Manufacturing Operating Conditions for Disturbance-Resistant Scale-Up
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Solution Overview
Problem
It is challenging to accurately control the resin temperature distribution inside an extruder during plastic manufacturing, especially when scaling up from small-scale testing to large-scale production, due to difficulties in direct temperature measurement and the impact of heat exchange and external interferences.
Innovation Solution
A method is proposed that involves inputting a variation amount related to operating conditions, using a pre-trained model to predict characteristic value distributions, calculating an objective function based on the predicted distribution and target values, exploring operating conditions to minimize the objective function, and outputting the optimized operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If small-scale testing is used to determine operating conditions, then development cost and time are reduced, but the operating conditions cannot be directly applied to large-scale production due to heat exchange differences
Solution Approach 1:
The patent applies parameter changes by introducing a scaling factor that adjusts operating conditions based on the ratio between small-scale and large-scale equipment dimensions. The scaling factor modifies temperature, pressure, and flow rate parameters to account for heat exchange differences, allowing operating conditions to be adapted when scaling from small-scale testing to large-scale production while maintaining process performance
Solution Approach 2:
The patent uses a simulation model as an intermediary between small-scale test results and large-scale production parameters. The simulation model incorporates heat exchange calculations and scaling relationships to translate operating conditions across different scales, serving as a mediator that bridges the gap between experimental data and production requirements
2Measurement precision
If direct temperature measurement is implemented in the extruder, then accurate resin temperature data is obtained, but measurement complexity and cost increase significantly
Solution Approach 1:
The patent employs indirect measurement methods using intermediaries such as cylinder wall temperature sensors and simulation models to estimate resin temperature. Instead of directly measuring resin temperature, the system uses temperature sensors on the cylinder wall and applies heat transfer models to calculate the resin temperature, thereby avoiding the complexity of direct internal measurement while maintaining measurement accuracy
Solution Approach 2:
The patent replaces direct physical measurement mechanisms with computational methods. Instead of inserting temperature sensors directly into the resin flow, the system uses thermal field simulation and heat transfer calculations to predict resin temperature based on measurable external parameters, substituting mechanical measurement with computational analysis
3Device complexity
If simulation with 100% fill rate is used, then computational difficulty is reduced, but the simulation does not reflect actual operating conditions with gas-liquid interfaces
Solution Approach 1:
The patent applies partial action by implementing a hybrid simulation approach that uses 100% fill rate conditions for baseline thermal field calculations, then applies correction factors to account for gas-liquid interface effects. This allows the system to benefit from the computational simplicity of full-fill simulations while still capturing the essential physics of partial-fill operations through targeted corrections
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the provision of robust operating conditions resistant to disturbances and external interferences, ensuring consistent product quality across different scales and environments.
Implementation Method 1
a predictor configured to receive the predetermined operating condition and the variation amount, and output a predicted characteristic value distribution of the product using a model in which an input is an operating condition and an output is a characteristic value of the product
Implementation Method 2
an objective function setting unit configured to set an objective function based on the predicted characteristic value distribution and the target characteristic value
Data Source
AI summary
An operating condition for a manufacturing apparatus that manufactures a product using a variation amount input step is provided in which an input device inputs a variation amount related to a predetermined operating condition. A model reading step reads, from memory, a model in which an input is the operating condition and an output is a characteristic value of the product. A target characteristic value is input for the product; and a predicted characteristic value distribution is calculated, using the model, and reflecting the variation amount related to the predetermined operating condition. An objective function is calculated based on the predicted characteristic value distribution and the target characteristic value; and an operating condition exploration step is performed in which a processor explores the predetermined operating condition that reduces the objective function. An operating condition output step is performed of outputting an operating condition that is a result of the exploration.