Defect prediction models to improve assembly processes in low-volume productions

et al. 2021

Prod. Eng. Res. Devel.

Self Cite

Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the effectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to define a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning.

Section: Defect Prediction Models In Inspection Planningmentioning

confidence: 99%

Section: Defect Prediction Models In Inspection Planningmentioning

confidence: 99%

Section: Defect Prediction Models In Inspection Planningmentioning

confidence: 99%

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Inspection planning by defect prediction models and inspection strategy maps

et al. 2021

Prod. Eng. Res. Devel.

Self Cite

“…A large number of studies in the literature use assembly complexity to predict product defects [8,10,19,20,[11][12][13][14][15][16][17][18]. These models were developed for predictive and quality improvement purposes in several industrial fields, ranging from the electromechanical to the automotive sector.…”

Section: Conceptual Background: Defect Prediction Model For Assembly Processesmentioning

confidence: 99%

“…4. The main reason is that, although each machine differs from the others in some details, this device is common to all assembled machines [18]. The three functions performed by a pre-stretch device are: (i) pulling/unwinding, (ii) pre-stretch and positioning of the plastic film, (iii) performing the necessary number of windings.…”

Section: Practical Case Studymentioning

confidence: 99%

Quality Monitoring of Assembled Products by Defect Prediction Models

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.

Defects-per-unit control chart for assembled products based on defect prediction models

Int J Adv Manuf Technol

et al. 2021

Self Cite

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. The innovative aspect of such DPU-chart is that, unlike conventional SPC charts requiring preliminary experimental data to estimate the control limits (phase I), it is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is based on the structural complexity of the assembled product. By avoiding phase I, the novel approach may be of interest to researchers and practitioners to speed up the chart’s construction phase, especially in low-volume productions. The description of the method is supported by a real industrial case study in the electromechanical field.