基于质量4.0的印制电路板智能缺陷检测研究

doi:10.12305/j.issn.1001-506X.2024.05.21

Abstract

Abstract:

With the rapid development of information technologies, we can find more opportunities to transform and develop the manufacturing industry, which drives the significant transformation of quality management methods. Based on the actual situation of the manufacturing industry, this work outlines the basic theory and key technologies of Quality 4.0, and further explore the application and implementation of Quality 4.0. Specifically, the printed circuit board (PCB) manufacturing is taken as an example, and design an intelligent defect detection scheme is design during PCB production based on the Quality 4.0 theory. And five key evaluation criteria for defect detection have been proposed. The proposed testing scheme can effectively help PCB manufacturing enterprises filter out false defects, control product yield, obtain defect resolution suggestions, and provide a learning and training platform for employees to master professional testing skills. This paper aims to study intelligent defect detection in the Quality 4.0 environment and its application in PCB, in order to promote the digital and intelligent transformation of manufacturing quality management.

Key words: Quality 4.0, quality management, printed circuit board (PCB) manufacturing, defect detection, intelligent manufacturing

CLC Number:

F253.3

Huchen LIU, Ke LI, Heming WANG, Hua SHI. Intelligent defect detection based on Quality 4.0: a case study of printed circuit board[J]. Systems Engineering and Electronics, 2024, 46(5): 1682-1690.

Figures/Tables 4

References 42

1	LU Y . Industry 4.0: a survey on technologies, applications and open research issues[J]. Journal of Industrial Information Integration, 2017, 6, 1- 10. doi: 10.1016/j.jii.2017.04.005
2	刘虎沉, 王鹤鸣, 施华, 等. 质量4.0: 概念、基础架构及关键技术[J]. 科技导报, 2023, 41 (11): 6- 18.
	LIU H C , WANG H M , SHI H , et al. Quality 4.0: concepts, basic architecture, and key technologies[J]. Science & Technology Review, 2023, 41 (11): 6- 18.
3	周济. 智能制造——"中国制造2025"的主攻方向[J]. 中国机械工程, 2015, 26 (17): 2273- 2284.
	ZHOU J . Intelligent manufacturing——main direction of "made in China 2025"[J]. China Mechanical Engineering, 2015, 26 (17): 2273- 2284.
4	SU D , ZHANG L J , PENG H , et al. Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system[J]. Technological Forecasting and Social Change, 2023, 188, 122275. doi: 10.1016/j.techfore.2022.122275
5	刘虎沉, 王鹤鸣, 施华. 智能质量管理: 理论模型、关键技术与研究展望[J]. 中国管理科学, 2024, 32 (3): 287- 298.
	LIU H C , WANG H M , SHI H . Intelligent quality management: theoretical framework, key technologies, and research prospect[J]. Chinese Journal of Management Science, 2024, 32 (3): 287- 298.
6	SADER S , HUSTI I , DAROCZI M . A review of quality 4.0: definitions, features, technologies, applications, and challenges[J]. Total Quality Management & Business Excellence, 2022, 33 (9/10): 1164- 1182.
7	THEKKOOTE R . Enabler toward successful implementation of Quality 4.0 in digital transformation era: a comprehensive review and future research agenda[J]. International Journal of Quality & Reliability Management, 2022, 39 (6): 1368- 1384.
8	LIU H C , LIU R , GU X , et al. From total quality management to Quality 4.0: a systematic literature review and future research agenda[J]. Frontiers of Engineering Management, 2023, 10 (2): 191- 205.
9	何骁, 周波, 沈江华, 等. 国内印制电路板产品失效现状与改进[J]. 印制电路信息, 2023, 31 (4): 55- 60.
	HE X , ZHOU B , SHEN J H , et al. Failure status of domestic PCB products and suggestions for improvement[J]. Printed Circuit Information, 2023, 31 (4): 55- 60.
10	庹冰, 黄丽雯, 唐鑫, 等. 基于YOLOX-WSC的PCB缺陷检测算法研究[J]. 计算机工程与应用, 2023, 59 (10): 236- 243.
	TUO B , HUANG L W , TANG X , et al. Research on PCB defect detection algorithm based on YOLOX-WSC[J]. Computer Engineering and Applications, 2023, 59 (10): 236- 243.
11	DIAS A M , CARVALHO A M , SAMPAIO P . Quality 4.0: literature review analysis, definition and impacts of the digital transformation process on quality[J]. International Journal of Quality & Reliability Management, 2022, 39 (6): 1312- 1335.
12	YIN S , DING S X , XIE X , et al. A review on basic data-driven approaches for industrial process monitoring[J]. IEEE Trans.on Industrial Electronics, 2014, 61 (11): 6418- 28.
13	HU J W , CHEN P . Predictive maintenance of systems subject to hard failure based on proportional hazards model[J]. Reliability Engineering & System Safety, 2020, 196, 106707.
14	ZHANG Y , HUANG Y , SHAO W J , et al. Research on MCVE piston machining and process parameter optimization[J]. The International Journal of Advanced Manufacturing Technology, 2017, 93 (9): 3955- 3966.
15	PRASHAR A . Quality management in industry 4.0 environment: a morphological analysis and research agenda[J]. International Journal of Quality & Reliability Management, 2023, 40 (3): 863- 885.
16	薛塬, 臧冀原, 孔德婧, 等. 面向智能制造的产业模式演变与创新应用[J]. 机械工程学报, 2022, 58 (18): 303- 318.
	XUE Y , ZANG J Y , KONG D J , et al. Evolution and innovative implementation of industrial model for intelligent manufacturing[J]. Journal of Mechanical Engineering, 2022, 58 (18): 303- 318.
17	LIU Y K , GUO L , GAO H L , et al. Machine vision-based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: a review[J]. Mechanical Systems and Signal Processing, 2022, 164, 108068.
18	TAN H , VAN-DIJKEN S . Dynamic machine vision with retinomorphic photomemristor-reservoir computing[J]. Nature Communications, 2023, 14 (1): 2169.
19	JIANG P Y , CAO W . An RFID-driven graphical formalized deduction for describing the time-sensitive state and position changes of work-in-progress material flows in a job-shop floor[J]. Journal of Manufacturing Science and Engineering, 2013, 135 (3): 031009.
20	任明仑, 宋月丽. 大数据: 数据驱动的过程质量控制与改进新视角[J]. 计算机集成制造系统, 2019, 25 (11): 2731- 2742.
	REN M L , SONG Y L . Big data: new perspective of process quality control and improvement driven by data[J]. Computer Integrated Manufacturing Systems, 2019, 25 (11): 2731- 2742.
21	XIANG Q , XU L , LIU B , et al. Processing anomaly detection based on rough set and support vector machine[J]. Computer Integrated Manufacturing Systems, 2015, 21 (9): 2467- 2474.
22	JORDAN M I , MITCHELL T M . Machine learning: trends, perspectives, and prospects[J]. Science, 2015, 349 (6245): 255- 260.
23	HABBAB F Z , KAMPOURIDIS M . An in-depth investigation of five machine learning algorithms for optimizing mixed-asset portfolios including REITs[J]. Expert Systems with Applications, 2024, 235, 121102.
24	XU L D , WANG C , BI Z , et al. AutoAssem: an automated assembly planning system for complex products[J]. IEEE Trans.on Industrial Informatics, 2012, 8 (3): 669- 678.
25	HU S , YU B , QIAN C , et al. Nonorthogonal interleave-grid multiple access scheme for industrial internet of things in 5G network[J]. IEEE Trans.on Industrial Informatics, 2018, 14 (12): 5436- 5446.
26	CHEN J X , WANG W , ZHOU Y C , et al. Exploiting 5G and blockchain for medical applications of drones[J]. IEEE Network, 2021, 35 (1): 30- 36.
27	BHASKAR P , TIWARI C K , JOSHI A . Blockchain in education management: present and future applications[J]. Interactive Technology and Smart Education, 2021, 18 (1): 1- 17.
28	KAEWUNRUEN S , LIAN Q . Digital twin aided sustainability-based lifecycle management for railway turnout systems[J]. Journal of Cleaner Production, 2019, 228, 1537- 1551.
29	TANG W Q , YANG Z W , SONG Y Z . Selective interactive networks with knowledge graphs for image classification[J]. Knowledge-Based Systems, 2023, 278, 110889.
30	SINGH S A , DESAI K A . Automated surface defect detection framework using machine vision and convolutional neural networks[J]. Journal of Intelligent Manufacturing, 2023, 34, 1995- 2011.
31	MALGE P . PCB defect detection, classification and localization using mathematical morphology and image processing tools[J]. International Journal of Computer Applications, 2014, 87 (9): 40- 45.
32	WANG C C , JIANG B C , LIN J Y , et al. Machine vision-based defect detection in IC images using the partial information correlation coefficient[J]. IEEE Trans.on Semiconductor Manufacturing, 2013, 26 (3): 378- 384.
33	MCGUINNESS B , DUKE M , AU C K , et al. Measuring radiata pine seedling morphological features using a machine vision system[J]. Computers and Electronics in Agriculture, 2021, 189, 106355.
34	HASSAN F U , NGUYEN T , LE T , et al. Automated prioritization of construction project requirements using machine learning and fuzzy failure mode and effects analysis (FMEA)[J]. Automation in Construction, 2023, 154, 105013.
35	LUCCHI A , SMITH K , ACHANTA R , et al. Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features[J]. IEEE Trans.on Medical Imaging, 2012, 31, 474- 486.
36	马玉凤, 向南, 豆亚杰, 等. 军事系统工程中的知识图谱应用及研究[J]. 系统工程与电子技术, 2022, 44 (1): 146- 153. doi: 10.12305/j.issn.1001-506X.2022.01.19
	MA Y F , XIANG N , DOU Y J , et al. Application and research of knowledge graph in military system engineering[J]. Systems Engineering and Electronics, 2022, 44 (1): 146- 153. doi: 10.12305/j.issn.1001-506X.2022.01.19
37	WANG X Y , EL-GOHARY N . Deep learning-based relation extraction and knowledge graph-based representation of construction safety requirements[J]. Automation in Construction, 2023, 147, 104696.
38	XU Z G , DANG Y Z . Data-driven causal knowledge graph construction for root cause analysis in quality problem solving[J]. International Journal of Production Research, 2023, 61 (10): 3227- 3245.
39	WU D , SUN B , SHANG M S . Hyperparameter learning for deep learning-based recommender systems[J]. IEEE Trans.on Services Computing, 2023, 16 (4): 2699- 2712.
40	ZEB A , UL-HAQ A U , ZHANG D , et al. KGEL: a novel end-to-end embedding learning framework for knowledge graph completion[J]. Expert Systems with Applications, 2021, 167, 114164.
41	YANG Y , RAO Y L , YU M H , et al. Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation[J]. Neural Networks, 2022, 146, 1- 10.
42	LIU C , JIANG P Y , JIANG W L . Web-based digital twin modeling and remote control of cyber-physical production systems[J]. Robotics and Computer-Integrated Manufacturing, 2020, 64, 101956.

Intelligent defect detection based on Quality 4.0: a case study of printed circuit board

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 42

Related Articles 0

Recommended Articles

Metrics

Comments