Measuring finger engagement during manual assembly operations in automotive assembly

Vedant, Rishabh Mulesh; Krugh, Matthew; Mears, Laine

doi:10.1016/j.promfg.2019.06.095

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…Content of the study Keywords Krugh et al (2016) A total of 41 input variables (R 2 = 0.576) were reduced to 6 variables by regression modeling (R 2 = 0.923) as engagement length, connector width, connector height, work height, female pigtail and male pigtail Critical product and process factors for correct connector assembly Deal and Bernard (2014) Studied the correlations among connector grip type, distance of travel, force of plugging, duration of the process and the acceptable effort for connector assembly using modified psychophysical method. Contrary to the connector travel distance, plugging force and low duty cycles have an important impact on acceptable effort Critical product and process factors for correct connector assembly Koiva et al (2012) Studied the forces exerted by fingers and measured them as in the range of 13-16 N by a strain-gauge-based device capable of accurately measuring fingertip forces Fingertip forces for correct connector assembly Potvin (2012) Developed a relationship between duty cycle and relative effort and proposed peak forces (N) and impulse (N.s) for the three grips at three different frequencies Fingertip forces for correct connector assembly Krugh et al (2016), Vedant et al (2019) Studied sensor glove to measure finger engagement interaction and contact force location of a manual electrical connector assembly task Fingertip engagement for correct connector assembly Mura et al (2016) Proposed an innovative system based on the interaction between a force sensor and an augmented reality (AR) equipment used to give to the worker the necessary information about the correct assembly sequence and to alert him in case of errors AR for correct assembly sequence…”

Section: Authorsmentioning

confidence: 99%

“…Electrical system defects represent a significant part of them (i.e. the first or second in the defect list) (Antani, 2014;Vedant et al, 2019). If any electrical failure occurs in a single connection during assembly process, the vehicle systems may not communicate correctly, and the vehicle may require extensive rework activity incurring a potentially added cost with risk of possible other defects' occurrence (Mura et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Koiva et al (2012) studied the forces exerted by fingers and measured them as in the range of 13-16 N by a strain-gaugebased device capable of accurately measuring fingertip forces. For better understanding of the process, Vedant et al (2019) studied the engagement of fingertips during the connector plugin operation and found that the index finger and thumb are used most frequently; on the other hand, the middle finger is generally activated in smaller connectors for the right-hand users. There is no dominant finger observed for the left-hand users.…”

Section: Introductionmentioning

confidence: 99%

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Failure prediction in electrical connector assembly: a case in automotive assembly process

Altinisik

Yildirim

2020

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Purpose Electrical defects cover an important part of assembly defects and strongly affect the vehicle system performance. Almost 40% of assembly defects are classified as human errors and electrical connection failures represent a significant part of them. Humans still remain a cost-effective solution for the flexible manufacturing systems with increasing product complexity. So, understanding human behaviors is still a challenging task. The purpose of this study is to define, prioritize and validate the critical factors for the complexity of electrical connector plugin process. Design/methodology/approach The critical variables were defined by the expert team members. The required number of measurements and variables were revised resulting preliminary analysis of binary logistic regression. After the revision of measurement plan, the list of critical input variables and the mathematical model were defined. The model has been validated by the fitted values of the residuals (FITS analysis). Findings To the best of the authors’ knowledge, this is one of the limited studies, which defines the critical factors for electrical connection process complexity. Female connector harness length, connector width/height/length differences, operator sense of correct connector matching and ergonomy were defined as the factors with the highest impact on the failure occurrence. The obtained regression equation strongly correlates the failure probability. Practical implications The obtained mathematical model can be used in new model development processes both for the product and assembly process design (ergonomy, accessibility and lay-out). Originality/value The obtained risk factors demonstrated a strong correlation with assembly process complexity and failure rates. The output of this study would be used as an important guide for process (assembly line ergonomy, accessibility and lay-out) and product design in new model development and assembly ramp-up phases.

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Section: Authorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Failure prediction in electrical connector assembly: a case in automotive assembly process

Altinisik

Yildirim

2020

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“…Such network supports mobile applications, easy deployment, high scalability, low latency, and geopolitical independence at a lower cost and restrictions than traditional cloud computing structures. Micro clouds could be essential for delay-sensitive manufacturing applications like real-time monitoring systems [26,27], smart assembly platforms [27], and human-machine integrated data representation [28]. A machine-level Micro-Cloud manages six major aspects (6M) namely, 1-Material: property, strengths, and functions 2-Machine: precision, calibration, and automation 3-Methods: tools, analytics, and knowledge, 4-Measurement: calibration, noise reduction, accuracy, 5-Maintenance: monitor, predict, and avoid 6-Models: predict, optimize, and resilient.…”

Section: Edge Micro-cloudsmentioning

confidence: 99%

From Centralized Systems to Distributed 5G-Enabled Manufacturing

Baxter¹,

Lundberg²

2020

Preprint

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The landscape of centralized cloud computing is now changing to distributed and decentralized clouds with promising impacts on energy consumption, resource availability, resilience, and customer experience. This research highlights the impacts of emerging IT trends, namely, 5G wireless technology, blockchain, and industrial Artificial Intelligence (AI) in development and realization of the next generation of cloud computing. Integration of these technologies in cyber-physical system and cloud manufacturing paradigms is explained and a unified edge-fog-cloud architecture is proposed for successful implementation in manufacturing systems.

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“…The most common human errors caused by “assembly system factors,” “product factors” and “operator factors” can be classified as omission, improper installation, wrong part assembly and incorrect operations (Mura et al , 2016). Regarding the assembly defects, 40% of the total defects are classified as human errors (Antani, 2014; Vedant et al , 2019). The manual tightening defects are accountable for the top 28% of failures found in automotive assembly, according to historical data collected over one year in a major automotive assembly plant (Antani, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of failure risks for manual tightening operations in automotive assembly lines

Altinisik¹,

Yildirim

Topçu

2022

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Purpose The tightening operations are one of the most critical operations in automotive assembly lines because of its direct impact on customer safety. This study aims to evaluate the major complexity drivers for manual tightening operations, correlate with real tightening failure data and propose mitigations to improve the complexity. Design/methodology/approach In the first stage, the complexity drivers for manual tightening operations were identified. Then, the relative importance of the risk attributes was defined by using pairwise comparisons questionnaire. Further, failure mode effect analysis–analytic hierarchy process (FMEA–AHP) and AHP ratings methods were applied to 20 manual tightening operations in automotive assembly lines. Finally, the similarities between the revealed results and the real failure rates of a Turkish automotive factory were examined and a sensitivity analysis was conducted. Findings The correlation between the proposed methods and manual tightening failure data was calculated as 83%–86%. On the other hand, the correlation between FMEA–AHP and AHP ratings was found as 92%. Poor ergonomics, operator competency and training, operator concentration-loose attention fatigue, manual mouthing before the tightening operation, frequent task changes, critical tightening sequence, positioning of the part and/or directional assembly were found relatively critical for the selected 20 tightening operations. Originality/value This is a unique study for the evaluation of the attributes for manual tightening complexity in automotive assembly lines. The output of this study can be used to improve manual tightening failures in manual assembly lines and to create low complexity assembly lines in new model launches.

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Measuring finger engagement during manual assembly operations in automotive assembly

Cited by 4 publications

References 11 publications

Failure prediction in electrical connector assembly: a case in automotive assembly process

Failure prediction in electrical connector assembly: a case in automotive assembly process

From Centralized Systems to Distributed 5G-Enabled Manufacturing

Evaluation of failure risks for manual tightening operations in automotive assembly lines

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