On the track of human errors - Procedure and results of an innovative assembly planning method

Refflinghaus, Robert; Kern, Christian

doi:10.1016/j.promfg.2018.02.106

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…También sobresalen otros estudios en la industria de la manufactura con enfoque de Confiabilidad (Torres, Nadeau y Landau, 2019;Muxfeldt y Steil, 2018;Refflinghaus y Kern, 2018;Salas-Arias et al, 2018;Lopes y Forster, 2013;Power y Fox, 2014;Ruiz-Moreno y Trujillo, 2012;Brito et al, 2011;Myszewski, 2010;Paun et al, 2011;Boring, Griffith y Joe, 2007;Taylor-Adams y Kirwan, 1997;Baber y Stanton, 1996;Razak, Kamaruddin y Azid, 2008;Ruckart y Burgess, 2007;Drury, 2002;Drury y Watson, 1999;Fujita y Hollnagel 2004) y, la industria militar (Yu et al, 2000Wang y Zhao, 2010).…”

Section: Resultsunclassified

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2021

3C Tecnología

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Section: Resultsunclassified

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2021

3C Tecnología

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“…Detection of human presence in modern life is one of the interesting topics in recent years. In manufacturing field [19], the detection by means of sensors or cameras can prepare a time series data set which helpful to evaluate the error rate of operators. In this paper, we use industrial data gathered by Arkite.…”

Section: Methodsmentioning

confidence: 99%

Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories

Moghaddam

López

Vuyst

et al. 2021

2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA)

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Tracking assembly lines in manufacturing to provide assistance is one of the essential requirement in Smart Industry. Nevertheless, these given assistance and guidelines should be offered to operators when needed. Otherwise, it can be deemed patronising in some cases, e.g., experienced operators may require less assistance than junior operators. Therefore, to provide tailored guidance and assistance in assembly lines, the operators' experience-level should be classified at different levels. In this paper, we introduce three scenarios to achieve the classification of operators expert levels in a real case study (micro-step time-series data from a factory assembly line). We implement a Convolutional Neural Network model for time-series classification, using 5 convolutional layers, max-pooling layers and 5 dense layers with dropout to avoid overfitting. We compare the results of our approach with the ground truth and also with other classifiers as K-nearest neighbours, Random Forest and Naive Bayes classifier. Results show an accuracy of 77 to 98% and 71 to 88% for two of considered scenarios.

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“…Context-specific methods are expected to provide more precision in the calculation of HEP, but they are more resourceintensive. For example, methods time and quality measurement (MTQM), developed in Germany, requires in-depth knowledge of predetermined motion time systems (PMTS) and high investments in training time and money [72]. Little information about context-specific techniques is available, which hinders replication of the analysis process.…”

Section: Human Reliability Analysis: Origins and Applications In Assementioning

confidence: 99%

“…Specific human error probabilities are based on data from German automotive manufacturing. Application reported in the automotive industry [72].…”

Section: No [6] Conference Papermentioning

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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On the track of human errors - Procedure and results of an innovative assembly planning method

Cited by 6 publications

References 7 publications

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Untitled

Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories

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

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