Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors

Krugh, Matthew; Antani, Kavit; Mears, Laine; Schulte, Jorg

doi:10.1016/j.promfg.2016.08.014

Cited by 20 publications

(20 citation statements)

References 8 publications

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“…Several models were developed in the literature to predict defects of a final assembled product [30][31][32][33][34][35][36][37][38][39]. A large part of these models relies on the close relationship between assembly complexity and defectiveness rate related to each process step, also called workstation.…”

Section: Defect Prediction Models In Inspection Planningmentioning

confidence: 99%

Inspection planning by defect prediction models and inspection strategy maps

Verna

Genta

Galetto

et al. 2021

Prod. Eng. Res. Devel.

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Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the effectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to define a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning.

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Section: Defect Prediction Models In Inspection Planningmentioning

confidence: 99%

Inspection planning by defect prediction models and inspection strategy maps

Verna

Genta

Galetto

et al. 2021

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

show abstract

“…A large number of studies in the literature use assembly complexity to predict product defects [8,10,19,20,[11][12][13][14][15][16][17][18]. These models were developed for predictive and quality improvement purposes in several industrial fields, ranging from the electromechanical to the automotive sector.…”

Section: Conceptual Background: Defect Prediction Model For Assembly Processesmentioning

confidence: 99%

Quality Monitoring of Assembled Products by Defect Prediction Models

Verna

Genta

Galetto

et al. 2021

Preprint

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Typically, monitoring quality characteristics of very personalized products is a difficult task due to the lack of experimental data. This is the typical case of processes where the production volume continues to shrink due to the growing complexity and customization of products, thus requiring low-volume productions. This paper presents a novel approach to statistically monitor Defects Per Unit (DPU) of assembled products based on the use of defect prediction models. Unlike traditional control charts requiring preliminary experimental data to estimate the control limits (phase I), the proposed DPU-chart is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is built on the structural complexity of assembled product. The novel approach may be of interest to researchers and practitioners to speed up the construction of the chart, especially in cases of low-volume productions due to the limited amount of data. The description of the method is supported by a real industrial case study in the electromechanical field.

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“…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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Prediction of Defect Propensity for the Manual Assembly of Automotive Electrical Connectors

Cited by 20 publications

References 8 publications

Inspection planning by defect prediction models and inspection strategy maps

Inspection planning by defect prediction models and inspection strategy maps

Quality Monitoring of Assembled Products by Defect Prediction Models

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

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