Glass Tube Converter Feedback Control for Dimensional Yield
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Solution Overview
Problem
Conventional converting machines for producing glass articles from glass tubes rely heavily on human operators to adjust burner parameters and forming tool positions, leading to significant variability in yield and quality due to differences in operator skill levels, resulting in inconsistent dimensional yields and high defect rates.
Innovation Solution
A feedback control system and method that uses measurement data to adjust process parameters automatically, minimizing an objective control function to improve consistency and reduce defects, incorporating statistical analysis and penalty factors to stabilize parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human operators manually adjust burner parameters and forming tool positions, then the converting machine can produce glass articles, but significant variability in yield and quality occurs due to differences in operator skill levels
Solution Approach 1:
The patent implements automated feedback control systems that continuously monitor glass article dimensions and automatically adjust burner parameters and forming tool positions based on measured deviations from target specifications. This closed-loop control eliminates operator skill variability by replacing manual adjustment with automated feedback-driven parameter optimization.
Solution Approach 2:
The patent replaces manual mechanical adjustment systems with automated computer-controlled mechanisms. Burner parameters are controlled by electronic control systems rather than manual valve adjustments, and forming tool positions are controlled by servo motors and PLCs rather than manual mechanical linkages, thereby eliminating human operator variability.
2Manufacturing precision
If automated control devices such as mass flow control valves and servo motors are added to the converting machine, then parameter adjustment precision improves, but device complexity increases
Solution Approach 1:
The patent integrates multiple control functions into unified control systems. The PLC controller and computer control system manage burner parameters, forming tool positions, and process timing through integrated software that coordinates all actuators, reducing the need for separate dedicated control devices for each function.
Solution Approach 2:
The patent combines multiple control devices into integrated control systems. Mass flow control valves are integrated with burner control systems, servo motors are integrated with positioning systems, and all control functions are merged into a unified PLC and computer control architecture that manages the entire converting process through centralized control.
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
The system enhances production yield and reduces defects by optimizing process parameters based on real-time data analysis, achieving consistent high-quality glass articles.
Implementation Method 1
heating elements, such as burners, heat the glass of the glass tube to a temperature at which the viscosity of the glass allows the glass to be formed
Implementation Method 2
heat the glass of the glass tube to a temperature at which the viscosity of the glass allows the glass to be formed into one or more features of the glass article
Data Source
AI summary
Methods for providing feedback control of converters for converting glass tubes to glass articles include a model predictive control framework. The methods include operating the converter, providing target values for attributes of the glass articles or glass tubes, measuring the attributes for the glass articles and glass tubes, conditioning the measurement data to remove outlier data points and calculating statistics representative of the measured attributes, and determine updated settings for one or more process parameters from the previous settings, the statistical properties, and the target values, where the updated settings are those that minimize an objective control function for the converter. The methods further include adjusting the process parameters to the updated settings. The model predictive control framework enables feedback control of the converter that compensates for disturbances that act on the process.