AI CAD-CAE Workflow for Incomplete Design and CNC Tool Selection
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Solution Overview
Problem
Current AI-based CAD/CAE and CNC machining systems are not unified, lacking a single software program that integrates algorithms for CAD/CAM/CAE designers and machine cutting tools, which hinders seamless design-to-manufacturing processes, particularly in adjusting dimensions, providing assembly instructions, and selecting optimal CNC machining tools.
Innovation Solution
A CNC system and software program utilizing a recurrent neural network (RNN) to complete incomplete designs, provide step-by-step assembly instructions, and automatically adjust dimensions, while also selecting the best CNC machining tools through a networked CNC system and deep learning networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate AI-based CAD/CAE and CNC machining systems are used, then design and manufacturing functions are provided, but the systems lack integration and seamless workflow between design-to-manufacturing processes
Solution Approach 1:
The patent merges separate AI-based CAD/CAE and CNC machining systems into a unified integrated system. The software program combines design algorithms with machine cutting tool control algorithms, enabling seamless workflow from design to manufacturing within a single system architecture, thereby improving adaptability while managing integration complexity through unified design.
Solution Approach 2:
The integrated system provides multi-functional capabilities by incorporating both design and manufacturing functions in one platform. The system can perform CAD/CAE operations, generate machining instructions, control CNC tools, and adapt to various design-to-manufacturing workflows, making it universally applicable across different engineering tasks.
2Productivity
If manual dimension adjustment is performed in design software, then design flexibility is maintained, but time is lost in manually adjusting dimensions and providing assembly instructions
Solution Approach 1:
The system implements self-service automation where the software automatically adjusts dimensions and generates assembly instructions without manual intervention. The integrated program detects design parameters, performs necessary dimension adjustments, and creates machining instructions autonomously, eliminating the time-consuming manual processes while maintaining design flexibility.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and preparing dimension adjustments and assembly instructions during the design phase. The software anticipates manufacturing requirements and prepares necessary adjustments and documentation in advance, reducing the time needed during actual manufacturing operations.
3Adaptability or versatility
If multiple CNC machining tools are available, then manufacturing options increase, but difficulty increases in selecting the optimal tool for each design
Solution Approach 1:
The integrated system implements feedback mechanisms that analyze design characteristics and automatically match them with suitable CNC machining tools. The software evaluates design parameters, material properties, and manufacturing requirements, then provides feedback to select the optimal tool from available options, simplifying the selection process while maintaining adaptability to various manufacturing scenarios.
Solution Approach 2:
The system acts as an intermediary between design specifications and CNC tool selection. The integrated software program analyzes design requirements and mediates the selection process by automatically matching designs with appropriate machining tools, eliminating the need for manual evaluation of multiple tool options and reducing selection complexity.
Data Source
AI summary
A CNC system and computer software program are operative to perform: receiving an design work; if the design work is incomplete, then using a convolutional neural network (CNN) to complete the design work and then using an auto-mode to snap fit components into the design; converting the compete design to CAD/CAE instructions; using a recurrent neural network (RNN) to create a step-by-step assembly instructions for the completed design work so as every connection of said design work is fulfilled; and assigning the completed design specification to be manufactured by a CNC machining tool in an array of CNC machining tools connected together and to the CNC system via a network.