Statistical Modeling of Defect Propensity in Manual Assembly as Applied to Automotive Electrical Connectors

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

doi:10.1016/j.procir.2016.02.335

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…By focusing on mixedmodel automotive assembly, manufacturing complexity was estimated incorporating variables driven by design, process and human factors [23]. In later studies, Krugh et al [6,40] adapted the approach proposed by Antani to be implemented with automotive electromechanical connections in a large complex system. Falck et al [24] designed a tool to predict and control operator-induced quality errors by developing a method for predictive assessment of the complexity of manual assembly.…”

Section: Defect Prediction Modelsmentioning

confidence: 99%

Defect prediction for assembled products: a novel model based on the structural complexity paradigm

Verna

Genta

Galetto

et al. 2022

Int J Adv Manuf Technol

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Increased assembly complexity is one of the main challenges in manufacturing as it can induce an increase in time, cost, and defects. Several approaches have been proposed in the literature to predict product defects using assembly complexity as a predictor. However, most of these are not directly applicable because they rely on experts’ prior subjective knowledge and are designed for specific industrial applications. To overcome this issue, the present research proposes a novel approach to predict product defects from a purely objective assessment of product complexity, without the need for expert evaluations and assembly experience. A recent conceptual paradigm of complexity that considers only structural properties of assembly parts and their architectural structure is adopted in the proposed approach. The novel model is applied to a real assembly process in the electromechanical field and is compared with one of the most accredited in the literature, i.e., the Shibata–Su model. Empirical results show that, despite the super-linear relationship between defect rates and complexity in both models, the objective approach used in the novel model leads to more accurate and precise predictions of defectiveness rates, as it does not include the variability introduced by expert subjective assessments. Adopting this novel model can effectively improve the estimate of product defects and support designers’ decisions for assembly quality-oriented design and optimization, especially in early design phases.

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Section: Defect Prediction Modelsmentioning

confidence: 99%

Defect prediction for assembled products: a novel model based on the structural complexity paradigm

Verna

Genta

Galetto

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…By focusing on mixed-model automotive assembly, manufacturing complexity was estimated incorporating variables driven by design, process and human factors [23]. In later studies, Krugh et al [6,39] adapted the approach proposed by Antani to be implemented with automotive electromechanical connections in a large complex system. Falck et al [24] designed a tool to predict and control operator-induced quality errors by developing a method for predictive assessment of the complexity of manual assembly.…”

Section: Defect Prediction Modelsmentioning

confidence: 99%

Defect Prediction For Assembled Products: A Novel Model Based On The Structural Complexity Paradigm

Verna

Genta

Galetto

et al. 2021

Preprint

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Increased assembly complexity is one of the main challenges in manufacturing. Complexity can induce increased assembly cost and time, operational issues, costly defects and quality losses. Several approaches have been proposed in the literature to predict product defects by using assembly complexity as predictor. Despite defect prediction is of utmost importance from the early stages of product and related quality inspection design, most approaches are not directly applicable because they rely on the operators' prior subjective knowledge and are designed for specific industrial applications. To overcome this issue, the present research proposes a novel approach to predict product defects from a more objective evaluation of complexity. This is one of the first attempts to predict product defects and improve its quality with a purely objective assessment of the complexity of the assembled product, without the need for operators' evaluations and assembly experience. Defect rates in the model are predicted by using a recent conceptual paradigm of complexity that considers only structural properties of assembly parts and their architectural structure. The novel model is applied to a real assembly process in the electromechanical field and is compared with one of the most accredited in the literature, i.e. the Shibata-Su model. Empirical results show that, despite the super-linear relationship between defect rates and complexity in both models, the objective approach used in the novel model leads to more accurate and precise predictions of defectiveness rates, as it does not include the variability introduced by operators' subjective assessments. Adopting this novel model can effectively improve the estimate of product defects and support designers’ decisions for assembly quality-oriented design and optimization, especially in early design phases.

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“…Krugh et al presented a framework for enumerating assembly variables in a fully manual automotive assembly process correlated with the potential for quality defect based on the design, process and human factors. A general regression model was created by applying all of the collected variables to an OLS regression model (Krugh et al, 2016). Falck et al presented a method for predictive assessment of basic manual assembly complexity and explained 32 complexity criteria to aid designers in preventing costly errors during assembly in early design phase of new manufacturing concepts (Falck et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Due to the unquantifiable information in decision-making issues and the influence of objective factors such as the uncertainty of the evaluation situation, it is difficult to make a definitive, reasonable and accurate judgement on the complex relationship between the influence factors (Zheng et al, 2016a;Wang et al, 2017;Feng et al, 2018b;Krüger et al, 2017). In addition, the increasing demands on high automation level, flexibility of manufacturing system, and also new requirements for their interaction between each other generate new challenges for both mechanic and electric design of modern assembly system and their subsystems (Gao et al, 2015;Krugh et al, 2016). However, the characteristics of electric design with respect to control system are rarely incorporated into analysis by measuring the complexity of assembly system in the previous.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the complexity of subassemblies in a fully automated assembly system

Gao

Zheng

et al. 2019

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Purpose Complexity is the main challenge for present and future manufacturers. Assembly complexity heavily affects a product’s final quality in the fully automated assembly system. This paper aims to propose a new method to assess the complexity of modern automated assembly system at the assembly design stage with respect to the characteristics of both manufacturing system and each single component to be mounted. Aiming at validating the predictive model, a regression model is additionally presented to estimate the statistic relationship between the real assembly defect rate and predicted complexity of the fully automated assembly system. Design/methodology/approach The research herein extends the S. N. Samy and H. A. ElMaraghy’s model and seeks to redefine the predictive model using fuzzy evaluation against a fully automated assembly process at the assembly design stages. As the evaluation based on the deterministic scale with accurate crisp number can hardly reflect the uncertainty of the judgement, fuzzy linguistic variables are used to measure the interaction among influence factors. A dependency matrix is proposed to estimate the assembly complexity with respect to the interactions between mechanic design, electric design and process factors and main functions of assembly system. Furthermore, a complexity attributes matrix of single part is presented, to map the relationship between all individual parts to be mounted and three major factors mentioned in the dependency matrix. Findings The new proposed model presents a formal quantification to predict assembly complexity. It clarifies that how the attributes of assembly system and product components complicate the assembly process and in turn influence the manufacturing performance. A center bolt valve in the camshaft of continue variable valve timing is used to demonstrate the application of the developed methodology in this study. Originality/value This paper presents a developed method, which can be used to improve the design solution of assembly concept and optimize the process flow with the least complexity.

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Statistical Modeling of Defect Propensity in Manual Assembly as Applied to Automotive Electrical Connectors

Cited by 7 publications

References 7 publications

Defect prediction for assembled products: a novel model based on the structural complexity paradigm

Defect prediction for assembled products: a novel model based on the structural complexity paradigm

Defect Prediction For Assembled Products: A Novel Model Based On The Structural Complexity Paradigm

Quantifying the complexity of subassemblies in a fully automated assembly system

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