AM Process Selection Logic for Cost and Quality Trade-Offs
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Solution Overview
Problem
Current additive manufacturing technologies face challenges in efficiently selecting the optimal manufacturing process for complex geometries and materials, leading to inconsistencies in part quality and increased costs, especially when scaling production.
Innovation Solution
A system and method that receives product specification data and multiple manufacturing process datasets to select an optimal additive manufacturing (AM) process using machine logic, evaluating factors like manufacturing time, cost, material availability, and quality to determine the best AM operation for producing a physical product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple additive manufacturing processes are evaluated manually to select the optimal process, then manufacturing flexibility and adaptability are improved, but time consumption and complexity increase
Solution Approach 1:
The system enables self-service by automatically selecting the optimal additive manufacturing process through machine logic that evaluates product specifications against multiple manufacturing process datasets, eliminating the need for manual human intervention in the selection process
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with an automated computer-based system that uses machine logic and algorithms to compare product specifications with manufacturing process datasets, substituting human decision-making with automated computational analysis
2Adaptability or versatility
If manual evaluation of manufacturing processes is used, then flexibility in process selection is maintained, but manufacturing precision and consistency deteriorate
Solution Approach 1:
The system replaces manual evaluation with automated computer-based analysis that systematically compares product specifications against manufacturing process datasets using machine logic, ensuring consistent and precise selection criteria are applied without human variability
Solution Approach 2:
The system incorporates feedback mechanisms where the automated evaluation process continuously refines process selection by comparing actual manufacturing outcomes with expected results, using this feedback to improve future selections and maintain high precision and consistency
3Reliability
If comprehensive evaluation of multiple manufacturing processes is performed, then optimal process selection is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The system segments the comprehensive evaluation process into distinct computational modules: one module stores product specification data, another stores manufacturing process datasets, and a third applies machine logic for comparison and selection, dividing the complex task into manageable segments that reduce overall system complexity
Solution Approach 2:
The patent creates a universal system architecture where the same machine logic and evaluation framework can be applied across different additive manufacturing processes and product types, allowing the system to handle multiple functions through a single unified approach rather than requiring separate systems for each process
Data Source
AI summary
Machine logic (for example, software) are choosing among pre-existing manufacturing processes to make a newly-designed physical piece part, or a piece part that reflects a modified design with respect to an earlier design. In some embodiments, the manufacturing process includes an additive manufacturing (AM) process. In some embodiments, the manufacturing process involves three dimensional (3D) printing.