ML-Driven Geometric Operation Automation in CAD

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Solution Overview

Problem

In computer-aided design (CAD) environments, users face inefficiencies and errors when repeatedly performing similar geometric operations, particularly for beginners or less experienced users, leading to time-consuming and cumbersome correction processes during design validation.

Innovation Solution

A method and system utilizing machine learning models to determine geometric operations, predict candidate components, and perform operations automatically, reducing user input and increasing accuracy by identifying suitable candidates and performing operations based on probability calculations and pre-defined grouping rules.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If users manually perform geometric operations in CAD environment, then design flexibility and control are maintained, but time consumption and error rates increase significantly

Engineering Contradiction:

Improvedesign speedVSAvoidtime for repeated operations

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service by allowing the CAD application to automatically perform geometric operations on candidate components without requiring manual user intervention. The machine learning model predicts suitable candidates and the system executes operations autonomously, freeing users from repetitive manual tasks while maintaining design control.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-identifying and grouping candidate components that are suitable for geometric operations before the user initiates the operation. The machine learning model analyzes the design context and pre-selects potential candidates, so when the user wants to perform an operation, the work is already prepared and ready for execution.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If beginners perform geometric operations without experience, then design accessibility is improved, but error rates and design quality deteriorate

Engineering Contradiction:

Improveaccessibility for beginnersVSAvoiddesign accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The machine learning model acts as an intermediary between the beginner user and the complex CAD operations. Instead of requiring users to have expertise in selecting appropriate candidates and performing operations, the ML model mediates by automatically analyzing the design context, identifying suitable candidates, and suggesting or executing the appropriate geometric operations, thereby bridging the skill gap.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback by using the machine learning model to continuously learn from design patterns and user interactions. The model analyzes the context, provides intelligent suggestions for geometric operations, and learns from corrections or confirmations, thereby improving its accuracy over time and ensuring reliable operation suggestions even for beginner users.

Inventive Principle:

Principle #23Feedback

3Reliability

If design validation is performed to identify errors, then design quality is improved, but time-to-market increases due to cumbersome correction processes

Engineering Contradiction:

Improvedesign qualityVSAvoidtime for error correction

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies preliminary anti-action by proactively preventing design errors before they occur. The machine learning model analyzes the design context and predicts appropriate geometric operations, guiding users toward correct decisions and preventing common mistakes. By anticipating potential errors and guiding users away from them, the system reduces the need for later validation and correction.

Inventive Principle:

Principle #9Preliminary anti-action

4Productivity

If machine learning models automatically perform geometric operations, then productivity and accuracy are improved, but system complexity increases

Engineering Contradiction:

Improvedesign output speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the complex task of geometric operation selection into distinct modules: a machine learning model for context analysis and candidate prediction, a candidate grouping module for organizing predictions, and an operation execution module for performing the actual geometric operations. This segmentation allows each component to specialize in one function, managing overall system complexity while maintaining high productivity.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20230252207A1Method and system for generating a geometric component using machine learning models

Publication Date: 2023.08.10 SIEMENS INDUSTRY SOFTWARE INC

AI summary

A method and system for generating a geometric component in a computer-aided design (CAD) environment using machine learning models is provided. A computer-implemented method for generating a geometric component in a CAD environment includes determining a geometric operation to be performed on at least one geometric component in the CAD environment based on a CAD command selected by a user. The method also includes determining one or more candidate groups including one or more candidates in the geometric component suitable for performing the geometric operation using one or more trained machine learning models. The method also includes identifying at least one candidate group from the one or more candidate groups on which the geometric operation is to be performed. The method also includes performing the geometric operation on the one or more candidates in the identified candidate group.