Managing a manufacturing process based on heuristic determination of predicted damages
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Solution Overview
Problem
Manufacturing processes, such as textile weaving, face challenges in managing defects during production, as existing methods fail to effectively predict and mitigate defects in real-time, leading to suboptimal product quality and classification issues.
Innovation Solution
A heuristic method and system that uses AI and IoT sensors to dynamically detect defects, compute defect occurrence counts, and determine damage mitigation measures, allowing for real-time adjustments to manufacturing equipment to maintain quality specifications, thereby reducing defects and improving output quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time defect detection and mitigation is implemented, then product quality is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary defect prediction by analyzing historical manufacturing data and current sensor readings to forecast potential defects before they occur. This allows proactive mitigation measures to be taken, improving product quality while managing system complexity through advance planning rather than reactive complex interventions
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the manufacturing process is constantly monitored, compared against quality specifications, and used to dynamically adjust manufacturing parameters. This closed-loop feedback mechanism maintains high product quality through real-time corrections without requiring overly complex manual intervention systems
2Productivity
If dynamic computation of damage mitigation range is performed, then manufacturing velocity is improved, but measurement precision requirements increase
Solution Approach 1:
The system computes damage mitigation ranges dynamically by focusing computational resources on the most critical defect parameters and high-risk manufacturing stages. Rather than performing exhaustive analysis on all parameters continuously, the system applies partial computation strategies that maintain manufacturing velocity while achieving sufficient precision for quality control decisions
Data Source
AI summary
A method, system and computer program product for a heuristic determination of in-process damage class control to manage expected output product category. The heuristic technique determines the predicted damages and their ranges while keeping the initial expected defects, the respective classes and range of defect and mitigation. The method dynamically computes a damage mitigation range of operation while being within the overall constraints and completes the computation in smaller number of loops being run at the edge computers so that the manufacturing equipment can operate at a higher velocity for higher quality of the output. The method includes a step of reducing error of the co-efficient and damage counts. An Internet of Things (IoT) based robot is used to mitigate the damages in the manufacturing steps to ensure that the output class of the product remains what was expected at the start despite damages and mitigation measures.