20230132487. DESIGN OF ENGINEERING COMPONENTS simplified abstract (ROLLS-ROYCE PLC)

DESIGN OF ENGINEERING COMPONENTS

Organization Name

ROLLS-ROYCE PLC

Inventor(s)

Andrew J Keane of Southampton (GB)

DESIGN OF ENGINEERING COMPONENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230132487 titled 'DESIGN OF ENGINEERING COMPONENTS

Simplified Explanation

The abstract describes a computer-based method for assessing the suitability of a component using a conditional generative adversarial network (CGAN). The CGAN consists of an input, a generator, and a discriminator. The method involves obtaining images or scans of acceptable and non-acceptable components, training the discriminator with these images, scanning or imaging a component to be assessed, and inputting the image or scan into the discriminator to determine if it is acceptable or not.

• Utilizes a conditional generative adversarial network (CGAN) for component assessment.
• Trains the CGAN's discriminator with images of acceptable and non-acceptable components.
• Scans or images a component to be assessed.
• Inputs the image or scan into the CGAN's discriminator to determine its acceptability.

Potential Applications

• Quality control in manufacturing industries.
• Automated assessment of components in various fields like automotive, aerospace, electronics, etc.
• Streamlining inspection processes in production lines.

Problems Solved

• Reduces the need for manual inspection and assessment of components.
• Provides a more objective and consistent evaluation of component suitability.
• Speeds up the assessment process, leading to increased efficiency in manufacturing.

Benefits

• Improved accuracy and reliability in component assessment.
• Cost and time savings by automating the evaluation process.
• Enables real-time assessment, allowing for immediate action if a component is deemed unacceptable.

Original Abstract Submitted

a computer implemented method of assessing the suitability of a component, including: utilising a conditional generative adversarial network (cgan), the cgan network having an input, a generator and a discriminator; obtaining images or scans of manufactured components for both acceptable and non-acceptable components; training the discriminator of the cgan with the images representing the acceptable and non-acceptable scans or images of a manufactured component; scanning or imaging a component to be assessed; and inputting the image or scan of a component to be assessed into the discriminator of the cgan, wherein the output of the discriminator assesses if the scan or image is acceptable for use or not.