Surrogate Part Model for Manufacturing Cost and Longevity Tradeoffs
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Solution Overview
Problem
Traditional manufacturing processes are unable to favorably alter the earnings curve of a product's lifecycle without compromising durability, as they lack the capability to mitigate costs without impacting performance.
Innovation Solution
A system and method that utilize a surrogate model, generated through machine learning algorithms, to optimize manufacturing processes by predicting the longevity of parts and balancing manufacturing costs with durability, allowing for strategic deployment of components and improved cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If typical manufacturing processes are optimized for maximum durability, then part longevity is improved, but manufacturing cost increases
Solution Approach 1:
The patent applies parameter changes by utilizing machine learning algorithms to analyze historical data and identify optimal manufacturing parameters (such as laser settings, EDM burn rates, and other process variables) that produce parts with desired longevity characteristics. By determining and applying these optimized parameters, the system achieves target durability outcomes while avoiding the need for over-engineered, high-cost manufacturing processes, thus resolving the contradiction between part longevity and manufacturing cost.
2Ease of manufacture
If manufacturing cost is reduced, then earnings are improved, but part durability is compromised
Solution Approach 1:
The patent implements feedback mechanisms by continuously collecting and analyzing data from fielded parts, including performance metrics, failure modes, and manufacturing parameters. This feedback loop enables the machine learning algorithms to refine predictions of part longevity and adjust manufacturing parameter recommendations accordingly. The system uses this feedback to ensure that cost-reduced manufacturing processes still produce parts meeting minimum durability thresholds, thus resolving the contradiction between manufacturing cost and part durability.
3Device complexity
If traditional manufacturing processes are used, then manufacturing simplicity is maintained, but capability to optimize earnings curve is lost
Solution Approach 1:
The patent replaces traditional mechanical and empirical manufacturing optimization approaches with data-driven machine learning algorithms and digital thread technology. Instead of relying on conventional trial-and-error methods or complex manual analysis, the system uses computational models to predict part longevity and optimize manufacturing parameters. This substitution enables earnings curve optimization capability while maintaining relative manufacturing process simplicity, as the automated algorithms handle the complexity of data analysis and parameter optimization.
Data Source
AI summary
There is provided a method for optimizing a manufacturing process of a new part. The method includes executing, by a system configured to drive the manufacturing process, a set of manufacturing functions. Executing these functions include receiving data associated with one or more field parts similar to the new part, and generating, based on the data, a forecast representative of a longevity of the one or more parts. The method further includes generating a digital thread forming a surrogate model for the new part, based on the forecast. Further, the method includes creating the set of manufacturing functions based on the surrogate model and manufacturing the new part according to the set of manufacturing functions.