Automatic classification of defects on the product surface in grinding and polishing

Zhang, Xiang; Krewet, Carsten; Kuhlenkoetter, Bernd

doi:10.1016/j.ijmachtools.2005.03.013

Cited by 48 publications

(20 citation statements)

References 21 publications

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“…In order to improve the process, predict the performance [5,6], they defined product DNA. Compared with product lifecycle management, product DNA will be more accurate to guide the optimization of product processing [7,8] and quality defect diagnosis [9,10].…”

Section: Introductionmentioning

confidence: 99%

A method of coding for aerospace product quality DNA

Jiayu

2018

MATEC Web Conf.

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Aiming at the problem that the manufacturing process of our aerospace products is relatively discrete and the lack of appropriate quality monitoring and feedback mechanism, a method of coding for aerospace product quality DNA was proposed. Based on the structure of biological DNA and the theory of quality assessment, equipment diagnosis and quality traceability, the biological DNA structure was transformed into the structure of aerospace product quality DNA, and the concept of aerospace product quality DNA was defined, including the genetic and variation characteristics of aerospace product quality DNA. The coding rules of aerospace product quality DNA were designed, and the designed encoding rules are applied to the case of welding of wall panels in the manufacturing process of aerospace products. The results show that the coding method can monitor and feedback the core information related to quality in the manufacturing process of aerospace products.

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Section: Introductionmentioning

confidence: 99%

A method of coding for aerospace product quality DNA

Jiayu

2018

MATEC Web Conf.

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“…Many classifiers are available to classify the defects. Support Vector Machine (SVM) had good accuracy in many studies [11,12]. Few researchers have observed that the performance of Artificial Neural Network (ANN) to be good for such applications [13,14].…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of surface defects of gun barrels using computer vision and machine learning

2015

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“…Tian et al [3] presented a review of the surface measurement methods characterized by using active vision and light scattering techniques and then proposed a new integrative method for multi-parameter surface measurement. Zhang et al [4] presented a vision system to automatize the detection and classification of surface defects due to the grinding and polishing processes. Sun et al [5] proposed a real-time imaging and detection system for weld defects in steel tubes.…”

Section: Introductionmentioning

confidence: 99%