View source for 17933495. SYSTEMS AND METHODS FOR PREDICTING MICROHARDNESS PROPERTIES OF WELDS simplified abstract (Arizona Board of Regents on Behalf of Arizona State University)

=SYSTEMS AND METHODS FOR PREDICTING MICROHARDNESS PROPERTIES OF WELDS=


==Organization Name==

[[:Category:Arizona Board of Regents on Behalf of Arizona State University|Arizona Board of Regents on Behalf of Arizona State University]]


[[Category:Arizona Board of Regents on Behalf of Arizona State University]]

==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 rates, predict a 3D distribution of microhardness values using machine learning, and generate display data based on the predicted values.

== Potential Applications ==
This technology can be applied in industries such as manufacturing, construction, and automotive for quality control and optimization of welding processes.

== Problems Solved ==
This technology solves the problem of accurately predicting microhardness properties of a weld joint, which is crucial for ensuring the structural integrity and strength of the weld.

== Benefits ==
The benefits of this technology include improved weld quality, reduced material waste, increased efficiency in welding processes, and enhanced overall performance of the welded components.

== Potential Commercial Applications ==
The potential commercial applications of this technology include welding equipment manufacturers, welding service providers, and industries that rely on high-quality weld joints for their products.

== Possible Prior Art ==
One possible prior art could be traditional methods of predicting microhardness properties of welds, which may involve manual testing and calculations based on limited data points.

=== Unanswered Questions ===

== How does this technology compare to existing methods of predicting microhardness properties of welds? ==
Answer: This article does not provide a direct comparison to existing methods, leaving the reader to wonder about the advantages and limitations of this new technology.

== What are the specific machine learning algorithms used in this system for predicting microhardness values? ==
Answer: The article does not specify the exact machine learning algorithms employed in the system, leaving a gap in understanding the technical aspects of the innovation.


==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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