Stator Weld Inspection Using Digital Twin Epoxy Imaging
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Solution Overview
Problem
Conventional destructive and non-destructive testing techniques for evaluating weld quality and epoxy thickness in electric motor stators are time-consuming and prone to incorrect evaluations, leading to acceptance of defective welds or rejection of proper ones.
Innovation Solution
A non-destructive testing method using image processing routines, including pre-weld and post-weld image data analysis, and a digital twin generation based on convolutional neural networks to assess weld and epoxy characteristics, enabling accurate determination of epoxy thickness and distribution without physical destruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual and visual inspections are performed to evaluate weld quality and epoxy properties, then evaluation can be conducted, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical inspection methods with an automated optical imaging system. Image sensors capture weld and epoxy characteristics, and image processing routines automatically analyze the captured images to evaluate quality metrics, eliminating the need for time-consuming manual visual inspections while maintaining or improving measurement precision.
Solution Approach 2:
The patent creates digital copies (images) of the weld and epoxy structures using image sensors. These digital representations are then processed through algorithms to extract quality information, allowing rapid repeated analysis without physical contact or destruction of the original component, significantly reducing testing time.
2Reliability
If conventional non-destructive testing techniques are used, then weld quality can be assessed, but incorrect evaluations occur leading to acceptance of defective welds or rejection of proper ones
Solution Approach 1:
The patent implements a feedback mechanism where image processing routines continuously analyze weld characteristics and provide quantitative measurements. The system compares actual weld properties against predefined quality criteria, providing objective feedback that reduces human error in judgment and improves the reliability of quality assessments.
Solution Approach 2:
The patent transforms qualitative visual assessments into quantitative parameter measurements through image processing. By converting image data into measurable parameters such as weld width, depth, and uniformity, the system enables precise objective evaluation that eliminates subjective errors in quality determination.
3Measurement precision
If destructive testing techniques are used to evaluate stator quality, then accurate assessment can be obtained, but the stator is damaged and cannot be reused
Solution Approach 1:
The patent replaces destructive mechanical testing methods with non-destructive optical imaging and image processing techniques. The system captures images of the weld and epoxy structures and uses computational algorithms to assess quality without physically damaging the stator, maintaining both measurement precision and component reusability.
Solution Approach 2:
The patent creates digital copies of the stator's critical features through imaging, allowing exhaustive analysis of the original component without physical alteration. This digital replication enables repeated testing and evaluation while preserving the integrity of the actual stator for potential reuse.
Data Source
AI summary
A method includes obtaining pre-weld image data of the electric motor stator from one or more image sensors; performing a welding process in response to obtaining the pre-weld image data, obtaining post-weld image data of the electric motor stator from the one or more image sensors in response to performing the welding process, obtaining epoxy image data of the electric motor stator from the one or more image sensors in response to obtaining the post-weld image data, performing a difference-based image processing routine based on the post-weld image data and the epoxy image data to generate a digital twin of the electric motor stator, and determining one or more epoxy characteristics of the electric motor stator based on the digital twin.