Slitter Director Control for Defect-Tracked Web Splicing
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Solution Overview
Problem
Current manufacturing processes for web materials, such as transparent polyester films, face challenges in efficiently and accurately detecting defects in moving webs due to high data acquisition rates, requiring manual intervention for defect removal and quality control, which is time-consuming and prone to errors, especially in multi-layer materials where top layers are opaque or have product-required markings.
Innovation Solution
An automated system, referred to as a slitter director, uses previously-generated anomaly data to precisely stop a slitter at defective locations, optimize slit roll selection, and discard defective rolls, eliminating the need for human inspectors by registering defect information with physical web positions and applying rulesets for defect removal, thereby enhancing operational efficiency and quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to identify and remove defective areas, then human inspectors can take proper action, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual inspection with an automated optical inspection system that captures images of the web material and uses image processing algorithms to detect defects. This substitution eliminates human error and accelerates the inspection process while maintaining high reliability through consistent automated defect identification.
Solution Approach 2:
The system enables self-service by automatically identifying defective areas and determining splicing locations without human intervention. The automated system processes images, detects defects, calculates optimal splicing positions, and controls the splicing mechanism, allowing the manufacturing line to correct defects autonomously.
2Manufacturing precision
If the slitter stops to splice out defective areas, then defect removal precision is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal splicing locations before the web material reaches the splicing point. The image processing system identifies defects ahead of time, computes the best splicing positions that minimize material waste, and prepares splicing instructions in advance, allowing the slitter to execute precise defect removal without unnecessary stopping.
Solution Approach 2:
The system dynamically adjusts the slitter operation by selectively stopping only at necessary locations for defect removal while maintaining continuous motion for non-defective areas. This dynamic control optimizes the balance between precision defect removal and overall processing speed, preventing unnecessary interruptions to the manufacturing flow.
3Reliability
If human inspectors manually identify and remove defects, then quality control can be performed, but the number of human inspectors must be increased
Solution Approach 1:
The patent replaces complex human inspection processes with an automated optical inspection system that uses image capture and processing algorithms. This system provides consistent quality control through standardized automated defect detection, eliminating variability in human judgment while reducing the need for multiple inspectors.
Solution Approach 2:
The system introduces an intermediary computational layer that processes images and translates visual defect information into actionable splicing instructions. This intermediary processing stage bridges the gap between raw image data and precise defect removal actions, enabling automated quality control without direct human intervention.
Data Source
AI summary
This disclosure describes techniques for automatically controlling the operation of a slitter (40) to convert a web (20) of material into smaller slit rolls (64, 66, 68). A slitter director (60) may automatically control the operation of a slitter (40) for defect removal, web splicing, and/or slit roll rejection based on continually registering previously-generated anomaly data (62) with physical locations of the web (20).