AI Manufacturing Feedback for Tolerance-Aware Thermal Components
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Solution Overview
Problem
Conventional manufacturing processes for objects like vapor chambers and heat pipes fail to account for manufacturing tolerances, leading to deviations from target values, which can result in suboptimal performance and failure to meet specifications.
Innovation Solution
The method involves using machine learning models to dynamically adjust target specification values for each step in the manufacturing process based on actual measured values, accounting for manufacturing tolerances and deviations, by training models with historical data and simulation inputs to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional manufacturing processes use fixed target values for each step, then the manufacturing process is simple and easy to control, but the final product fails to meet specifications due to accumulated tolerance deviations
Solution Approach 1:
The patent implements feedback by measuring actual values after each manufacturing step and using these measurements to adjust target values for subsequent steps. The system continuously monitors deviations from target values and feeds this information back to the machine learning model, which then modifies future target values to compensate for accumulated tolerance deviations, ensuring the final product meets specifications.
Solution Approach 2:
The patent transforms the static, fixed target value approach into a dynamic system where target values are continuously adjusted based on actual measurements. The machine learning model dynamically recalculates target values for each subsequent step based on the actual values achieved in previous steps, allowing the manufacturing process to adapt in real-time to tolerance variations.
2Manufacturing precision
If machine learning models dynamically adjust target values based on actual measurements, then manufacturing precision is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model with historical manufacturing data before actual production. This training phase prepares the model to quickly process real-time measurements and make accurate target value adjustments without requiring complex computational resources during the actual manufacturing process, reducing operational complexity.
Solution Approach 2:
The machine learning model acts as an intermediary between the measurement system and the manufacturing process. It receives actual measurements, processes this information through learned patterns from training data, and outputs adjusted target values, simplifying the control system by replacing complex real-time calculations with pre-trained predictive models.
3Productivity
If fixed target values are used without accounting for tolerances, then the manufacturing process is efficient and fast, but the final product performance is suboptimal
Solution Approach 1:
The system maintains high productivity by using feedback to make quick adjustments to target values based on actual measurements. Rather than slowing down the manufacturing process for extensive rework, the system rapidly calculates adjusted target values for subsequent steps that compensate for previous deviations, keeping the production flow efficient while ensuring specification compliance.
Solution Approach 2:
The patent changes the parameters used for target value determination from fixed predetermined values to dynamically adjusted values based on actual measurements. The machine learning model learns optimal parameter adjustments from historical data and applies these changes in real-time, maintaining manufacturing speed while improving product reliability and specification compliance.
Data Source
AI summary
Methods are described herein for artificial-intelligence-based manufacturing. The present invention may include performing an initial step in a series of steps to achieve a target value of an attribute, where the series of steps is for manufacturing an object, and, after performing the initial step, measuring an actual value of the attribute achieved by the initial step. The method may include, for each subsequent step in the series, providing actual values of attributes achieved by preceding steps in the series to a respective machine learning model to determine a respective target value for a respective attribute to be achieved by the respective step. The method may also include, for each step, performing the respective step to achieve the respective target value of the respective attribute and, after performing the respective step, measuring a respective actual value of the respective attribute achieved by the respective step.