Welding Assistance Feedback for Stable Manual Weld Quality
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Solution Overview
Problem
Existing technologies for improving welding quality, such as those disclosed in Patent Document 1, are difficult to apply to welding work performed by human welders, leading to instability in welding quality and an increased occurrence of defects.
Innovation Solution
A welding assistance device that includes an acquisition unit to collect welding data, a determination unit to calculate abnormality using a learned model, an extraction unit to identify an optimal condition range, a setting unit to set parameter recommendations, and an output unit to display assistance information, thereby stabilizing welding quality and reducing defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automatic welding technology is used, then welding quality is improved, but it cannot be applied to manual welding work by welders
Solution Approach 1:
The patent introduces a welding assistance device as an intermediary system between the welder and the welding work. This device includes a learning model that analyzes welding data and provides real-time guidance to the welder, enabling manual welding to achieve quality levels comparable to automatic welding without requiring full automation of the welding process itself.
Solution Approach 2:
The system implements a feedback mechanism where welding data is continuously collected, analyzed by the learning model, and used to generate guidance information that is returned to the welder. This closed-loop feedback enables manual welding operations to be corrected and optimized in real-time, bridging the quality gap between manual and automatic welding.
2Manufacturing precision
If welding assistance is provided to welders, then welding quality is stabilized, but system complexity increases
Solution Approach 1:
The welding assistance device is designed to perform multiple functions using a single integrated system: it collects welding data, constructs learning models, performs abnormality detection, extracts optimal conditions, and provides guidance information. This multi-functionality reduces the need for multiple separate systems and manages complexity through consolidation.
Solution Approach 2:
The system employs a learning model that automatically learns from welding data and improves its own performance over time. The model autonomously identifies abnormal patterns and extracts optimal welding conditions without requiring manual programming or frequent external adjustments, enabling the system to serve itself and reduce operational complexity.
Data Source
AI summary
A welding assistance device includes an acquisition unit configured to acquire welding data including a plurality of parameters indicating a welding state, a determination unit configured to calculate an abnormality degree of the welding data based on a learning model constructed by learning normal data including welding data collected during a period in which the welding state is normal, and the welding data acquired by the acquisition unit, an extraction unit configured to extract, based on the welding data, part of the normal data of the learning model as an optimal condition range, a setting unit configured to set a recommendation range of a value of at least one parameter included in the welding data based on the optimal condition range, and an output unit configured to output assistance information including the recommendation range.