18063773. SYSTEMS AND METHODS FOR PREDICTING MATERIAL PROPERTIES OF A PART TO BE ADDITIVE-MANUFACTURED simplified abstract (THE BOEING COMPANY)

SYSTEMS AND METHODS FOR PREDICTING MATERIAL PROPERTIES OF A PART TO BE ADDITIVE-MANUFACTURED

Organization Name

THE BOEING COMPANY

Inventor(s)

Andrew H. Baker of St. Louis MO (US)

Luke A. Berglind of Brentwood MO (US)

Lawrence E. Pado of St. Charles MO (US)

Justin L'hote of St. Peters MO (US)

Elaine Macdonald of Wildwood MO (US)

Baily J. Thomas of Lake St. Louis MO (US)

SYSTEMS AND METHODS FOR PREDICTING MATERIAL PROPERTIES OF A PART TO BE ADDITIVE-MANUFACTURED - A simplified explanation of the abstract

This abstract first appeared for US patent application 18063773 titled 'SYSTEMS AND METHODS FOR PREDICTING MATERIAL PROPERTIES OF A PART TO BE ADDITIVE-MANUFACTURED

Simplified Explanation

This patent application describes a method for predicting material properties of a part to be additive-manufactured by analyzing thermal profiles of standard parts.

• Additive-manufacture a variety of standard parts.
• Obtain thermal profiles at specific locations during the manufacturing process.
• Store thermal profiles and corresponding material properties in a database.
• Use machine-learning algorithms to predict material properties of the part to be manufactured based on the stored data.

Key Features and Innovation

• Utilizes thermal profiles of standard parts to predict material properties.
• Incorporates machine-learning algorithms for accurate predictions.
• Database storage for easy access to thermal profiles and material properties data.

Potential Applications

This technology can be applied in various industries such as aerospace, automotive, and medical, where accurate material properties are crucial for part performance.

Problems Solved

• Predicting material properties of additive-manufactured parts.
• Ensuring quality and reliability of manufactured parts.
• Streamlining the manufacturing process by utilizing data-driven predictions.

Benefits

• Improved accuracy in predicting material properties.
• Enhanced quality control in additive manufacturing.
• Cost-effective and efficient manufacturing process.

Commercial Applications

• "Predictive Material Property Analysis in Additive Manufacturing": Implementing this technology in additive manufacturing processes to optimize part performance and quality control.

Prior Art

Prior research in the field of additive manufacturing and material property prediction can be found in academic journals and industry publications. Researchers have explored similar methods using thermal analysis and machine learning algorithms.

Frequently Updated Research

Stay updated on the latest advancements in additive manufacturing and material property prediction by following research publications and industry conferences.

Questions about Additive Manufacturing Prediction

How can thermal profiles improve material property predictions in additive manufacturing?

Thermal profiles provide valuable data on the manufacturing process, allowing for more accurate predictions of material properties based on real-time conditions.

What are the potential challenges in implementing machine-learning algorithms for material property prediction in additive manufacturing?

Challenges may include data accuracy, algorithm optimization, and integration with existing manufacturing processes. Ongoing research is focused on addressing these challenges to enhance predictive capabilities.

Original Abstract Submitted

A method is provided for predicting material properties of a part to be additive-manufactured. The method comprises additive-manufacturing a plurality of standard parts, and obtaining thermal profiles at select predetermined locations of physical samples of the plurality of standard parts during additive-manufacturing of the plurality of standard parts. The method also comprises storing the thermal profiles and corresponding material properties of the physical samples of the plurality of standard parts in a database. The method further comprises running a machine-learning algorithm to predict material properties of the part to be additive-manufactured based upon the thermal profiles and corresponding material properties of the physical samples of the plurality of standard parts stored in the database.