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International Journal of Production Research Volume 53, 2015 - Issue 14
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A dynamic quality control approach by improving dominant factors based on improved principal component analysis

Guangzhou DiaoState Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaCorrespondence[email protected]
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,
Liping ZhaoState Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaView further author information
&
Yiyong YaoSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaView further author information
Pages 4287-4303 | Received 12 Mar 2014, Accepted 04 Dec 2014, Published online: 02 Jan 2015

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Sheng Hu, Liping Zhao, Yiyong Yao & Rushan Dou. (2016) A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis. International Journal of Production Research 54:19, pages 5783-5797.
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Articles from other publishers (12)

Yuan Gao, Xianhui Yin, Zhen He & Xueqing Wang. (2023) A deep learning process anomaly detection approach with representative latent features for low discriminative and insufficient abnormal data. Computers & Industrial Engineering 176, pages 108936.
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Sheng Hu, Gang Zhang, Xiaohui Zhao, Zhe Li & Wen Li. (2023) A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process. Journal of Engineered Fibers and Fabrics 18.
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Erdemalp ÖZDEN. (2022) Forecasting of Export Volume Using Artificial Intelligence Based Algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 11:2, pages 715-726.
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Wahb Zouhri, Lazhar Homri & Jean-Yves Dantan. (2022) Identification of the key manufacturing parameters impacting the prediction accuracy of support vector machine (SVM) model for quality assessment. International Journal on Interactive Design and Manufacturing (IJIDeM) 16:1, pages 177-196.
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Xuemin Sun, Shimin Liu, Jinsong Bao, Jie Li & Zengkun Liu. (2021) A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning. Machines 10:1, pages 11.
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Wahb Zouhri, Hamideh Rostami, Lazhar Homri & Jean-Yves Dantan. 2021. Artificial Intelligence and Industrial Applications. Artificial Intelligence and Industrial Applications 15 31 .
Yuehjen E. Shao & Yu-Ting Hu. (2020) Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process. Mathematics 8:1, pages 102.
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Yixiong Feng, Tianyue Wang, Bingtao Hu, Chen Yang & Jianrong Tan. (2020) An Integrated Method for High-Dimensional Imbalanced Assembly Quality Prediction Supported by Edge Computing. IEEE Access 8, pages 71279-71290.
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Guodong Wang, Anna Ledwoch, Ramin M. Hasani, Radu Grosu & Alexandra Brintrup. (2019) A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing 85, pages 105683.
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Dorina Weichert, Patrick Link, Anke Stoll, Stefan Rüping, Steffen Ihlenfeldt & Stefan Wrobel. (2019) A review of machine learning for the optimization of production processes. The International Journal of Advanced Manufacturing Technology 104:5-8, pages 1889-1902.
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Yongping Zhang, Ying Cheng, Xi Vincent Wang, Ray Y. Zhong, Yingfeng Zhang & Fei Tao. (2019) Data-driven smart production line and its common factors. The International Journal of Advanced Manufacturing Technology 103:1-4, pages 1211-1223.
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Ying Cheng, Ken Chen, Hemeng Sun, Yongping Zhang & Fei Tao. (2018) Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration 9, pages 1-13.
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