Product family formation based on complexity for assembly systems

Hasan, Syed Maaz; Baqai, Aamer Ahmed; Butt, Sajid Ullah; Zaman, Uzair Khaleequz quz

doi:10.1007/s00170-017-1174-4

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…The ALC algorithm [34], [36] has been widely used in previous studies. However, the purpose of this method is to ensure that the data points in the same cluster are as close as possible and that the distances between different clusters are as far as possible [38]. This approach is different from the aim of typical process route knowledge discovery, which is to find exemplars to represent other data.…”

Section: B Clustering Algorithmsmentioning

confidence: 99%

A Novel Operation Sequence Similarity-Based Approach for Typical Process Route Knowledge Discovery

Wang

2021

IEEE Access

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A typical process route essentially represents the commonly used process planning-related knowledge and can be modified to generate new process routes easily. Hence, its quality directly affects the performance of newly generated process routes and thereby the goodness of products. To effectively discover typical process route knowledge, a reasonable similarity measure and a clustering method specifically for process routes are required. However, existing operation sequence similarity coefficients often assign coarse-grained similarities, which leads to inaccurate clustering results. For the clustering problem, most researchers have not considered the practical constraints during typical process route discovery. In this paper, an operation sequence similarity-based discovery method is presented. First, the characteristics and information requirements of the operation sequence similarity problem are analysed, and a novel comprehensive similarity coefficient combined with a modified pseudo-longest-commonsubsequence (pseudo-LCS) and Jaccard similarity coefficient is proposed based on this analysis. This coefficient considers the precedence relationship, the number of common operations, and the operation similarity simultaneously to handle all the potential similarity situations. Second, two soft constraints, namely, quantity constraint and size constraint, are introduced in the traditional process route clustering problem to ensure the quality and validity of the discovered typical process routes. To solve this more practical problem and achieve a balance between these two conflicting constraints, the K-medoids method is improved with an adjustment mechanism to generate valid results under these two soft constraints. Finally, numerical illustrations are presented to verify the effectiveness of the proposed methods. The results show that compared with existing similarity coefficients, the proposed comprehensive similarity coefficient is more sensitive and much better at distinguishing the tiny difference between the process routes. In addition, the modified K-medoids method can perform much better than existing methods on process route discovery data sets under two conflicting soft constraints.

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Section: B Clustering Algorithmsmentioning

confidence: 99%

A Novel Operation Sequence Similarity-Based Approach for Typical Process Route Knowledge Discovery

Wang

2021

IEEE Access

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“…Table 3 reports, for each i-th workstation, the complexities 1, , 2, and 3, , evaluated according to Eqs. ( 6), (7), and ( 9), respectively, and the final assembly complexity Ci derived by Eq. (5).…”

Section: The Novel Defect Prediction Model: Dpu Vs Structural Complexitymentioning

confidence: 99%

“…In manual assembly, the complexity of the assembly process is a fundamental driver affecting the usability of the equipment, the acquisition of dynamic skills, operation safety and human performance [3]. As a result, if assembly complexity is not managed adequately at early stages of process planning, it can lead to increased assembly time and errors and reduce assembly quality [4][5][6][7].…”

Section: Introductionmentioning

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.

show abstract

Section: Introductionmentioning

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.

show abstract

Product family formation based on complexity for assembly systems

Cited by 15 publications

References 34 publications

A Novel Operation Sequence Similarity-Based Approach for Typical Process Route Knowledge Discovery

A Novel Operation Sequence Similarity-Based Approach for Typical Process Route Knowledge Discovery

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

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