Workpiece Checking With AI Feedback for Systematic Production Faults
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Solution Overview
Problem
Conventional methods for checking workpieces during or after manufacture do not allow for reliable detection of systematic production faults, leading to inefficiencies in optimizing workpiece quality and production processes.
Innovation Solution
A method utilizing artificial intelligence (AI) to suggest and implement process optimizations by determining and processing workpiece and facility parameters, creating workpiece-specific data sets for improved quality verification and optimizing production processes, including the use of sensors for contactless measurements and simulation data to assess treatment results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional checking methods are used, then checking can be performed during or after manufacture, but reliable detection of systematic production faults is not achieved
Solution Approach 1:
The system performs preliminary actions by determining workpiece parameters and facility parameters before final quality assessment, creating comprehensive data sets that enable reliable detection of systematic faults. The checking facility collects and processes data during the manufacturing process to identify patterns before defects become critical.
Solution Approach 2:
The invention implements feedback mechanisms where checking results are used to adjust facility parameters and optimize production processes. The system continuously monitors workpiece parameters and provides feedback to control systems, enabling real-time corrections and improving detection reliability for systematic faults.
2Measurement precision
If comprehensive workpiece and facility parameters are determined and processed, then workpiece quality verification is improved, but checking complexity increases
Solution Approach 1:
The checking facility is designed as a universal system that can determine multiple workpiece parameters and facility parameters simultaneously. The same facility performs diverse functions including geometric measurements, material property assessments, and process parameter monitoring, reducing the need for separate specialized devices.
Solution Approach 2:
The invention introduces intermediary components such as sensors, data processing units, and control systems that mediate between the workpiece/facility and the checking system. These intermediaries simplify the interface and data collection process, making the overall system more manageable despite the comprehensive nature of measurements.
3Productivity
If AI is used to suggest and implement process optimizations, then production efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service capabilities where AI algorithms automatically analyze workpiece and facility parameters, suggest optimizations, and implement process adjustments without extensive human intervention. The checking facility autonomously generates quality reports and triggers process corrections, reducing manual complexity while maintaining high productivity.
Solution Approach 2:
The invention utilizes parameter changes as a core mechanism for AI-driven optimization. The system dynamically adjusts facility parameters based on analyzed data, transforming static production processes into adaptive systems that automatically optimize for quality and efficiency, thereby improving productivity while managing complexity through systematic parameter management.
Data Source
AI summary
In order to provide a checking facility for checking workpieces and also a treatment facility for treating workpieces, which enable efficient and reliable quality optimisation, it is proposed that workpiece parameters are detected, for example by means of an automatic checking station, and a workpiece-specific data set is created on this basis and/or from facility parameters.