMEA, which requires the joint work of a group of experts formulating collective judgments. This paper revises the traditional P-FMEA approach and integrates it with the ZMII-technique – i.e. a recent aggregation technique based on the combination of the Thurstone’s Law of Comparative Judgment and the Generalized Least Squares method – allowing experts distributed through organizations to formulate their judgments individually. The revised approach – referred to as “distributed-Process FMEA” or more briefly dP-FMEA – allows to manage a number of experts, without requiring them to physically meet and formulate collective decisions, thus overcoming a relevant limitation of the traditional P-FMEA. The dP-FMEA approach also includes a relatively versatile response mode and overcomes several other limitations of the traditional approach, including but not limited to: (i) arbitrary formulation and aggregation of expert judgments, (ii) lack of consideration of the dispersion of these judgments, and (iii) lack of estimation of the uncertainty of results. The description is supported by a real-life application example concerning a plastic injection-molding process.