Industrial Process Quality Control via Signal Energy Analysis
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Solution Overview
Problem
Current quality control methods for industrial processes, such as laser-welding, are inefficient due to reliance on offline skilled inspections or partial automatic detection methods that are sensitive to machine settings and result in significant material waste and time expenditure.
Innovation Solution
A method involving discrete wavelet transform and Fourier analysis to compare transformed reference and real signals, calculating energies and time-frequency distributions to identify defects, allowing for on-line and real-time detection without the need for skilled operators or extensive reference signal creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline inspection by skilled staff is used, then defect detection accuracy is improved, but productivity decreases and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical inspection by skilled staff with an automated optical-electronic inspection system that uses sensors, signal processing units, and computer algorithms to detect and classify defects automatically, thereby maintaining high accuracy while dramatically improving productivity
Solution Approach 2:
The system enables self-service inspection where the automated system performs defect detection and classification without requiring skilled operators, using pre-stored reference signals and automated comparison algorithms to independently assess weld quality
2Productivity
If automatic detection methods with sensors are used, then productivity is improved, but measurement precision decreases due to sensitivity to machine settings
Solution Approach 1:
The patent transforms the inspection approach by changing from direct signal comparison to frequency-domain analysis using Fourier transforms and wavelet transforms, converting time-domain signals into frequency representations that are invariant to machine setting variations, thereby maintaining precision while preserving automated productivity
Solution Approach 2:
The patent transitions from analyzing signals in the time domain to analyzing them in the frequency domain through Fourier and wavelet transforms, adding a dimensional transformation that eliminates sensitivity to machine settings while maintaining defect detection capability
3Measurement precision
If reference signals are created from samples of good-quality welds, then measurement precision is improved, but loss of time and material increases
Solution Approach 1:
The patent performs preliminary action by pre-storing reference signals representing both good-quality and defective welds in a database during system setup, allowing rapid automated comparison during actual inspection without requiring time-consuming manual reference creation for each inspection case
Solution Approach 2:
The patent uses copying by creating digital replicas of reference weld signals (both good and defective) and storing them as reference patterns, which can be repeatedly used for comparison without requiring physical sample welds to be recreated each time
4Device complexity
If simple block-based signal splitting is used, then device complexity is reduced, but measurement precision decreases to very approximate detection
Solution Approach 1:
The patent replaces simple block-based signal processing with advanced signal processing techniques including Fourier transforms and wavelet transforms, substituting mathematical transformation methods that provide frequency-domain analysis capability and significantly improve defect detection precision while maintaining computational efficiency
Data Source
AI summary
A method for controlling the quality of an industrial process, of the type that comprises the steps of: providing one or more reference signals for the industrial process; acquiring one or more real signals that are indicative of the quality of said industrial process; and comparing said one or more reference signals with said one or more real signals in order to identify defects in said industrial process. According to the invention, the method moreover comprises the operations of: obtaining a transformed signal from said reference signal; obtaining a transformed signal from said real signal; and calculating energies of said transformed reference signal and said real signal, respectively, said comparison operation comprising: comparing with one another said energies of said transformed reference signal and said transformed real signal, respectively, in order to extract corresponding time-frequency distributions for selected frequency values; calculating energies of said time-frequency distributions; and comparing the energies of said time-frequency distributions with threshold values in order to identify energy values associated to defects.