AI Machining Quotation from 3D Drawings and Complexity Analysis
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Solution Overview
Problem
The existing manufacturing industry faces inefficiencies in calculating processing quotations, which are typically time-consuming and manpower-intensive, leading to significant time loss and reduced productivity.
Innovation Solution
A computer program utilizing deep learning techniques to determine a to-be-processed region, select processing methods, and estimate processing complexity and costs for three-dimensional objects, incorporating models for region determination, method selection, complexity calculation, and price estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quotation calculation is performed manually by checking drawings and calculating processing elements, then accuracy of quotation can be maintained, but time consumption increases significantly (average 4 days)
Solution Approach 1:
The patent replaces the manual mechanical process of checking drawings and calculating processing elements with an automated system using deep learning models. The system automatically processes design drawing files, extracts coordinate and vector information, determines to-be-processed regions, selects processing methods, and calculates quotations without human intervention, thereby dramatically reducing time while maintaining accuracy through algorithmic precision.
Solution Approach 2:
The patent introduces deep learning models as intermediary components between the input design drawings and the final quotation output. These models serve as intelligent mediators that automatically interpret drawing information, determine processing requirements, and generate accurate quotations, eliminating the need for manual calculation while preserving measurement precision.
2Productivity
If manual quotation process is used, then flexibility in handling complex cases can be maintained, but productivity and transaction volume are reduced
Solution Approach 1:
The patent transforms the quotation system from a manual parameter-based calculation approach to an automated deep learning-based parameter extraction and analysis system. The system automatically extracts geometric parameters, material parameters, and processing parameters from design drawings, enabling high-speed processing while maintaining the ability to handle complex variations through the flexibility of the neural network models.
Solution Approach 2:
The patent divides the quotation calculation process into distinct modular segments: drawing file processing, coordinate and vector information extraction, to-be-processed region determination, processing method selection, and quotation calculation. Each segment is handled by a specialized deep learning model, enabling parallel processing and improving overall productivity while maintaining adaptability through modular design.
3Measurement precision
If detailed processing analysis is performed manually, then processing complexity can be accurately assessed, but manpower requirements increase
Solution Approach 1:
The patent implements a self-service quotation system where the deep learning models automatically perform all processing analysis tasks without requiring human manpower. The system independently extracts information from design drawings, determines processing requirements, assesses complexity, and generates quotations autonomously, eliminating the need for manual analysis while maintaining accurate assessment through intelligent algorithms.
Data Source
AI summary
A computer program stored in a computer-readable storage medium, according to various embodiments of the present application, may comprise the steps of: acquiring coordinate information and vector information about a three-dimensional object from a design drawing file including the three-dimensional object; inputting coordinate information and vector information into a machining area determination model stored in a memory, so as to determine a machining area for the three-dimensional object; inputting the machining area into a machining method determination model stored in the memory, so as to determine a machining method for the three-dimensional object; inputting the machining method into a machining complexity determination model stored in the memory, so as to calculate the machining complexity of the machining method from machining information acquired from the machining method; and inputting the machining complexity into a machining price estimation model stored in the memory, so as to select a price for machining the three-dimensional object.