Bayesian Robust Process Optimization Under Parameter Noise
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Solution Overview
Problem
In production and machining processes, process parameters can deviate due to noise or disturbances, leading to undesired properties in workpieces, necessitating a robust optimization method that accounts for variations in physical or chemical processes.
Innovation Solution
A Bayesian optimization method is employed to determine a robust optimum by adapting statistical models using new measurement points, incorporating noise and disturbances, and controlling processes to achieve desired properties despite parameter deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If model-based optimization methods are used to determine process parameters, then desired properties of workpieces can be obtained, but process parameters are subject to noise or disturbances causing deviations from desired properties
Solution Approach 1:
The patent applies preliminary action by determining a robust optimum in advance that accounts for potential noise and disturbances. The Bayesian optimization method incorporates uncertainty modeling to predict optimal process parameters that remain stable even when disturbances occur, rather than simply optimizing for nominal conditions and correcting deviations later.
Solution Approach 2:
The patent transforms the optimization approach by changing from deterministic parameter optimization to probabilistic robust optimization. It introduces a robustness criterion that evaluates parameters based on their stability under noise and disturbances, fundamentally altering how optimality is defined and achieved in the manufacturing process.
2Manufacturing precision
If process parameters are optimized for desired properties, then workpiece quality improves, but deviations due to noise cause properties to deviate from desired values
Solution Approach 1:
The patent converts the harmful effect of noise and disturbances into a beneficial factor by using them as input for the robustness criterion. Instead of treating noise purely as a detrimental element to be eliminated, the methodology incorporates it into the optimization process to identify parameters that are inherently more robust, thereby turning the presence of noise into an opportunity for finding more reliable operating conditions.
Solution Approach 2:
The patent applies preliminary anti-action by proactively compensating for the harmful effects of noise and disturbances before they can degrade workpiece quality. The robust optimization methodology pre-adjusts process parameters to counteract anticipated disturbances, ensuring that even when noise occurs, the workpiece properties remain close to desired values.
3Reliability
If a robust optimum is determined to account for deviations, then process robustness improves, but computational complexity increases with Bayesian optimization methods
Solution Approach 1:
The patent introduces a robustness criterion as an intermediary element that bridges the gap between noisy process parameters and desired workpiece properties. This criterion acts as a mediator that translates the complex Bayesian optimization problem into a more manageable form by providing a clear metric for evaluating robustness, thereby reducing the overall computational complexity while maintaining reliability.
Data Source
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AI summary
A device and a method for determining a robust optimum of a physical or chemical process according to a Bayesian optimization method are disclosed, wherein each measurement point of known measurement points (202) has an input parameter value of a physical or chemical process and a measured output parameter value associated with the input parameter value, wherein a first statistical model describes the relationship between the input parameter values and the output parameter values of the physical or chemical process, and wherein in the method a second statistical model (208) is determined for the first statistical model (204), wherein the second statistical model (208) describes a robustness for the output parameter values of the physical or chemical process with respect to a change in the input parameter values (206), and wherein a new measurement point (210) is selected such thatthat a difference between an entropy of the first statistical model (204) described by the known measurement points (202) for the physical or chemical process at the new measurement point (210) and an expected entropy of the first statistical model (204) described by the known measurement points (202) at the new measurement point (210) and an assumed maximum value of the output parameter of the second statistical model (208) at the robust optimum of the physical or chemical process is essentially maximal or lies within a specified region of the maximum.