Classification Model Training for Reliable Manufacturing Process Control
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Solution Overview
Problem
The effectiveness of manufacturing process control using classification models is hindered by the variability and accuracy of user-labeled training sets, requiring significant manual effort for quality assurance and supervision.
Innovation Solution
A computer-implemented method for training a classification model that includes generating a training set, acquiring labels from agents, determining labeling metrics, and training a labeling score model to optimize the accuracy and efficiency of labeling, thereby reducing manual interaction and improving data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling by users is performed to generate training sets, then the classification model can be trained, but the labeling quality varies over time and between users, reducing reliability
Solution Approach 1:
The system enables self-service through automated quality assessment. The labeling score model automatically evaluates labeling quality without requiring manual supervision, allowing the system to self-regulate and maintain consistent labeling standards over time.
Solution Approach 2:
The system implements feedback by calculating labeling scores based on multiple metrics (labeling speed, quality, consistency) and using these scores to adjust future labeling tasks. This feedback loop ensures continuous improvement and maintains reliability without constant manual intervention.
2Measurement precision
If extensive manual supervision is applied to ensure training set quality, then labeling accuracy improves, but the complexity and effort of the process increases significantly
Solution Approach 1:
The labeling score model acts as an intermediary between labelers and supervisors. It automatically assesses labeling quality using multiple metrics and provides objective feedback, replacing the need for complex manual supervision systems while maintaining high labeling accuracy.
Solution Approach 2:
The system replaces mechanical manual supervision with an automated computational system. The labeling score model uses algorithmic assessment based on labeling metrics to evaluate quality, substituting human supervisory effort with automated technical means.
3Measurement precision
If multiple labeling metrics are calculated to assess agent performance, then the accuracy of quality assessment improves, but the computational effort and processing time increases
Solution Approach 1:
The system applies partial action by selectively calculating labeling metrics based on the specific context and requirements. Not all metrics are computed for every labeling task - the system adjusts the level of assessment based on the importance and complexity of the labeling work, optimizing computational resource usage.
Data Source
AI summary
Controlling a manufacturing process by a computer-generated classification model is provided. This is combined with a reward system based on a distributed ledger and smart contracts. The classification model is trained by: Providing data entities being indicative of a property of a manufacturing of a product. Acquiring labels for each of the data entities from an agent. Determining labeling metrics based on the acquiring of the agent. Training the classification model, wherein the training set includes the data entities and their labels. Validating the trained classification model yielding a classifier score. Training a labeling score model based on the data entities, the respective labels, the labeling metrics and the classifier score. Determining a labeling score for the agent based on the labeling score model, the labels and the set of labeling metrics.