View source for 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==

[[:Category:GM GLOBAL TECHNOLOGY OPERATIONS LLC|GM GLOBAL TECHNOLOGY OPERATIONS LLC]]


[[Category:GM GLOBAL TECHNOLOGY OPERATIONS LLC]]

==Inventor(s)==

[[:Category:Ying Lu of Novi MI (US)|Ying Lu of Novi MI (US)]][[Category:Ying Lu of Novi MI (US)]]

[[:Category:Junjie Ma of Troy MI (US)|Junjie Ma of Troy MI (US)]][[Category:Junjie Ma of Troy MI (US)]]

[[:Category:Hui-ping Wang of Troy MI (US)|Hui-ping Wang of Troy MI (US)]][[Category:Hui-ping Wang of Troy MI (US)]]

[[:Category:Mitchell Poirier of Owosso MI (US)|Mitchell Poirier of Owosso MI (US)]][[Category:Mitchell Poirier of Owosso MI (US)]]

[[:Category:Baixuan Yang of Canton MI (US)|Baixuan Yang of Canton MI (US)]][[Category:Baixuan Yang of Canton MI (US)]]

[[:Category:Jay Oswald of Chandler AZ (US)|Jay Oswald of Chandler AZ (US)]][[Category: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.

=== Unanswered Questions ===

== 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.



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