A novel method of analyzing quality defects due to human errors in engine assembly line

Yang, Lei; Su, Qiang; Liangfa, Shen

doi:10.1109/iciii.2012.6339943

Cited by 13 publications

(6 citation statements)

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“…Con la mejora significativa de la confiabilidad y la estabilidad de las instalaciones y equipos, el error humano se ha convertido en uno de los factores más críticos para los defectos de calidad. Según la literatura, alrededor del 70% al 90% de los defectos de calidad en los sistemas de producción son directa o indirectamente debido a los errores humanos (Le, Qiang, y Liangfa, 2012). Los errores humanos generalmente han sido reconocidos como actos inseguros realizados por los operadores o los tomadores de decisiones (Liu, Hwang y Liu, 2009).…”

Section: Resultsunclassified

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2021

3C Tecnología

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Section: Resultsunclassified

“…Algunos estudios han identificado los errores humanos que contribuyen a los defectos o fallas en procesos o productos tales como en Producción y Calidad (Qeshmy et al, 2019;Neumann, Kolus y Wellss, 2016;Layer, Karwowski y Furr, 2009;Lin, Drury y Kim, 2001;Bubb, 2005;Sylla y Drury, 1995;Le et al, 2012;Haug, Zachariassen y van Liempd, 2011;Wang, 2013).…”

Section: Resultsunclassified

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2021

3C Tecnología

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“…Broadly speaking, there are two main approaches to human reliability in manual assembly: the development of context-specific techniques [37,69,70] and the application of classic HRA techniques with or without modifications [6,15,71]. Context-specific methods are expected to provide more precision in the calculation of HEP, but they are more resourceintensive.…”

Section: Human Reliability Analysis: Origins and Applications In Assementioning

confidence: 99%

“…However, in manufacturing domains where product complexity and variety present particular challenges, manual work remains a viable alternative. This is the case of such manufacturing domains as consumer electronics [3], aerospace manufacturing [4,5], combustion engine assembly [6], automotive manufacturing [7,8] and the production of industrial machines and tools [9,10]. Hence, for manual assembly to yield a final product of the appropriate level of quality, several operations must be executed properly.…”

Section: Introductionmentioning

confidence: 99%

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

2021

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Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors.

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“…Estrada et al (2007) develop a taxonomy of assembly errors. Kern and Refflinghaus (2015) and Yang et al (2012) adopted human reliability analysis techniques to assess human error in assembly operations. Nevertheless, human errors and QPs in parts feeding processes received much less attention.…”

Section: Introductionmentioning

confidence: 99%

Modeling errors in parts supply processes for assembly lines feeding

Caputo

Pelagagge

Salini

2017

IMDS

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Purpose The purpose of this paper is to develop a quantitative model to assess probability of errors and errors correction costs in parts feeding systems for assembly lines. Design/methodology/approach Event trees are adopted to model errors in the picking-handling-delivery-utilization of materials containers from the warehouse to assembly stations. Error probabilities and quality costs functions are developed to compare alternative feeding policies including kitting, line stocking and just-in-time delivery. A numerical case study is included. Findings This paper confirms with quantitative evidence the economic relevance of logistic errors (LEs) in parts feeding processes, a problem neglected in the existing literature. It also points out the most frequent or relevant error types and identifies specific corrective measures. Research limitations/implications While the model is general purpose, conclusions are specific to each applicative case and are not generalizable, and some modifications may be required to adapt it to specific industrial cases. When no experimental data are available, human error analysis should be used to estimate event probabilities based on underlying modes and causes of human error. Practical implications Production managers are given a quantitative decision tool to assess errors probability and errors correction costs in assembly lines parts feeding systems. This allows better comparing of alternative parts feeding policies and identifying corrective measures. Originality/value This is the first paper to develop quantitative models for estimating LEs and related quality cost, allowing a comparison between alternative parts feeding policies.

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A novel method of analyzing quality defects due to human errors in engine assembly line

Cited by 13 publications

References 1 publication

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Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

Modeling errors in parts supply processes for assembly lines feeding

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