ML Process Sequencing for Real-Time Manufacturing Optimization
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Solution Overview
Problem
Existing manufacturing process optimization systems struggle to reflect the know-how of experienced workers in real-time, leading to inefficiencies and defects, as they require manual data collection and are difficult to apply directly to the work site, limiting productivity improvements.
Innovation Solution
An artificial intelligence-based process optimization system that includes a reading module using machine learning to evaluate unit processes in different sequences, collect execution data, and determine optimal execution scenarios based on criteria like time and defect rate, generating real-time instruction data for improved process efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual data collection and inspection methods are used, then workers can reflect their know-how in process optimization, but the system cannot determine optimal process sequences in real-time without interrupting production
Solution Approach 1:
The patent replaces manual data collection and inspection methods with an automated machine learning-based system. The reading module automatically captures process data, and the determination module uses machine learning algorithms to analyze this data and determine optimal process sequences, eliminating the need for manual intervention while maintaining the ability to reflect worker know-how through trained models
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically learn from historical process data and execution results, continuously improving its ability to determine optimal process sequences without requiring manual reprogramming or intervention. The system serves itself by automatically collecting data, analyzing performance, and optimizing processes
2Ease of manufacture
If theoretical regression models are used to propose process sequences, then process optimization can be attempted, but the models are difficult to apply directly to the work site
Solution Approach 1:
The patent replaces theoretical regression models with a machine learning-based determination module that processes actual execution data from the work site. This substitution enables direct applicability by using real-world data and automated decision-making algorithms that can be immediately implemented without theoretical abstractions
Solution Approach 2:
The system implements continuous feedback by collecting execution data from actual process operations, analyzing this data through the machine learning model, and using the results to determine and adjust optimal process sequences. This closed-loop feedback mechanism ensures the system adapts to actual work site conditions and maintains direct applicability
3Reliability
If sampling and inspection are performed after work completion, then quality control can be achieved, but production must be stopped and manual data collection is required
Solution Approach 1:
The patent enables continuous quality control by implementing real-time monitoring and analysis of process execution data. The reading module continuously collects data during production, and the determination module continuously analyzes this data to maintain quality standards without interrupting the production flow, eliminating the need for stop-and-inspect cycles
Solution Approach 2:
The system replaces manual sampling and inspection with automated machine learning-based analysis of execution data. This substitution eliminates the need for physical inspection activities and manual data collection, maintaining quality control while keeping production continuous and uninterrupted
Data Source
AI summary
An artificial intelligence-based process optimization method includes: executing one or more unit processes in different sequences, wherein, in the unit processes, an entire process for manufacturing a product is executed in a series of sequences, evaluating each of the unit processes in accordance with an evaluation criterion by a reading module while each execution progresses, collecting execution data generated by cumulatively evaluating the unit processes in sequence, and transmitting the execution data to a determination module; and generating instruction data as the execution data for an optimal execution determined among a plurality of executions in which the unit processes are executed in different sequences by the determination module, based on the execution data of the reading module.