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ICACT20250115 Question.1
Questioner: luabt@viettel.com.vn    2025-02-19 오후 7:52:55 		ICACT20250115 Answer.1
Answer by Auhor arifulislamro@gmail.com   2025-02-19 오후 7:52:55
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Which step of the welding process that applied your classification. We saw that it has select thickness step, what is the different between normal number round method with classification that auto select the thickness for next steps? The classification is applied in the thickness selection step of the welding process. In traditional methods, thickness selection is typically done using a normal number rounding method, where a predefined set of thickness values is available, and the closest standard value is selected based on manual input or simple rounding rules. However, in a classification-based approach, machine learning or AI-driven models analyze various factors such as material type, welding parameters, and previous data to automatically determine the optimal thickness for the next steps. This automated selection ensures more precise and efficient decision-making, reducing errors and improving welding quality by adapting to specific conditions rather than relying on fixed rounding rules.
ICACT20250115 Question.2
Questioner: luabt@viettel.com.vn    2025-02-19 오후 7:53:41 		ICACT20250115 Answer.2
Answer by Auhor arifulislamro@gmail.com   2025-02-19 오후 7:53:41
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The performance of your method, the recall and precision, it calculated based on which output quality metric, how meaningful it affect to the wellding process. If performance increased, which characteristic of welding product is improved? The performance of the proposed method, particularly recall and precision, is calculated based on an output quality metric that evaluates the accuracy of defect detection, weld consistency, or classification of welding parameters. These metrics are crucial in determining how well the method identifies correct welding conditions while minimizing false detections. A higher recall ensures that more defects or inconsistencies are detected, reducing the chances of faulty welds going unnoticed. Precision, on the other hand, ensures that correct predictions are made without unnecessary false alarms. An improvement in performance directly enhances the overall welding process by increasing the reliability of defect detection, optimizing material usage, and reducing rework or rejection rates. As a result, the final welding product benefits from improved strength, durability, and surface quality, ensuring higher manufacturing efficiency and compliance with industry standards.