18152587. SENSOR FUSION FOR WELDING QUALITY MONITORING simplified abstract (Hitachi, Ltd.)
Contents
SENSOR FUSION FOR WELDING QUALITY MONITORING
Organization Name
Inventor(s)
Quan Zhou of West Bloomfield MI (US)
SENSOR FUSION FOR WELDING QUALITY MONITORING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18152587 titled 'SENSOR FUSION FOR WELDING QUALITY MONITORING
Simplified Explanation: The patent application involves using sensor data from a robotic welding process to predict internal and surface parameters of a weld seam, as well as the quality of the weld. The parameters are then used to modify the welding process for better results.
Key Features and Innovation:
- Utilizes thermal imaging data from sensor data in a robotic welding process.
- Executes a machine learning model to predict internal and surface parameters of a weld seam.
- Predicts the quality of the weld based on the predicted parameters.
- Modifies parameters of the welding process based on the predicted quality and parameters.
Potential Applications: This technology can be applied in various industries that use robotic welding processes, such as automotive, aerospace, and manufacturing.
Problems Solved: This technology addresses the need for real-time monitoring and adjustment of welding processes to ensure high-quality welds.
Benefits:
- Improves the quality of welds in robotic welding processes.
- Enhances efficiency by predicting and adjusting parameters in real-time.
- Reduces the need for manual intervention in the welding process.
Commercial Applications: Potential commercial applications include automated welding systems for industries like automotive manufacturing, shipbuilding, and construction.
Prior Art: Readers can explore prior art related to machine learning in welding processes, robotic welding technology, and quality control in welding.
Frequently Updated Research: Researchers are continuously exploring advancements in machine learning models for predicting weld quality and parameters in robotic welding processes.
Questions about Robotic Welding Technology: 1. How does machine learning improve the quality of welds in robotic welding processes? 2. What are the key parameters that can be predicted using sensor data in a welding process?
Original Abstract Submitted
Systems and methods described herein involve intaking sensor data associated with an arc weld from a robotic welding process, the sensor data involving thermal imaging data; executing a machine learning model on the sensor data, the machine learning model configured to output predicted internal parameters of a weld seam of the arc weld, predicted surface parameters of the weld seam of the arc weld, and a predicted quality of the arc weld associated with the predicted internal parameters and the predicted surface parameters; and modifying parameters of the robotic welding process of the arc weld based on the output predicted quality, the output predicted internal parameters, and the predicted surface parameters.
