Welding Standoff Monitoring Using ML Reflection Separation
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Solution Overview
Problem
Automated welding processes face challenges in accurately determining the standoff distance due to reflections from high-reflective metals, leading to reduced precision and quality issues in workpiece manufacturing, especially in RPD® additive manufacturing systems, where laser line scanners are hindered by noise and reflections from plasma arcs.
Innovation Solution
A computer-implemented method using a machine-learned model, such as a convolutional neural network, processes optical signal data from a workpiece to distinguish desired reflections from unwanted ones, enabling precise determination of geometric properties and standoff distance, thereby improving manufacturing precision and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the intensity of the laser light is increased to improve the signal-to-noise ratio, then the signal quality improves, but unwanted reflections from the metal surface increase, impairing the signal quality again
Solution Approach 1:
The system dynamically adjusts the laser light intensity based on real-time detection of reflection levels and signal quality. The controller modulates the laser intensity to maintain optimal signal-to-noise ratio while preventing excessive reflections that would degrade measurement precision.
Solution Approach 2:
The system implements a feedback loop where the laser line scanner continuously monitors the quality of the reflected signal from the workpiece surface. Based on this feedback, the controller adjusts the laser intensity in real-time to optimize the signal-to-noise ratio while avoiding the harmful effect of excessive reflections.
2Manufacturing precision
If laser line scanner is used to determine standoff distance in real-time, then manufacturing precision improves, but the plasma arc emission creates noise that reduces measurement accuracy
Solution Approach 1:
The system introduces an intermediary filtering mechanism that separates the desired laser reflection signal from the harmful plasma arc noise. The controller uses signal processing techniques to identify and extract the laser line scanner signal from the composite signal containing both laser reflections and plasma arc emissions, enabling accurate standoff distance measurement despite the noisy environment.
3Manufacturing precision
If automated welding system performs high-precision work, then manufacturing quality improves, but the system complexity increases due to multiple monitoring and control components
Solution Approach 1:
The controller is designed as a multi-functional device that performs multiple tasks: it controls the laser line scanner operation, processes the optical signal data, determines standoff distance, identifies plasma arc interference, and adjusts welding parameters. This universal controller reduces overall system complexity by consolidating multiple functions into a single integrated component.
Solution Approach 2:
The system merges the monitoring and control functions into a unified process. The laser line scanner, plasma arc detector, and welding controller are integrated to work as a coordinated system, where the same hardware components serve multiple purposes and the control algorithm simultaneously performs signal processing, measurement, and adjustment functions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enhances manufacturing precision, reduces defects, and allows for real-time control of the welding process, resulting in higher-quality and automated manufacturing processes.
Implementation Method 1
a laser line scanner profile provides a three-dimensional profile measurement
Implementation Method 2
the intensity of the laser light may be increased. However, the interaction of the strong laser light with the metal surface may introduce unwanted reflections
Data Source
AI summary
A computer-implemented method for monitoring a workpiece being manufactured using an automated welding system. These technologies can real-time monitor, read, or interrogate a workpiece or a substrate on which the workpiece is positioned, as the workpiece is moved past a directed energy source, or vice versa. The method can be used with an automated welding system for standoff distance monitoring and control, which can be responsive, dynamic, and in real-time. These technologies can use a feedback controller to responsively and dynamically control the standoff distance in real-time based on data from the standoff distance measurement system.