AI Workpiece Checking for Systematic Fault Detection
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Solution Overview
Problem
Conventional methods for checking workpieces during or after manufacturing do not allow for a meaningful and/or 100% reliable assessment of systematic production faults, leading to inefficiencies in workpiece quality and production processes.
Innovation Solution
A method and facility that utilize artificial intelligence to determine workpiece and facility parameters, creating workpiece-specific data sets for efficient quality assessment and process optimization, including automatic implementation of corrective measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional checking methods are used to inspect workpieces, then the checking process can be performed, but the reliability of detecting systematic production faults is insufficient
Solution Approach 1:
The patent segments the checking process into multiple independent checking units, each responsible for specific parameters. Multiple checking stations are distributed along the production line to collect data from different stages, enabling comprehensive and reliable detection of systematic faults through divided responsibility and localized expertise.
Solution Approach 2:
The patent transitions from traditional single-point inspection to multi-dimensional data collection by gathering workpiece parameters, facility parameters, and environmental parameters across multiple stations and time points. This dimensional expansion enables more reliable detection of systematic production faults through comprehensive data analysis.
2Reliability
If comprehensive workpiece and facility parameters are collected and analyzed, then quality assessment reliability improves, but the complexity of the checking system increases
Solution Approach 1:
The patent implements a centralized control system that performs multiple functions: collecting data from various checking units, storing workpiece and facility parameters, analyzing quality trends, and controlling treatment facilities. This multi-functional approach consolidates complexity into a single coordinating system rather than distributing it across multiple independent systems.
Solution Approach 2:
The control system acts as an intermediary between checking units and treatment facilities, mediating data flow and control signals. It collects comprehensive parameters from multiple sources, processes this information, and translates it into actionable control decisions, thereby managing system complexity through a central coordinating layer.
3Productivity
If manual checking methods are used, then the checking process is simple to implement, but productivity and detection efficiency are reduced
Solution Approach 1:
The checking system performs self-service through automated data collection, analysis, and control functions. Checking units automatically measure workpiece parameters, the control system autonomously analyzes data trends, and the system self-regulates by sending control signals to treatment facilities, eliminating the need for continuous manual intervention while maintaining high productivity.
Solution Approach 2:
The patent implements continuous feedback loops where checking units monitor workpiece parameters, the control system analyzes this data in real-time, and control signals are automatically sent back to treatment facilities to adjust processing parameters. This closed-loop feedback system enables high-speed automated operation with immediate quality correction capabilities.
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.