solar cell quality inspection Julen Balzategui 1and Luka Eciolaza 1Mondragon Unibertsitatea June 2021 Abstract In industry, Deep Neural Networks have shown high defect detection rates surpassing other more traditional manual feature engineering based proposals. This has been achieved mainly through supervised training where a great amount of data is required in order
Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts.
This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed.
In this work, we experiment with the incremental few-shot learning proposed in [15] in the industrial context of solar cell inspection where we try to update a model that has been trained on three base defect classes (i.e., cracks, micro-cracks, and nger interruptions) and incorporate new defects (i.e., black spots and bad soldering) such that t...
Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, withnon-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification modelsfrom the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty
This work presents a methodology to develop a robust inspection system, targeting thesepeculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomalydetection model based on a Generative Adversarial Network (GAN) is employed.
The technique of weight imprinting in the context of solar cell quality inspection is explored where a network is trained on three base defect classes, and then it is incorporated into new defect classes using few samples, showing that this technique allows the network to extend its knowledge with regard to defect classes with few samples. In industry, Deep Neural
Glava Energy Center''s 2-day training on inspection of solar cell installations includes theory and practical training in the form of troubleshooting in an authentic environment in Glava Energy Center''s solar park. The course provides in-depth knowledge about inspection of solar cells and the general guidelines and regulations that exist. The
Abstract: Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification models from the
DOI: 10.3390/s21134361 Corpus ID: 232134897; Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network @article{Balzategui2021AnomalyDA, title={Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network}, author={Julen Balzategui
A further related approach involves the training of a CNN first to detect low-quality cells and then deriving feature vectors to perform bin classification. 3 Approach 3.1 Overview. We propose three sequential algorithms for solar cell quality inspection using measurement data and expert knowledge. First, we derive a comprehensive
Therefore, surface defect detection of solar cells plays a key role in controlling the quality of solar cell products during manufacturing process Based on the dataset, the three challenging problems about training and inspection of solar cell surface defect mainly include: (1) There are 6 types of defects in the dataset. The characteristics of each defect type are quite
A cell quality inspector plays a critical role in photovoltaic manufacturing by ensuring the quality and reliability of solar cells. Their responsibilities include conducting inspections, tests, and evaluations of cells to identify defects and non-conformities. They use various inspection tools and techniques to assess cell quality and
First the system can be applied for cell-level inspection in PV assembly lines to inspect solar cells manufactured on the assembly line. The second application is that module-level inspection can be used to assess large-scale PV modules thereby minimizing manual labour and saving time while maintaining a high level of accuracy.
However, these approaches usually result in less accurate models than those obtained with supervised training. In the case of solar cell inspection, anomaly detection approaches have been proposed in Qian et al. [34,43], where they train a Stacked Denoising AutoEncoder (SDAE) to extract features from defect-free samples using the sliding window
We propose three sequential algorithms for solar cell quality inspection using measurement data and expert knowledge. First, we derive a comprehensive representation of each solar cell by compressing the measurement data. We explain how this EDT can be derived only within a deep learning model in Section
In this manuscript, a pipeline to develop an inspection system for defect detection of solar cells is proposed. The pipeline is divided into two phases: In the first phase, a Generative
In this work, we explore the technique of weight imprinting in the context of solar cell quality inspection. This technique allows to incorporate new classes into the classification network using just a few samples. We tested the technique by first training a base network for the segmentation of three base defect classes and then
Inspection Solar cells. Glava Energy Center''s two day training on the inspection of solar power systems includes theory and practical training in the form of troubleshooting in an authentic environment at Glava Energy Center''s solar park. The course provides in-depth knowledge of solar cell inspection and the general guidelines and regulations
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