Causal Predictive Models for Free-Form Manufacturing Control
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Solution Overview
Problem
Existing free-form manufacturing technologies face challenges in accurately determining control inputs for manufacturing apparatuses to produce products with specific attributes, due to the complexity of non-linear and non-local manufacturing processes.
Innovation Solution
The method involves receiving model data including initial state data, model control inputs, and target measurement data, and learning a causal predictive model based on this data. The model compares a final state with target measurement data to determine differences, and calculates control inputs to be assigned to manufacturing process controls, defining the design of the end product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physical modeling techniques are used for free-form manufacturing, then manufacturing precision can be improved, but device complexity increases due to the need for accurate understanding of governing physical relations and simulations
Solution Approach 1:
The patent creates a digital twin or virtual model of the manufacturing process that replicates the physical system's behavior. This virtual model allows simulations and predictions to be performed in the digital domain, improving manufacturing precision through accurate physical modeling without adding physical complexity to the actual manufacturing apparatus.
Solution Approach 2:
The patent replaces complex physical measurements and adjustments with computational models and algorithms. By using virtual simulations and digital twins to predict and optimize manufacturing outcomes, the system achieves high precision without requiring complex physical measurement and adjustment mechanisms.
2Manufacturing precision
If complex non-linear and non-local processes are used for free-form manufacturing, then manufacturing precision can be improved, but ease of manufacture deteriorates due to process complexity
Solution Approach 1:
The patent introduces a computational intermediary layer that mediates between the complex non-linear processes and the control system. This software intermediary translates complex physical processes into manageable control parameters, making the manufacturing process easier to control while maintaining high precision through accurate modeling of non-linear and non-local effects.
Solution Approach 2:
The patent transforms complex non-linear process control into manageable parameter optimization problems. By identifying key parameters and using computational methods to optimize them, the system maintains manufacturing precision while simplifying the ease of manufacture through parameter-based control rather than direct process control.
3Manufacturing precision
If iterative trial-and-error methods are used to determine control inputs, then manufacturing precision can be improved, but productivity decreases due to time-consuming iterations
Solution Approach 1:
The patent performs preliminary computational simulations and predictions before actual manufacturing. By using the virtual model to predict optimal control inputs and outcomes in advance, the system reduces or eliminates iterative trial-and-error in physical manufacturing, thereby improving productivity while maintaining precision through pre-optimized control parameters.
Solution Approach 2:
The patent implements a feedback mechanism where the virtual model is continuously updated with actual manufacturing data. This closed-loop system uses feedback to refine predictions and optimize control inputs, achieving high precision without repeated physical iterations, thus improving productivity through intelligent feedback-driven optimization.
Data Source
AI summary
Methods and systems for determining control inputs to a manufacturing apparatus to manufacture a product are described. A processor may receive model data including initial state data indicating an initial state of an input material, a set of model control inputs, and target measurement data associated with a target product. The processor may learn a causal predictive model based on the target data. Each state of the causal predictive model may be based on an application of the model control inputs on a previous state of the causal predictive model. The processor may compare a final state of the causal predictive model with the target measurement data to determine a difference. The processor may determine, based on the difference, a set of control inputs to be assigned to one or more controls. The one or more controls may define a design of the manufacturing process of an end product.