17933495. SYSTEMS AND METHODS FOR PREDICTING MICROHARDNESS PROPERTIES OF WELDS simplified abstract (GM GLOBAL TECHNOLOGY OPERATIONS LLC)

SYSTEMS AND METHODS FOR PREDICTING MICROHARDNESS PROPERTIES OF WELDS

Organization Name

GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor(s)

Ying Lu of Novi MI (US)

Junjie Ma of Troy MI (US)

Hui-ping Wang of Troy MI (US)

Mitchell Poirier of Owosso MI (US)

Baixuan Yang of Canton MI (US)

Jay Oswald of Chandler AZ (US)

SYSTEMS AND METHODS FOR PREDICTING MICROHARDNESS PROPERTIES OF WELDS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17933495 titled 'SYSTEMS AND METHODS FOR PREDICTING MICROHARDNESS PROPERTIES OF WELDS

Simplified Explanation

The abstract describes a system and method for predicting microhardness properties of a weld joint between two workpieces based on temperature data collected during the welding process.

• The system includes a processor programmed to receive temperature data from various points of the weld, determine peak temperature values and cooling rate values for each point, predict a 3D distribution of microhardness values using machine learning methods, and generate display data based on the predicted values.

Potential Applications

This technology can be applied in industries such as automotive, aerospace, and construction where the quality of weld joints is critical for the performance and durability of the final product.

Problems Solved

This technology helps in predicting the microhardness properties of weld joints accurately, which can prevent potential failures or defects in the welded components due to improper hardness distribution.

Benefits

- Improved quality control in welding processes - Enhanced structural integrity of welded components - Cost savings by reducing the need for post-welding inspections and rework

Potential Commercial Applications

"Predictive Microhardness Analysis System for Weld Joints: Applications and Benefits"

Possible Prior Art

There may be prior art related to machine learning applications in predicting material properties based on temperature data in welding processes. However, specific examples would need to be researched to provide accurate information.

How does this technology compare to traditional methods of predicting microhardness properties in weld joints?

This article does not provide a direct comparison between this technology and traditional methods of predicting microhardness properties in weld joints. It would be beneficial to understand the advantages and limitations of this new approach compared to existing techniques.

What are the potential limitations or challenges in implementing this predictive microhardness analysis system in industrial settings?

The article does not address the potential obstacles or difficulties that may arise when implementing this technology in real-world industrial applications. Understanding the practical challenges can help in assessing the feasibility and scalability of this system.

Original Abstract Submitted

Systems and methods are provided for predicting microhardness properties of a weld that defines a weld joint between at least two workpieces. The system includes a processor programmed to: receive temperature data that includes temperature values each attributed to a corresponding one of a plurality of points of the weld at corresponding times during a welding process used to produce the weld, determine peak temperature values and cooling rate values for each of the points of the weld based on the temperature values, predict a three-dimensional (3D) distribution of microhardness values of the weld based on a machine learning method that evaluates the peak temperature values and the cooling rate values, and generate display data based on the 3D distribution of microhardness values.