Predictive Sequence Control for Consistent Manufacturing Output
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Solution Overview
Problem
Manufacturing systems face challenges in maintaining consistent product characteristics due to variability in control and exogenous parameters over time, which existing statistical techniques struggle to effectively predict and control.
Innovation Solution
A sequence-to-sequence model, utilizing recurrent neural networks and long short-term memory networks, processes time series data from sensors to generate a feature set describing the manufacturing system's state space, predicting feature parameter values and adjusting control parameters via a controller agent to maintain target values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing statistical techniques are used to control manufacturing parameters, then the control system is simple and easy to implement, but the system cannot effectively predict and maintain consistent product characteristics when control and exogenous parameters vary over time
Solution Approach 1:
The patent replaces traditional statistical control techniques with a sequence-to-sequence model based on recurrent neural networks. This substitution transforms the control approach from simple statistical methods to an intelligent predictive system that can capture temporal dependencies and non-linear relationships in manufacturing data, thereby improving product characteristic consistency while accepting increased system complexity
Solution Approach 2:
The patent changes the fundamental parameters of the control system by introducing a sequence-to-sequence model that processes time series data of control parameters and exogenous parameters. This model transforms input sequences into output sequences, enabling predictive control that adapts to varying manufacturing conditions and maintains product consistency through dynamic parameter adjustment
2Manufacturing precision
If a sequence-to-sequence model is implemented to predict feature parameters and adjust control parameters, then part-to-part consistency is enhanced, but the device complexity and computational requirements increase
Solution Approach 1:
The sequence-to-sequence model performs preliminary prediction of future feature parameter values based on historical time series data before actual manufacturing deviations occur. This predictive capability allows the controller agent to proactively adjust control parameters to maintain target values, enhancing part-to-part consistency by anticipating and preventing deviations rather than merely reacting to them
Solution Approach 2:
The patent implements a closed-loop feedback system where the sequence-to-sequence model continuously predicts feature parameter values, compares them with target values, and feeds this information back to the controller agent for real-time control parameter adjustment. This feedback mechanism enables continuous optimization of manufacturing precision while managing system complexity through iterative learning and adaptation
Data Source
AI summary
One or more processors generate a feature set describing evolution of a state space of a manufacturing system from time series data of sensors measuring values of control parameters and exogenous parameters of the manufacturing system, and measuring values of feature parameters of components produced by the manufacturing system. The one or more processors also generate from the feature set predicted values of at least one of the feature parameters, and alter at least one of the control parameters according to the feature set and the predicted values to drive the predicted values toward a target value or target values.