Key Quality Characteristics Identification Method for Mechanical Product

Ma, Lan; Mao, Jian; Fan, Hongri

doi:10.1016/j.procir.2016.10.015

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…It investigates the relationship of the characteristics with the label of the product, assuming two classes labeled as either OK or NOK in its original formulation, but has been extended to multiple classes, see Kononenko (1994) and Robnik-Sikonja and Kononenko (2003). Ma et al (2016) extend the process by the Mahalanobis-Taguchi system. The system enables the definition of KCs even if the dataset does not contain any NOK labels (Wei et al, 2011).…”

Section: Methods Based On Data Analysismentioning

confidence: 99%

Auto-encoder-based algorithm for the selection of key characteristics for products to reduce inspection efforts

Greipel

Frank

Huber

et al. 2022

IJQRM

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PurposeTo ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics. For optimization of costs and benefits, key characteristics can be defined by which the product quality can be checked with sufficient accuracy. The manual selection of key characteristics requires substantial planning effort and becomes uneconomic if many product variants prevail. This paper, therefore, aims to show a method for the efficient determination of key characteristics.Design/methodology/approachThe authors present a novel Algorithm for the Selection of Key Characteristics (ASKC) based on an auto-encoder and a risk analysis. Given historical measurement data and tolerances, the algorithm clusters characteristics with redundant information and selects key characteristics based on a risk assessment. The authors compare ASKC with the algorithm Principal Feature Analysis (PFA) using artificial and historical measurement data.FindingsThe authors find that ASKC delivers superior results than PFA. Findings show that the algorithms enable the cost-efficient selection of key characteristics while maintaining the informative value of the inspection concerning the quality.Originality/valueThis paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.

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Section: Methods Based On Data Analysismentioning

confidence: 99%

Auto-encoder-based algorithm for the selection of key characteristics for products to reduce inspection efforts

Greipel

Frank

Huber

et al. 2022

IJQRM

View full text Add to dashboard Cite

show abstract

“…Ma et al believed that the identification of KQC helped to reduce the scope of quality detection and improving detection efficiency. They proposed the Mahalanobis-Taguchi System (MTS) based on the RELIEFF algorithm, which combined the least-squares regression with the state-space model to identify KQCs [13]. Wang et al proposed an improved IGSA algorithm based on reverse learning and immune algorithm, which combined the advantages of filtering efficiency and high-precision packaging to solve the problem of high-quality feature output dimension [14].…”

Section: Introductionmentioning

confidence: 99%

Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process

Gao

Zhou

et al. 2022

Processes

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The product’s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvement of enterprise in the future.

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“…the gap and the flush between the skin of two parts. Ma et al (2016) first selected 22 quality characteristics from 60 normal samples and 30 abnormal samples, then cut to the final 16 using Relief algorithm. However, the number of options is still too large, which would cause difficulties in actual production.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive identification of aircraft coordination feature based on complete importance modeling and its engineering application

Guo

Zou

Liu

et al. 2018

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Purpose Coordination feature (CF) is the information carrier in dimension and shape transfer process in aircraft manufacturing. The change of its geometric size, shape, position or other attributes would affect the consistency of accumulated errors between two or more assemblies. To identify these “key characteristics” that have a close relationship with the assembly precision, a comprehensive method was developed under digital manufacturing environment, which was based on importance calculation. The multi-hierarchy and multi-station assembly process of aircraft products were also taken into consideration. Design/methodology/approach First, the interaction and evaluation relationship between components at different manufacturing stages was decomposed with a hierarchical net. Second, to meet coordination accuracy requirements, with the integrated application of Taguchi quality loss function, accuracy principal and error correction coefficient H, the quality loss between target features and candidate features at adjacent assembly hierarchies were calculated, which was based on their precision variation. Third, the influence degree and affected degree of the features were calculated with DEMATEL (decision-making trial and evaluation laboratory) method, and the concepts of centrality degree index and cause degree index were proposed for calculating the complete importance degree to eventually identify the CFs. Findings Based on the proposed methodology, CFs, affecting the skin profile and the flush coordination accuracy, were successfully identified at different assembly hierarchies to a certain type of wing flap component. Originality/value Benefit results for the engineering application showed that the deviation of skin profile was more accurate than before, and the tolerance was also closer to the centerline of required assembly precision range. Moreover, the stability in the assembly process was increased by 26.9 per cent, which could bring a higher assembly quality and an enhancement on aircraft’s flight performance.

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Key Quality Characteristics Identification Method for Mechanical Product

Cited by 8 publications

References 5 publications

Auto-encoder-based algorithm for the selection of key characteristics for products to reduce inspection efforts

Auto-encoder-based algorithm for the selection of key characteristics for products to reduce inspection efforts

Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process

Comprehensive identification of aircraft coordination feature based on complete importance modeling and its engineering application

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