Blow Molding Parameter Control for Wall Thickness Accuracy
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Solution Overview
Problem
Existing blow molding processes struggle to consistently produce containers with minimal deviation in wall thickness from target values, affecting compressive strength and overall quality.
Innovation Solution
A method involving an iterative process to determine optimal machine parameters for blow molding machines, using predictive models and reinforcement learning to minimize deviations in physical parameters like wall thickness, by adjusting heating power, pressure, and temperature profiles, while accounting for disturbance variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If direct adjustment of machine parameter based on deviation is used, then adjustment speed is fast, but manufacturing precision is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the actual wall thickness measurement is compared with the target wall thickness, and the deviation is fed back to adjust machine parameters. The control unit modifies heating power, blowing pressure, or stretching speed based on the measured deviation to minimize wall thickness variation in subsequent containers.
Solution Approach 2:
The patent replaces direct mechanical parameter adjustment with an iterative calculation system that uses measured deviation data to compute optimal parameter changes. Instead of direct deterministic adjustment, the system uses measurement data and iterative algorithms to determine parameter modifications, achieving higher precision through computational methods.
2Manufacturing precision
If iterative process is used to determine optimal machine parameter, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating optimal machine parameters based on expected deviation ranges and storing them in the control unit. When a deviation is measured, the system retrieves pre-computed parameter adjustments rather than performing full iterative calculations, significantly reducing the time penalty of using iterative optimization.
Solution Approach 2:
The patent applies partial iterative adjustment by performing only the necessary number of iteration steps to achieve sufficient precision rather than exhaustive optimization. The system stops the iterative process when the parameter change falls below a threshold or when a predetermined number of iterations is reached, balancing precision with time efficiency.
3Manufacturing precision
If machine parameter is directly linked to deviation, then ease of operation is high, but manufacturing precision is limited
Solution Approach 1:
The patent introduces an intermediary calculation layer between the deviation measurement and machine parameter adjustment. The control unit acts as an intermediary that processes measured deviations, applies iterative optimization algorithms, and translates them into appropriate parameter adjustments. This intermediary layer enables precise control while maintaining operational simplicity through automated computation.
Solution Approach 2:
The system implements self-service by automatically measuring wall thickness, calculating deviations, determining optimal parameter adjustments, and executing the adjustments without manual intervention. The blow molding machine's control system autonomously performs the entire optimization cycle, eliminating the need for operator expertise in complex calculations while achieving high precision.
Data Source
AI summary
A method for minimizing a deviation of a physical parameter of a blow-molded container from a target value comprises determining a physical parameter of a container assigned to a machine parameter value of a blow molding machine and an environmental condition, based on the physical parameter and the target value, determining a change in the machine parameter, based on an iteration process, determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container, the iteration process comprising a first iteration step for determining a deviation from the target value of the physical parameter of a blow-molded container based on a change in the machine parameter value, and a second iteration step for determining an adjusted change in the machine parameter value based on the deviation of the physical parameter of a blow-molded container from the target value.