Inspection planning by defect prediction models and inspection strategy maps

Verna, Elisa; Genta, Gianfranco; Galetto, Maurizio; Franceschini, Fiorenzo

doi:10.1007/s11740-021-01067-x

Cited by 16 publications

(16 citation statements)

References 52 publications

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“…Inspectors perform various quality control activities to detect these defects, which may require specific equipment depending on the workstation-output [9]. The adopted inspection controls are listed in Table 3.…”

Section: Practical Case Studymentioning

confidence: 99%

“…Most of them refer to mass productions, involving hundreds or thousands of pieces produced per month [10-15, 17, 19]. Recently, the authors focused on identifying appropriate defect prediction models in assembly processes [9,[21][22][23][24] according to the product structural complexity paradigm proposed by Alkan [25] and Sinha [26]. The same model was also recently adopted to help inspection designers in the inspection process planning from the early design phases [9].…”

Section: Conceptual Background: Defect Prediction Model For Assembly Processesmentioning

confidence: 99%

“…In this case, the number of elementary operations is 9 and, accordingly, the total number of possible defects that can occur in the workstation is at most 9. Turn the wrench to tighten the nut Damage to the nut Verna et al [9,24] showed that the total number of defects detected in each i-th workstation divided by the number of produced units, i.e. the defects per unit related to the i-th workstation ( ), can be expressed as a function of product complexity, as follows:…”

Section: Figurementioning

confidence: 99%

“…(1), depending solely on physical design information, is beneficial, especially in the early design stages, when real production data or the physical mockup of the product are not available. This model has been recently applied by the authors in the assembly of a low-volume production of wrapping machines, showing that the relationship between complexity and DPU follows a power-law relationship, as follows [9,24]:…”

Section: Figurementioning

confidence: 99%

“…In this study, assembly processes are addressed in detail. In particular, a defect prediction model developed in a recent study by the authors [9] is used to set up a control chart to monitor DPU detected in each workstation of a manufacturing process. This model relies on the relationship between DPU and product structural complexity.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Practical Case Studymentioning

confidence: 99%

Section: Conceptual Background: Defect Prediction Model For Assembly Processesmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quality Monitoring of Assembled Products by Defect Prediction Models

Verna

Genta

Galetto

et al. 2021

Preprint

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A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method

2023

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In manufacturing, complexity is considered a key aspect that should be managed from the early phases of product and system design to improve performance, including productivity, efficiency, quality, and costs. The identification of suitable methods to assess complexity has always been of interest to researchers and practitioners. As complexity is affected by several aspects of different nature, it can be assessed from objective or subjective viewpoints or a combination of both. To assess experienced complexity, the analysis relies on the subjective evaluations given by practitioners, usually expressed on nominal or ordinal scales. However, methods found in the literature often violate the properties of the scales, potentially leading to bias in the results. This paper proposes a methodology based on the analysis of categorical data using the multi expert-multi criteria decision making method. A number of criteria are adopted to assess assembly complexity and, from subjective evaluations of operators, product assembly complexity is assessed at an individual level and then, aggregating results, at a global level. A comparison between experienced complexity and an objective assessment of complexity is also performed, highlighting similarities and differences. The assessment of experienced complexity is much more straightforward and less demanding than objective assessments. However, this study showed that it is preferable to use objective assessments for highly complex products as individuals do not discriminate between different complexity levels. An experimental campaign is conducted regarding a manual assembly of ball-and-stick products to show the applicability of the methodology and discuss the results.

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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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Inspection planning by defect prediction models and inspection strategy maps

Cited by 16 publications

References 52 publications

Quality Monitoring of Assembled Products by Defect Prediction Models

Quality Monitoring of Assembled Products by Defect Prediction Models

A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method

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

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