Circular Knitting Camera Inspection for Doffing Defect Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional circular knitting machines are unable to identify specific positions for repair based on fabric surface status and cannot detect defects synchronously during doffing, leading to resource waste and increased production costs due to the need to replace all knitting needles when defects are discovered post-production.
Innovation Solution
A circular knitting machine equipped with a camera module and information processing unit that photographs the fabric during doffing, compares image data from successive revolutions, and uses encoder-generated pulse signals to determine abnormal knitting needles, allowing for immediate identification and correction of defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fabric quality testing is performed after knitting completion, then comprehensive quality assessment is achieved, but production time is lost and all knitting needles must be replaced even when only specific needles are defective
Solution Approach 1:
The camera module captures fabric images during the doffing process, performing quality inspection before the knitting machine completes its operation. This preliminary action allows defect detection at the moment of doffing rather than after production, eliminating the need to wait for completion and enabling timely identification of specific defective needles.
Solution Approach 2:
The system creates digital copies (images) of the fabric surface during doffing, which are then analyzed by the control unit to identify defects. This copying approach enables non-contact, rapid inspection without interrupting the knitting process, allowing quality assessment to be performed on image data rather than requiring physical inspection after production.
2Reliability
If all knitting needles are replaced when defects are detected, then production reliability is maintained, but resource waste increases due to replacing functional needles
Solution Approach 1:
The system identifies the specific location and number of defective knitting needles by analyzing fabric defects in captured images. Instead of treating all needles uniformly, the control unit provides targeted prompts for only the defective needles, allowing local repair while preserving functional needles. This transforms a global replacement approach into a localized maintenance strategy.
Solution Approach 2:
The knitting machine system performs self-diagnosis by automatically capturing fabric images, analyzing defects, and identifying defective needle positions. The control unit generates maintenance prompts without human intervention, enabling the system to monitor its own status and guide operators to specific repair locations, thereby reducing resource waste through precise problem identification.
3Ease of operation
If manual inspection methods are used to identify defective needles, then simplicity is maintained, but measurement precision is insufficient to locate specific defective positions
Solution Approach 1:
The system replaces manual visual inspection with an automated optical detection system. The camera module captures high-resolution images of the fabric, and the control unit automatically analyzes these images to identify defect positions and map them to specific knitting needles. This substitution of mechanical/manual inspection with automated image processing maintains operational simplicity while dramatically improving location precision.
Solution Approach 2:
The camera module and control unit serve as intermediaries between the fabric and the operator. Instead of directly observing and manually locating defects, the system uses image capture and automated analysis as intermediary steps to translate physical fabric defects into actionable information about specific defective needle positions, bridging the gap between visual inspection and precise needle identification.
4Measurement precision
If image processing is performed on all captured images, then comprehensive defect detection is achieved, but computational time and resources are wasted on defect-free areas
Solution Approach 1:
The control unit extracts and processes only the relevant portions of captured images that contain potential defects, rather than analyzing entire images uniformly. By identifying and focusing computational resources on specific regions of interest where defects are detected, the system reduces unnecessary processing of defect-free areas while maintaining comprehensive defect detection capability.
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 instantaneous detection of defects, reducing computational requirements and enabling targeted needle replacement, thereby minimizing waste and production downtime.
Implementation Method 1
a camera module (24), an information processing unit (25), and an encoder (22)... The camera module (24) photographs the fabric (30) during doffing
Implementation Method 2
The encoder (22) generates a plurality of pulse signals (221) when the needle cylinder (23) rotates
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention provides a circular knitting machine (20) for prompting a knitting machine status instantaneously based on a cloth surface status of the fabric, comprising a needle cylinder (23), a camera module (24) capable of photographing the fabric (30) during doffing, an information processing unit (25), and an encoder (22). A camera lens (242) of the camera module (24) does not rotate with the needle cylinder (23), and a shooting timing of the camera lens (242) is controlled by photographing signals (27). The information processing unit (25) receives image data (241) generated by the camera module (24), and compares the images, when there is a difference between the two consecutive image data (241) on a same vertical line (50), a knitting machine status is prompted. The encoder generates pulse signals when the needle cylinder rotates, the encoder (22) outputs the pulse signals (221) to the camera module (24) or the information processing unit (25), and the receiver counts the pulse signals (221) to generate the photographing signals (27).