Tobacco Machine Control Device for Automatic Defect Correction
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Solution Overview
Problem
Existing control systems for cigarette manufacturing machines lack the ability to automatically detect and correct production errors, failing to precisely localize errors and differentiate between issues in starting materials and machine malfunctions, and they do not allow for subsequent classification of defects due to the absence of original image storage.
Innovation Solution
A control device equipped with optoelectronic sensors to generate image data of both material strands and produced articles, an analysis device to classify defects, and an assignment device to correlate and evaluate these images, enabling automatic optimization of machine parameters based on overall defect analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual intervention is used to correct production errors, then the operator can identify and fix issues, but the production process loses automation and efficiency
Solution Approach 1:
The control device automatically detects, localizes, and corrects production errors without requiring manual operator intervention. The system self-regulates by comparing actual machine parameters with target values and autonomously adjusting parameters to maintain quality standards, thereby improving automation while managing complexity through integrated feedback loops.
Solution Approach 2:
The system implements continuous feedback by monitoring production parameters, comparing them against target values, and automatically adjusting machine settings when deviations are detected. This closed-loop control enables automatic correction of errors while maintaining manageable system complexity through structured feedback mechanisms.
2Loss of information
If only parameter evaluation is performed, then the control system remains simple, but subsequent classification of defects is not possible
Solution Approach 1:
The control device creates and stores digital copies of original images and parameter data during the production process. These copied data sets are preserved in memory for subsequent defect classification and analysis, enabling comprehensive quality assessment without requiring additional physical sampling or manual documentation.
Solution Approach 2:
The system performs preliminary data capture and storage of original images and production parameters at the source, before defects occur or need classification. This preliminary action ensures all necessary information is available for future analysis, enabling detailed defect classification while avoiding the need for complex retroactive data collection.
3Loss of information
If the operator is only informed about production errors, then the information system remains simple, but precise localization of errors and differentiation between material and machine issues is not possible
Solution Approach 1:
The control device segments the production process into distinct measurable parameters and monitors each separately. By dividing the complex production system into individual controllable variables, the system can precisely localize errors to specific parameters and differentiate between material issues and machine malfunctions through targeted parameter analysis.
Solution Approach 2:
The system replaces manual operator analysis with automated electronic monitoring and evaluation of production parameters. Sensors and control systems continuously measure and compare parameter values, automatically identifying error locations and causes without requiring manual inspection or interpretation, thereby providing detailed error information while managing system complexity through automation.
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
Enables reliable detection and automatic correction of production errors, precise localization of defects, and classification of error types, improving the quality of produced articles by adjusting machine parameters accordingly.
Implementation Method 1
at least a first optoelectronic sensor means (13a, 13b), which is designed and set up to generate strand image data (17) of at least a partial surface area of the first strand of material (14a)
Data Source
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AI summary
The device (30) has an analyzing unit (31) connected with testing units (23, 27) by a data transmission unit (32). The analyzing unit analyses image data selected from a group consisting of extrusion error, cast and/or renumbered error image data, using reference picture data and/or a machine model. A controlling unit (34) is connected to the analyzing unit by another data transmission unit and adapted and arranged such that correction of a predetermined parameter of a testing device and/or another testing unit on the basis of a correction value. An independent claim is also included for a method for controlling a machine or a machine combination for manufacturing rod-shaped article in tobacco processing industry.