Primary Authors: Nishaanthini Gnanavel and Genevieve Richards
Effective product quality control is of utmost importance in the manufacturing industry. The presence of defective components can have adverse effects on various aspects, including escalating production costs, compromising product quality, diminishing product longevity, and leading to substantial resource wastage. However, by promptly identifying defects and implementing suitable measures, significant reductions in operational expenses and quality-related expenditures can be achieved.
To mitigate these expenses, one possible approach is to conduct manual inspections during the production process. However, this method is time-consuming, expensive, and can lead to production delays, ultimately impacting market responsiveness. To address this challenge, the implementation of a machine vision defect detection system becomes imperative. Such a system has the potential to decrease production costs by minimizing the quantity of rejected and reprocessed faulty materials. It also aids in minimizing wastage of labor, materials, and machine time, reducing energy consumption, and facilitating the identification of underlying causes for defects.
Our customer data science team, recently developed a solution to detect different types of defects on manufacturing products (steel sheets) using deep learning techniques and deployed the model in MLOps. The solution is then packaged in an AI application which can be accessed by different stakeholders.
H2O’s AI cloud offers a diverse range of components that facilitate the complete lifecycle of developing, deploying, and utilizing machine learning or deep learning models. In this particular solution, we have leveraged three crucial components:
Dataset: For model training purposes, a dataset comprising surface images of hot-rolled steel sheets was utilized. To prepare the dataset, the images were converted to grayscale and subsequently labeled into six distinct categories.
Model: By employing the H2O Hydrogen Torch model, numerous image classification models were trained, incorporating diverse backbones like efficientnets, resnets, mobilenets, and VGG architectures. These models were integrated together to enhance overall performance. The selected loss function for training was CrossEntropy, accompanied by the Adam W optimization algorithm.
Deployment: Through the utilization of H2O MLOps, the scoring pipeline generated from the Hydrogen Torch AI engine was deployed as a standalone model. This deployment offered a REST endpoint and allowed inference calls to be made using CURL request parameters. The REST endpoint was seamlessly integrated into the AI application, providing configurability. This implies that in the future, if there is a need to modify the deployment options from single to batch, or single to champion/challenger model, or even enable AB testing mode, all such options can be supported effortlessly.
Application: The entire solution, model, and the deployment were wrapped in a Wave Application to view the results, set alerts, monitor the predictions etc. The wave app has a range of functionalities including:
The dashboard features various useful visualizations relevant to the scoring results. The Defect Classification Analysis by image names section of the dashboard displays both the raw input image of the hot rolled steel strips and a spectral image that highlights the area of the defect more clearly to the user. The spectral image is generated using OpenCV Saliency, which helps the user analyze the defect in that strip more effectively.
The Product Defect Classification app provides a reliable and efficient solution to the problem of product quality control in the manufacturing industry. With the ability to detect defects, classify them and provide timely and accurate results, this application helps in reducing operational and quality related costs by minimizing rejected and reprocessed defective materials. Ultimately, our Product Defect Classification app is a valuable tool for the manufacturing industry to improve product quality, reduce costs, and increase overall efficiency.
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