Part Tolerance Dimensioning for Manufacturability Feedback
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Solution Overview
Problem
Manufacturing facilities face challenges in providing quick and efficient feedback to customers with varying skill levels regarding part models, as users often select inappropriate tolerances that can lead to increased manufacturing time and cost due to lack of formal engineering training.
Innovation Solution
An apparatus and method utilizing a processor to extract tolerance data from user-submitted part models using machine-learning processes, determining manufacturability data, generating corrections to improve machinability, and creating updated prints and manufacturing quotes for optimal manufacturing parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customers create part models themselves, then service speed is improved, but design accuracy deteriorates due to varying skill levels and lack of engineering knowledge
Solution Approach 1:
The system automatically analyzes customer-submitted part models using machine learning to extract tolerance data and provides feedback on manufacturability. This feedback loop enables customers to submit models quickly while receiving automated guidance to improve design accuracy without requiring formal engineering training.
Solution Approach 2:
Customers independently create and submit part models without requiring expert intervention. The system empowers them to perform self-service model creation while automated manufacturing analysis provides the necessary guidance to maintain quality standards.
2Manufacturing precision
If tight tolerances are specified, then manufacturing precision is improved, but manufacturing time increases
Solution Approach 1:
The machine learning system analyzes the part model and automatically adjusts tolerance parameters to optimal values. It identifies which features require tight tolerances and which can use looser tolerances, changing the parameter set to balance manufacturing precision with production time efficiency.
3Manufacturing precision
If tight tolerances are specified, then manufacturing precision is improved, but manufacturing cost increases
Solution Approach 1:
The system optimizes tolerance parameters to achieve the necessary manufacturing precision while minimizing cost. By analyzing which features truly require tight tolerances and relaxing others, it changes the parameter configuration to reduce manufacturing cost while maintaining quality.
4Productivity
If automated machine learning analysis is implemented, then manufacturing analysis speed is improved, but system complexity increases
Solution Approach 1:
The patent replaces manual manufacturing analysis with automated machine learning processes. This substitution of mechanical/human analysis with intelligent algorithms improves analysis speed while the automated nature of the system manages the complexity through software-based solutions.
Data Source
AI summary
Apparatuses and methods for dimensioning and modifying a part to be manufactured are provided. Part information for a part to be manufactured is received by a processor, where the part information includes a model and print of the part. Tolerance datum are extracted from the print and a manufacturability datum is determined as a function of the model and tolerance datum. Updated tolerance datum is generated and a manufacturability of the part is determined. A manufacturing quote and user feedback is provided.