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

Arizona Board of Regents on Behalf of Arizona State University

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 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.

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.