Instant Part Quoting Without Toolpathing Using Geometric Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated quoting methods for custom manufactured parts require expensive computational power and user oversight, especially for machined parts, due to the necessity of toolpathing, which is a complex and computationally expensive process.
Innovation Solution
A method and system using machine learning processes to generate an instantaneous quote by constructing rotation-invariant features from a geometric model, predicting manufacturing time, selecting stock, and estimating a quote without the need for toolpathing, leveraging machine learning models for manufacturing time, stock selection, and go/no-go classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated quoting methods are used for custom manufactured parts, then quoting accuracy is improved, but computational cost and time increase
Solution Approach 1:
The system performs preliminary action by pre-computing and storing rotation-invariant features from geometric models in a database during the quoting process. These pre-extracted features (such as volume, surface area, bounding box dimensions, and other geometric properties) are prepared in advance and stored for rapid retrieval, eliminating the need for repeated computational analysis during actual quoting operations. This allows instantaneous quoting while maintaining accuracy.
2Manufacturing precision
If toolpathing is performed for accurate quoting of machined parts, then manufacturing precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The system extracts and utilizes only the essential geometric features (rotation-invariant features) from the complete toolpathing process. Instead of performing full toolpathing calculations, the system extracts key geometric properties such as volume, surface area, bounding box dimensions, and other rotation-invariant characteristics that are sufficient for accurate quoting. This extraction approach maintains quoting precision while eliminating the complexity of full toolpathing operations.
Solution Approach 2:
The system replaces expensive, computationally intensive toolpathing software with simpler, more economical machine learning models that use pre-extracted geometric features. By using readily available geometric data and applying ML algorithms for manufacturing time prediction and cost estimation, the system achieves accurate quoting without requiring costly CAM software licenses and computational resources.
3Reliability
If expert user oversight is required for automated quoting, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically performing all quoting operations without requiring expert user oversight. The machine learning models autonomously predict manufacturing times, select appropriate stock materials, and generate cost estimates based on pre-extracted geometric features. The system self-validates results and provides instantaneous quotes, eliminating the need for human experts to review or adjust quoting parameters while maintaining high reliability through the robustness of the ML algorithms.
Data Source
AI summary
Methods and In an aspect a method of generating an instantaneous quote of any part without toolpathing, the method includes receiving, using a computing device, a geometric model of a part, constructing, using the computing device, at least a rotation-invariant feature as a function of the geometric model, predicting, using the computing device, a manufacturing time as a function of the at least a rotation-invariant feature and a manufacturing time machine learning model, selecting, using the computing device, a stock as a function of the at least a rotation-invariant and a stock selection machine learning model feature, and estimating, using the computing device, a quote as a function of the manufacturing time and the stock.