Product quality improvement method in manufacturing process based on kernel optimisation algorithm

Wei, Zhe; Feng, Yixiong; Zhang, Hong; Rong-xia, QU; Tan, Jianrong

doi:10.1080/00207543.2017.1324223

Cited by 19 publications

(7 citation statements)

References 23 publications

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“…Therefore, redundancy can be measured by the amount of common information between 𝑥 and 𝐷. The definition of redundancy 𝑅 between 𝑥 and 𝐷 is shown in Equation (4).…”

Section: 𝐼(𝑥mentioning

confidence: 99%

“…Wei et al proposed a kernel-based hybrid manifold learning and support vector machine algorithm for aero-engine product quality prediction. The data-driven prediction method is based on the algorithm model [4]. When the measurement data are insufficient or inaccurate, the algorithm model only depends on the previously collected data, significantly affecting the prediction results [5].…”

Section: Introductionmentioning

confidence: 99%

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A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning

et al. 2021

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The manufacturing of a high-precision servo valve belongs to multi-variety, small-batch, and customized production modes. In the process of assembly and commissioning, various characteristic parameters are critical indicators to measure product performance. To meet the performance requirements of a high-precision servo valve, the traditional method usually relies on the test bench and manual experience for continuous trial and error commissioning, which significantly prolongs the whole assembly-commissioning cycle. Therefore, this paper proposed a performance prediction method for a high-precision servo valve supported by digital twin assembly-commissioning. Firstly, the cloud-edge computing network is deployed in the digital twin assembly-commissioning system to improve the efficiency and flexibility of data processing. Secondly, the method workflow of performance prediction is described. In order to improve the accuracy of measurement data, a data correction method based on model simulation and gross error processing is proposed. Aiming at the problem of high input dimension of the prediction model, a key assembly feature parameters (KAFPs) selection method, based on information entropy (IE), is proposed and given interpretability. Additionally, to avoid the poor prediction accuracy caused by small sample data, a performance prediction method based on TrAdaboost was utilized. Finally, the hysteresis characteristic commissioning of a high-precision servo valve is taken as an example to verify the application. The results indicate that the proposed method would enable accurate performance prediction and fast iteration of commissioning decisions.

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“…Therefore, redundancy can be measured by the amount of common information between 𝑥 and 𝐷. The definition of redundancy 𝑅 between 𝑥 and 𝐷 is shown in Equation (4).…”

Section: 𝐼(𝑥mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning

et al. 2021

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“…If more defects are found than the smaller number, the lot is rejected. Quality analysis and quality improvement are eternal pursuits for manufacturing companies [17].…”

Section: Available Technologymentioning

confidence: 99%

Quality Analysis of 5.56 mm Ammunition Defect using Taguchi Method: A Review

Anggamawarti

Alviari

Sanjiwani³

et al. 2020

MECHTA

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In a manufacturing company, the quality loss is estimated by considering the number of defects. Taguchi is a method that finds strong conditions in uncontrollable environments of the field. Taguchi quantifies quality loss through a quality loss function. The Taguchi method particularly is focused on industrial processes. The method is actualizing quality philosophy for continuous quality improvement and cost reduction to improve manufacturing performance. The analysis is designed using Taguchi technique which is related to quality. A high-quality product has a minimal defect. The Taguchi method is used to analyze several defects of ammunition to reduce the number of ammunition defects. Ammunition consists of several parts are called projectile or bullet, cartridge case, propellant charge, and primer. Every part of its process possibly contributes to any defect. The defect type in every part of ammunition consists of critical, major, and minor defects. This paper is focused on cartridge case caliber 5.56 mm defect by using the Taguchi method. The quality characteristic of the experiment result used is smaller the better. Critical to Quality (CTQ) is determined to get a critical defect for cartridge cases such as split and perforated case. The influencing factors are brass cup thickness, hardness case after annealing, and annealing temperature. The Taguchi method is effective in reducing defects for the ammunition process to produce a good quality product.

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“…There are few studies on the key factors of manufacturing data affecting the quality of complex equipment. Wei Qian studied how to improve the product quality in the process of intelligent manufacturing, and pointed out that the utilization rate of production data, quality inspection compliance, quality problems and customer satisfaction are the key factors affecting the formation of relationships [1]. Shen Ying et al pointed out that data of different time spans and high-level quality inspection compliance can effectively improve deliver quality when studying deliver quality sensitive information management [2].…”

Section: Introductionmentioning

confidence: 99%

Research on the influence of manufacturing data on the quality of complex equipment

Wang

Bai²,

Wang³

et al. 2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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A relationship model between manufacturing data and complex equipment quality is studied and constructed. Combined with the data formed in each link of the whole life cycle of complex equipment, this paper studies and reveals the impact of manufacturing data on the quality of complex equipment. Based on the regulated intermediary theory model, the basic model of the regulation and intermediary relationship between product data richness, product quality inspection standard severity, product manufacturing risk and product quality is studied and established, and the action mechanism of product data richness on the quality of complex equipment is revealed, Through the case study, it is proved that the product manufacturing risk plays an intermediary role in the formation mechanism of the relationship between product data richness and product quality, and the severity of product quality inspection standards plays a regulatory role in the relationship between product data richness and product manufacturing risk, which provides a data-driven theoretical method for the quality control of complex equipment.

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Product quality improvement method in manufacturing process based on kernel optimisation algorithm

Cited by 19 publications

References 23 publications

A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning

A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning

Quality Analysis of 5.56 mm Ammunition Defect using Taguchi Method: A Review

Research on the influence of manufacturing data on the quality of complex equipment

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