End-to-end deep learning framework for printed circuit board manufacturing defect classification
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
End-to-end deep learning framework for printed circuit board manufacturing defect classification
Authors
Keywords
-
Journal
Scientific Reports
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-07-22
DOI
10.1038/s41598-022-16302-3
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Transformers in Vision: A Survey
- (2022) Salman Khan et al. ACM COMPUTING SURVEYS
- Advancing zero defect manufacturing: A state-of-the-art perspective and future research directions
- (2022) Daryl Powell et al. COMPUTERS IN INDUSTRY
- MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface
- (2022) Zexuan Guo et al. SENSORS
- Small object detection method with shallow feature fusion network for chip surface defect detection
- (2022) Haixin Huang et al. Scientific Reports
- Machine vision based online detection of PCB defect
- (2021) Zhichao Liu et al. MICROPROCESSORS AND MICROSYSTEMS
- Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework
- (2021) Paul-Arthur Dreyfus et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research
- (2019) Foivos Psarommatis et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
- A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
- (2019) Yalin Wang et al. ISA TRANSACTIONS
- Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application
- (2019) Te Han et al. ISA TRANSACTIONS
- Support vector machines based non-contact fault diagnosis system for bearings
- (2019) Deepam Goyal et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks
- (2019) Gaowei Xu et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
- (2019) Angelos Angelopoulos et al. SENSORS
- An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
- (2019) Yu He et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- A Deep Learning framework for simulation and defect prediction applied in microelectronics
- (2019) Nikolaos Dimitriou et al. SIMULATION MODELLING PRACTICE AND THEORY
- Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
- (2019) Xiaofeng Yuan et al. IEEE Transactions on Industrial Informatics
- Focal loss for dense object detection
- (2018) Tsung-Yi Lin et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Object Detection Networks on Convolutional Feature Maps
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise
- (2017) Hoyeop Lee et al. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
- The Impact of Mass Customisation on Manufacturing Trade-offs
- (2010) Brian Squire et al. PRODUCTION AND OPERATIONS MANAGEMENT
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started