Intelligent CAD Operation Prediction for Feasible Mechanical Design
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Solution Overview
Problem
Current CAD systems are time-consuming and costly due to complex coordination between engineers in different technical domains, particularly challenging for inexperienced designers, and often result in errors or infeasible designs.
Innovation Solution
A computer-implemented method that converts sequences of CAD operations into tokens and metadata vectors, trains a model on these, and predicts subsequent operations based on user inputs and constraints, providing intelligent design assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual design methods with optimization software tools are used, then design flexibility and engineer control are maintained, but design time and complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical design processes with an AI-based predictive system that automatically suggests CAD operations. The machine learning model learns from historical design data and predicts optimal design operations, substituting the engineer's manual decision-making with automated intelligent suggestions, thereby reducing design time while maintaining design quality.
Solution Approach 2:
The patent introduces an AI assistant as an intermediary between the engineer and the CAD system. This assistant analyzes the current design state, retrieves relevant information from the knowledge base, and provides predictive suggestions for next steps. The intermediary processes information and coordinates between the engineer's requirements and the CAD system's capabilities, reducing the cognitive burden on engineers.
2Reliability
If rule-based standardized methodology (V-model) is followed, then design quality and coordination are improved, but process complexity and cost increase
Solution Approach 1:
The patent transforms the rigid rule-based V-model into a more flexible process by introducing AI-based predictive suggestions that adapt to specific design contexts. The system dynamically adjusts design recommendations based on learned patterns from historical data, allowing the process to maintain quality standards while reducing unnecessary coordination steps and simplifying the overall methodology.
Solution Approach 2:
The patent enables the design system to partially self-service by automatically retrieving relevant design information, constraints, and standards from the knowledge base without requiring manual coordination between multiple engineers. The AI assistant independently queries the knowledge base, processes information, and generates suggestions, reducing the need for complex inter-engineer coordination while maintaining design quality.
3Manufacturing precision
If comprehensive metadata and design constraints are processed, then design accuracy and feasibility are improved, but computational complexity increases
Solution Approach 1:
The patent segments the comprehensive metadata and design constraints into structured categories (geometric constraints, manufacturing constraints, performance requirements, etc.) that can be independently processed by the AI model. This segmentation allows the system to handle complex information in manageable chunks, improving design accuracy while controlling computational complexity through organized data processing.
Data Source
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AI summary
The disclosure relates to a computer-implemented method for assisting a design engineer in generating a 3D design of a physical object. The method comprises steps of, in a training phase, acquiring sequences of computer-aided design operations for designs of physical objects and metadata associated with the sequences of computer-aided design operations. The method proceeds with converting the acquired sequences of computer-aided design operations into tokens of computer-aided design operations, and converting the metadata into metadata vectors. The method then trains a model based on the tokens of the computer-aided design operations and the metadata vector, and stores the trained model in a database. In an application phase, the method executes steps of acquiring a user input for the 3D design of the physical object including design constraints and a current sequence of computer-aided design operations, converting the acquired current sequence of computer-aided design operations into one or more tokens of the current design sequence, converting the acquired design constraints into a vector of design constraints, predicting at least one computer-aided design operation based on the trained model stored in the database, and at least one of the tokens of the current design sequence and the vector of the design constraints, and generating information including the predicted at least one computer-aided design operation and outputting the generated information in a signal to the user.