Machine Learning Weld Tracking for Automatic Part Identification
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Solution Overview
Problem
Conventional weld monitoring systems face challenges in automatically identifying and tracking parts assembled via welds, requiring extensive manual setup and being unable to identify parts created prior to setup, while also lacking real-time tracking capabilities.
Innovation Solution
The implementation of machine learning techniques to analyze weld data, identify part characteristics, and construct models to automatically recognize parts assembled from welds, reducing the need for manual input and expertise in configuring tracking systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional weld monitoring systems are used to track parts, then weld data can be collected, but automatic part identification requires extensive manual setup and expertise
Solution Approach 1:
The system automatically identifies parts by analyzing weld data patterns without requiring manual operator input to define part boundaries or characteristics. The machine learning model self-configures by learning from historical weld data, eliminating the need for experts to manually set up tracking parameters for each part type.
Solution Approach 2:
The patent replaces manual configuration processes with automated machine learning algorithms that analyze weld data to automatically identify and track parts. This substitution of manual mechanical setup with intelligent automated analysis resolves the contradiction between automation extent and setup complexity.
2Adaptability or versatility
If conventional part tracking systems are implemented, then part tracking is possible, but systems cannot identify parts created prior to setup
Solution Approach 1:
The system performs preliminary learning by analyzing historical weld data in advance to build part identification models before actual tracking begins. This preliminary analysis of past weld data enables the system to immediately identify parts created prior to setup, eliminating the time loss associated with conventional systems that must be configured before they can track anything.
Solution Approach 2:
The patent cushions against the loss of time by pre-processing and storing weld data with associated metadata that enables immediate part identification upon system activation. This beforehand preparation ensures that no production time is lost to setup configuration, as the system can immediately track both historical and current parts.
3Productivity
If manual operator input is required for part tracking, then system configuration is possible, but real-time tracking capability is reduced
Solution Approach 1:
The system serves itself by automatically analyzing weld data and identifying parts without requiring continuous manual operator input. The machine learning model processes weld data in real-time autonomously, enabling both high productivity through real-time tracking and ease of operation by eliminating the need for operators to manually configure or monitor each part identification event.
Data Source
AI summary
Systems and methods for part tracking using machine learning techniques are described. In some examples a part tracking system analyzes feature characteristics related to one or more welds to identify, determine characteristics of, and/or label one or more parts repeatedly assembled by the welds. Identifying parts assembled from the welds may make it possible to do part based analytics (e.g., related to part quality, cost, production efficiency, etc.), as opposed to just weld based analytics, on past welding data. Additionally, identifying a part assembled from several welds results in an ordering of those several welds used to create the part, which can make it easier to compare/contrast similar welds across parts. Further, determining the characteristics of the parts can assist in configuring certain part tracking systems, thereby reducing the expertise, time, and personnel required.