A Knowledge Based FMEA to Support Identification and Management of Vehicle Flexible Component Issues

Renu, Rahul Sharan; Visotsky, Darian; Knackstedt, Stephan; Mocko, Gregory M.; Summers, Joshua D.; Schulte, Joerg

doi:10.1016/j.procir.2016.02.112

Cited by 24 publications

(14 citation statements)

References 13 publications

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“…Inconsistent communication errors and information transfers as reported by Cabanes et al (2016) and Renu et al (2016) and failures in the quality of information during the DFMEA as Würtenberger et al (2014) report, can influence the quality of the product. Therefore, there are different integration practices for the execution of the DFMEA that make the information more specific and detailed, improving the quality of the same, and can explain the fact that most of the problems related to quality in case B, not related to the projects, but to the manufacturing problems, corroborating with some authors of the area (CHAN; IP; ZHANG, 2012;JIANG et al, 2012;KOCH et al, 2013, MARINI et al, 2015LUNDGREN;HEDLIND;KJELLBERG, 2015;WÜRTENBERGER et al, 2014).…”

Section: Dfmea's Role In Integrating Pd and Qementioning

confidence: 97%

The role of DFMEA in the integration between product development and quality engineering: cases in large companies

Bueno¹,

Toledo²,

Silva³

2019

Product (São Carlos, Online)

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The increasing trend of product recalls is due to quality implications in the early stages of the Product Development Process (PDP) and could be prevented through preventive measures from the quality management system such as Design Failure Modes Analysis and Effects (DFMEA), one of the main tools of Quality Engineering. Besides the quality of products, functional integration is also considered one of the ways in which companies respond to competitive pressures. Thus, in the search for best practices to organize and manage product development, this article aims to examine the role of DFMEA in the context of functional integration between Product Development (PD) and Quality Engineering (QE) through case studies in two Brazilian industrial companies. The companies differ in the physical proximity between these departments, where one case PD and QE are co-located and the other are located in different cities and states. The results point that the use of integration practices such as Information and Communication Technologies (ICTs) and co-location during a DFMEA and despite the advancement of ICTs, co-location has indicated that it is a relevant mechanism to improve problem solving, due to the detail of the information and dynamism of the discussion meetings of failures and solutions. In addition, companies link the compensation system of employees to the failure rates of the new product in the market and the costs of non-quality boosting communication and collaboration between teams. In one of the cases, the integration is also facilitated by the PD and QE activities and the DFMEA reports are reported to the same manager.

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Section: Dfmea's Role In Integrating Pd and Qementioning

confidence: 97%

The role of DFMEA in the integration between product development and quality engineering: cases in large companies

Bueno¹,

Toledo²,

Silva³

2019

Product (São Carlos, Online)

View full text Add to dashboard Cite

show abstract

“…Design FMEA (DFMEA). DFMEA aims at analyzing the product design itself in terms of quality-critical aspects, while PFMEA was developed to investigate manufacturing or assembly processes and the potential failure CERs involved in these [17] [18]. Other than FMEA or FTA, which are both based on CERs of failures, the method presented in [6] provides a framework for the expert-based assessment of CERs of process characteristics without breaking it down into potential failures of these.…”

Section: Quality Management and Cause-effect Relationshipsmentioning

confidence: 99%

“…This may add further value to the overall achievable process quality. this information can either be collected in the form of error protocols [18] or from user interactions when inquiring the network for the root cause of a present failure [32].…”

Section: Quality Management and Cause-effect Relationshipsmentioning

confidence: 99%

Failure Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network

Kirchhof¹,

Haas²,

Kornas³

et al. 2020

Preprint

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The production of lithium-ion battery cells is characterized by a high degree of complexit due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks such as failure analysis challenging. In this paper, a method is presented, which includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production. Using this model, we are able to conduct root cause analyses as well as analyses of failure propagation. The former support operators in identifying root causes once a cell possesses a specific failure by calculating most-probable explanations matched to the individual battery cell data. The latter enable us to analyze propagation of failures and deviations in the production chain and thus provide support for placement of quality gates, leading to a significant reduction in scrap rate. Moreover, it gives an insight into which process steps are key drivers for which final product characteristics.

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“…FMECA is a systematic approach to map failure mode, effect, and cause of technical system. Additionally, some alternative approaches are selected, including data envelopment analysis (DEA) [25], technique for order preference by similarity to an ideal solution (TOPSIS) [26], grey relational analysis (GRA) [27], decision-making trial and evaluation laboratory (DEMATEL) [28], artificial neural network [29], knowledge base [30], and fuzzy linguistic variables [31]. By using fault tree analysis and Bayesian network, Dongiovanni and Iesmantas [32] presented a new methodology to extrapolate and estimate components failure rate.…”

Section: Failure Analysismentioning

confidence: 99%

Analysis of Failures and Influence Factors of Critical Infrastructures: A Case of Metro

Deng

Song

Zhou

et al. 2020

Advances in Civil Engineering

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Metro plays an indispensable role in promoting social and economic development of the big cities. However, the safety operation of the metro system is believed to be an arduous work for its operation being susceptible to various factors, especially equipment failure, which could bring about delay, interruption, and even accidents. In consideration of the seriousness of metro accident, it is becoming more and more crucial to conduct proactive, targeted, and effective risk management to reduce failures. Firstly, based on the FMECA and influence diagram theory, this paper proposes a new analytical framework to study metro equipment failure in accordance with system decomposition. Furthermore, a case study is implemented to verify the feasibility and availability of this framework in evaluating criticality of failure modes and assessing its influence factors based on the collected data from metro operation company. As indicated by the research results, wheel damage is the most serious failure mode in the bogie system, and its most paramount influence factor is the shortage of professional skills. Finally, a discussion about the research methods and results is carried out, and several recommendations are put forward to improve safety management on the basis of research findings. This study has the potential to offer suggestions in regard to metro equipment design, operation, and maintenance for raising the safety level in metro operation.

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A Knowledge Based FMEA to Support Identification and Management of Vehicle Flexible Component Issues

Cited by 24 publications

References 13 publications

The role of DFMEA in the integration between product development and quality engineering: cases in large companies

The role of DFMEA in the integration between product development and quality engineering: cases in large companies

Failure Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network

Analysis of Failures and Influence Factors of Critical Infrastructures: A Case of Metro

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