Design Failure Mode And Effects Analysis Research Articles

Cost consequence of failure in failure mode and effect analysis

PurposeThe purpose of this paper is to investigate the possibility of including the cost consequence of failure in the a priori risk assessment methodology known as failure mode and effect analysis (FMEA).Design/methodology/approachA model of the standard costs that are incurred when an electronic control module in an automotive application fails in service was developed. These costs were related to the Design FMEA ranking of the level of severity of the failure mode and the probability of its occurrence. Monte Carlo simulations were conducted to establish the average costs expected for each level of severity at each level of occurrence. The results were aggregated using fuzzy utility sets into a nine-point ordinal scale of cost consequence. The criterion validity of this scale was assessed with warranty cost data derived from a case study.FindingsIt was found that the model slightly underestimated the warranty costs that accrued, but the fit could be improved with adjustments dictated by actual usage conditions.Research limitations/implicationsCost data used in the simulations were derived from government and academic surveys, analyses, and estimates of the manufacturing cost structure; and nominal costs for various quality issues experienced by Tier 2 automotive electronics supplier. Specificity is lacking. The sample size and the type of the failure modes used to validate the model are constrained by the number and type of products which have had demonstrable performance concerns over the past three years, with cost data available to the authors. The power of the validation is limited. The validation is considered a screening assessment.Practical implicationsThis work relates the characterization of risk with its potential cost and develops a scaling instrument to allow the incorporation of cost consequence into an FMEA.Originality/valueA ranking scale was developed that related severity and occurrence rank scores to a cost consequence rank that keys to a cost of quality figure (given as percent of sales) that would accompany a realization of the failure mode.

An approach to support entries for a design failure modes and effects analysis

The design of a fixture device is very laborious, requiring consideration of many issues from series production, information on the raw part material, required precision surfaces, ensuring an appropriate orientation scheme, continue with requirements related device itself as number of parts, parts type, complexity, manageability, easy operation and maintenance. That is why realising a proper analyse regarding the possible failures associated to these issues is overwhelming. This paper’s aim is to propose an approach for collection of items necessary for starting a Design FMEA, using Functional Analysis, TRIZ functional modelling and axiomatic design approaches. The proposed approach – to extract items for a FMEA analyse – is applied to an example of an expanding mandrel regarding its internal components and theirs relations in order to accomplish the main requirement: the accurate locating and clamping of a part.

Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions

Recent developments in intelligent manufacturing have validated the use of probabilistic Boolean networks (PBN) to model failures in manufacturing processes and as part of a methodology for Design Failure Mode and Effects Analysis (DFMEA). This paper expands the application of PBNs in manufacturing processes by proposing the use of interventions in PBNs to model an ultrasound welding process in a preventive maintenance (PM) schedule, guiding the process to avoid failure and extend its useful work life. This bio-inspired, stochastic methodology uses PBNs with interventions to model manufacturing processes under a PM schedule and guides the evolution of the network, providing a new mechanism for the study and prediction of the future behavior of the system at the design phase, assessing future performance, and identifying areas to improve design reliability and system resilience. A process engineer designing manufacturing processes may use this methodology to create new or improve existing manufacturing processes, assessing risk associated with them, and providing insight into the possible states, operating modes, and failure modes that can occur. The engineer can also guide the process and avoid states that can result in failure, and design an appropriate PM schedule. The proposed method is applied to an ultrasound welding process. A PBN with interventions model was simulated and verified using model checking in PRISM, generating data required to conduct inferential statistical tests to compare the effects of probability of failures between the PBN and PBN with Interventions models. The obtained results demonstrate the validity of the proposed methodology.

Design Failure Modes and Effects Analysis (DFMEA) of a Human Powered Recumbent Vehicle

Design Failure Modes and Effects Analysis (DFMEA) of a Human Powered Recumbent Vehicle

English

Design FMEA is structured method of identifying potential failure modes and providing corrective actions before first production run occurs. This paper aims to provide probable causes of failure, levels of effects of failure and corrective actions to be taken in the design phase for Bevel- Planetary Vertical Roller Mill Gearbox. Keywords—DFMEA, Bevel-Planetary Gearbox, Vertical Roller Mill, Risk Priority Number I. In tro d uct io n 1) Vert ica l Ro ll er M il ls: Vertical roller mills (VRM) are well accepted as most effectual means for grinding raw material in cement and power generation industry. These mills are driven by heavy duty gearboxes with horizontal input shaft and vertical output shaft (i). Generally, Bevel-Helical or Bevel-Planetary gearboxes are used for VRM. The gearbox is integral component of VRM as its output flange is rigidly connected with the mill grinding table. Fig. 1 shows the general arrangement of VRM with gearbox located at its bottom.

DESIGN FAILURE MODES AND EFFECTS ANALYSIS (DFMEA) OF AN ALL-TERRAIN VEHICLE

Society of Automotive Engineers (SAE) organizes a student engineering design competition named Baja in which an All-terrain vehicle (ATV) is designed and fabricated by undergraduate engineering students. ATV is a vehicle that can run on a wide variety of terrains and travels on low-pressure tires with a seat straddled by the operator. SAE BAJA involves designing and fabrication of a modified and scaled down smaller version of ATV. Starting from initial design and analysis to actual fabrication of ATV, everything is done by the students. As in any engineering design, there is a constant need to design a safe and sustainable vehicle. This involves predicting and defining all failure modes in the initial design step itself. An effective method of doing this failure analysis is DFMEA (Design Failure Modes and Effects Analysis), which is an extension of popular Failure Modes and Effects Analysis (FMEA) technique and is done in the design stage. In this paper DFMEA technique is used to list out all modes of failure for various components of the ATV, its causes, effects and ways of preventing it. Risk Priority Number methodology of FMEA is used to find out the components which are more susceptible to failure and needs more attention than others.

A case study on FMEA-based quality improvement of packaging designs in the TFT-LCD industry

Nowadays enterprises are encountering diversified customer requirements, and using market information well is critical to product development. It is extremely important to conduct risk planning, prevent potential failures, and reduce uncertain factors by transforming customer requirements into exact development targets. This study is based on the method of quality function deployment (QFD) and failure modes and effects analysis (FMEA). The package design for semi-finished products of thin film transistor-liquid crystal display (TFT-LCD) modules in the photoelectricity industry was selected as a case study. First, customers conducted a questionnaire survey which helped determine the items they demanded. Second, QFD has been combined with FMEA to serve as the standard of evaluating the importance of customer requirements. Through mutual assessments and investigations by QFD and design failure mode and effects analysis (DFMEA), the key factors of a product design can be determined. The results of design of experiments verified that this method indeed effectively reduced defective products and customer complaints. The average number of customer complaints per year has been reduced from 9.5 to just only one after the introduction of the proposed method, which has been verified to assist in rapidly developing new products during the early design stage. This approach also reduces possible losses and enhances competitiveness.

The Pennsylvania State University's Advanced Vehicle Team: EcoCAR3 Year One Final Technical Report

This paper details the powertrain modeling and control strategies the Penn State Advanced Vehicle Team (PSUAVT) developed for submission to the EcoCAR 3 competition, an advanced vehicle design competition in which university teams compete to hybridize a stock 2016 Chevrolet Camaro. The team utilized model-in-the-loop studies using both public-domain and custom-built vehicle simulation packages to undergo an iterative powertrain model development process. This analysis lead to the development of a pre-transmission plug-in parallel hybrid electric vehicle that uniquely meets PSUAVT's goals. The design process was guided by vehicle technical specifications (VTS) based on consumer market research as well as the team's emphasis on producing a technically-feasible but performance-weighted hybrid architecture.Component models are presented that blend physics-based and map-based methods. A heuristic master vehicle controller (MVC) is described that splits the required driving torque between the motor and engine so engine brake-specific fuel consumption (BSFC) is minimized. Fault analysis of this controller is also presented based on design failure mode and effects analysis (DFMEA) performed in previous studies.

Failure mode and effects analysis of mechanical wave energy converters

A novel method of generating electric power from ocean wave energy using heave motion of a floating buoy is investigated. The proposed mechanical wave energy converter is examined for its efficient functionality through failure mode and effects analysis FMEA to study its reliability. Design FMEA studies are mandatory as the developed methodology is relatively new and still in the development stage. As the preliminary design of MEWC showed high risk priority numbers RPNs on few components, a revision is attempted to make the design as safe, simple and reliable. The FMEA tests deliberated in the current study illustrate more accurate estimate of field failure rates and reveal the possible failure modes/design weaknesses ahead of field deployments. The presented study shall be a good showcase for assessing the reliability of product development so that field trials can be justified for successful deployment.

Using Failure Mode and Effects Analysis to design a comfortable automotive driver seat

Given enough time and use, all designs will fail. There are no fail-free designs. This is especially true when it comes to automotive seating comfort where the characteristics and preferences of individual customers are many and varied. To address this problem, individuals charged with automotive seating comfort development have, traditionally, relied on iterative and, as a result, expensive build-test cycles. Cost pressures being placed on today's vehicle manufacturers have necessitated the search for more efficient alternatives. This contribution aims to fill this need by proposing the application of an analytical technique common to engineering circles (but new to seating comfort development), namely Design Failure Mode and Effects Analysis (DFMEA). An example is offered to describe how development teams can use this systematic and disciplined approach to highlight potential seating comfort failure modes, reduce their risk, and bring capable designs to life.

Development of an Integrated FMEA (i-FMEA) Using DAIREC Methodology for Automotive Manufacturing Company

Quality, reliability, and safety of products are the most important requirement of manufacturer in order to maintain their product competitiveness in market. Customers needs related to quality product in the market therefore drive the design and process activities to a high quantity of analysis. One possibility to reduce the number of activities is to integrate FMEA (i-FMEA) to merge Design FMEA (DFMEA) and Process (PFMEA) through DAIREC methodology. In this paper, a regression analysis between critical dimensional decided during design stage and key process factor in current process control is analyzed using MINITAB software. A judgment on a new ranking has been decided based on process performance indices (Ppk) for improvement lot.

Design Optimization of Thermal Management System for Electric VehicleUtilizing CFD Analysis, DFMEA and CES

Thermal management design for electric vehicles (EV) is very important in order to manage the thermal dissipated by operating components. This research aims at performing design optimization of a thermal management system for battery modules, controller and electric motor in EV. A combination of passive and active cooling systems is proposed where air cooled is used in battery modules, water cooling for controller and water jacket for electric motor. Design Failure Mode and Effect Analysis (DFMEA) method is deployed to identify the potential failure modes and causes so that improvements can be made for battery modules, controller and electric motor. The design for all of the components and the thermal management system were done with CATIA V5. The material selections process for the designs was based on the analysis using Cambridge Engineering Selector (CES EduPack). Final design was utilizing water cooled for electric motor and controller while using air cooled for battery modules. It was found that best material for electric motor and controller water jacket is with aluminum alloy 6060 while air cooled ducting using High Density Poly Ethylene (HDPE) and battery housing using Polycyclohexylenedimethylene Terephthalate (PCT). The thermal management system for battery modules is simulated using ANSYS CFD software. Results from simulation were validated with manual calculation and have shown good agreement based on the data collected at various vehicle speeds.

Application of FMEA to an automotive leaf spring manufacturing organization

PurposeThe purpose of this article is to report the application of failure mode and effect analysis (FMEA) to an automotive leaf spring manufacturing organization.Design/methodology/approachFMEA has been used as a decision‐making tool to prioritize the corrective actions so as to enhance product/system performance by reducing the failure rate. Both design and process FMEA documents have been developed by the systematic formation of team.FindingsThe study results indicated the actions that lead to improvement in design. There has been improvement in key decision factors apart from conventional factors. In addition, the quality of leaf springs produced also has been improved.Research limitations/implicationsConventional design and process FMEA approaches have been developed. In future, fuzzified FMEA can be used.Practical implicationsFMEA has been systematically deployed in a typical industrial scenario. The real improvements have been gained as a result of implementation.Originality/valueThe article presents the results of the case study conducted in an industrial scenario. The contributions of the study are original and valuable.

A Systematized FMEA Practice for Magnet Relays

The Design Failure Modes and Effects Analysis (DFMEA) are generally applied to risk management of New Product Development (NPD) through standardization of potential failure modes and effect-ranking of rating criterion with failure modes. Typical 1 to 10 of effect-ranking are widely weighed the priority of classification, that framing effects and status quo senses might cause decision trap happening thus. The FMEA follows considerable indexes which are including Severity, Occurrence and Detection, and need be associated with difference between every two failures individually. However, we suspect that a more systematic construction of the analysis by which failure modes belong is necessary in order to make intellectual progress in this area. Two ways of such differentiation and construction are improvable effect-ranking and systematized indexes; here we resolve for attributes of failures with classification, maturity and experiance of indexes according to an existing rule. In Severity model, the larger differentiation is achieved by separating indexes to the classification of the Law & Regulation, Function and Cosmetic. Occurrence model has its characteristic a reliable ranking indexwhich assists decisionmakers to manage their venture. This is the model most closely associate with product maturity by grouping indexes to the new, extend and series product. Detection model offers a special perspective on cost; here the connections concerned with phase occasion of the review, verification and validation. Such differentiations will be proposed and mapped with the Life Cycle Profile (LCP) to systematize FMEA. Meanwhile, a more reasonable Risk Priority Number (RPN) with the new weighting rule will be worked out for effect-ranking and management system will be integrated systematiclly

A novel efficient approach for DFMEA combining 2-tuple and the OWA operator

Design Failure Mode and Effect Analysis (DFMEA) is the application of the Failure Mode and Effects Analysis (FMEA) method specifically to product design. DFMEA is not only an important risk assessment technique but also a major task for enterprises in implementing production management. The purpose is to ensure that the product can achieve its designed functions under specific operating conditions. Most current DFMEA methods use the Risk Priority Number (RPN) value to evaluate the risk of failure. However, conventional RPN methodology has the serious problem of measurement scales and loses some valued information, which experts have to provide. In order to improve the method of RPN evaluation, this paper proposes a novel technique, combining 2-tuple and the Ordered Weighted Averaging (OWA) operator for prioritization of failures in a product DFMEA. A case of the Color Super Twisted Nematic (CSTN) that has been drawn from a midsized manufacturing factory is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed method with the other two listed approaches, it was found that the proposed approach can effectively solve the problem of measurement scales and has not lost any expert to provide the useful information. As a result, stability of the product and process can be assured.

Case Study in Cost-Based Risk Assessment for Selecting a Stream Restoration Design Method for a Channel Relocation Project

A low-risk, stream restoration design includes methods that validate design assumptions, incorporate uncertainty in the decision-making process during the project design phase, and reduce uncertainty by checking the final design. A two-step method of incorporating uncertainty and risk in stream restoration design has been developed as a combination of design failure modes and effects analysis (DFMEA) and risk quantification. As a first step, DFMEA is applied to identify risk in terms of ratings with respect to consequence of failure, the likelihood of occurrence of a failure, and the ability to detect a failure. Due to its evolutionary nature, the DFMEA can be revised to account for design modifications and relative ratings are reevaluated to examine reductions in uncertainty, and thereby, risk. The second step of the method is quantifying risk using initial and expected failure costs. The two-step, risk-based method is illustrated through application to a stream relocation project in Pennsylvania. Both sediment transport capacity and supply analysis and alluvial channel modeling design methods were shown to reduce uncertainty and risk by detecting design deficiencies that the initial design using incipient motion tests overlooked. However, the alluvial channel model method was favored due to its ability to simulate the combined effects of flow hydraulics, sediment transport, and river channel adjustment.

Design and development of modified service failure mode and effects analysis model

One of the prominent techniques in the field of Total Quality Management (TQM) is Failure Mode and Effects Analysis (FMEA). FMEA facilitates the recording of failures and analysing them to provide solutions for preventing their recurrence. During the early periods of FMEA evolution, there were two types namely Design FMEA and Process FMEA (PFMEA) in practice. In recent days, more types such as System FMEA, Service FMEA and Maintenance FMEA are being prescribed by the researchers. Meanwhile, a number of benefits of FMEA implementation have been reported. Yet, FMEA has not found its implementation in various fields. One among them is service field. This paper reports the examination of FMEA implementation in service industry. This direction of research led to the design of an improved model, named as 'Modified service FMEA'. Its implementation was examined in an Indian State Government owned passenger Transport Company. Despite certain practical hurdles, this exercise was successful in developing modified service FMEA table and pinpointing the seriousness of failures through the portrayal of Service Lost (SL) and Cost Lost (CL).

Robust Design Failure Mode and Effects Analysis in Designing for Six Sigma

Mode and Effects Analysis (FMEA) is a methodology that has been used in most industries for many years. Typically, the Design FMEAs are developed early in the product development cycle, in conjunction with the product risk analysis, product architecture and product requirements. Robust Design FMEA, the subject of this paper, is an enhancement to the currently in use Design FMEA by anticipating safety and reliability failure modes through the use of a Parameter Diagram (P-Diagram). The Design FMEAs have gone through a metamorphosis of sorts in the last decade, as a focus on severity and occurrence has replaced Risk Priority Number- (RPN-) driven activities. In large part this is due to the measurement of risk outcomes, resulting from associated RPNs being misinterpreted. As so many practitioners of the Design FMEA believe that the RPN is the most important outcome. However, the Design FMEA methodology must consider taking actions as soon as it is practical.

Challenges with the Performance of Failure Mode and Effects Analysis in Healthcare Organizations: An IV Medication Administration HFMEA™

The value of performing prospective analysis before technology implementation is well known to the chemical, nuclear and aviation industries. The performance of a failure mode and effects analysis (FMEA) is now required by healthcare organizations accredited by JCAHO as part of their patient safety standards. However, most healthcare organizations have little experience with applying human factors engineering techniques to process or technology evaluation and the procedure of how and when healthcare organizations should perform FMEA is not well described. This paper describes the method and challenges of performing a process and design FMEA to prepare for the implementation of a new intravenous infusion pump at a University Hospital. Recommendations are made for the performance of a process and design FMEA for new technology implementation in healthcare organizations.

Using FMEA models and ontologies to build diagnostic models

Product design and diagnosis are, today, worlds apart. Despite strong areas of overlap at the ontological level, traditional design process theory and practice does not recognize diagnosis as a part of the modeling process chain; neither do diagnosis knowledge engineering processes reference design modeling tasks as a source of knowledge acquisition. This paper presents the DAEDALUS knowledge engineering framework as a methodology for integrating design and diagnosis tasks, models, and modeling environments around a common Domain Ontology and Product Models Library. The approach organizes domain knowledge around the execution of a set of tasks in an enterprise product engineering task workflow. Each task employs a Task Application which uses a customized subset of the Domain Ontology—the Task Ontology—to construct a graphical Product Model. The Ontology is used to populate the models with relevant concepts (variables) and relations (relationships), thus serving as a concept dictionary-style mechanism for knowledge sharing and reuse across the different Task Applications. For inferencing, each task employs a local Problem-solving Method (PSM), and a Model-PSM Mapping, which operate on the local Product Model to produce reasoning outcomes. The use of a common Domain Ontology across tasks and models facilitates semantic consistency of variables and relations in constructing Bayesian networks for design and diagnosis.The approach is motivated by inefficiencies encountered in cleanly exchanging and integrating design FMEA and diagnosis models. Demonstration software under development is intended to illustrate how the DAEDALUS framework can be applied to knowledge sharing and exchange between Bayesian network-based design FMEA and diagnosis modeling tasks. Anticipated limitations of the DAEDALUS methodology are discussed, as is its relationship to Tomiyama's Knowledge Intensive Engineering Framework (KIEF). DAEDALUS is grounded in formal knowledge engineering principles and methodologies established during the past decade. Finally, the framework is presented as one possible approach for improved integration of generalized design and diagnostic modeling and knowledge exchange.