Unveiling the Untold: A Comprehensive Journey into the World of AI Visual Inspection

Artificial Intelligence (AI) is already catalyzing a paradigm shift in how we traverse, transact, shop and socialize. Given that novel AI initiatives are constantly emerging, an education in and an understanding of AI has become indispensable,which?has become a vital?differentiator for businesses, as AI boasts numerous applications across various sectors, including autonomous vehicles and the textile industry.

AI plays a pivotal role in enabling cutting-edge services that are transforming our quotidian existence. As AI Visual Inspection gains traction, it is driving a revolution of industry 4.0 across manufacturing industries, further providing a significant opportunity for both established and emerging modern companies to propel themselves forward. With AI visual inspection technology as the prime mover of the textile industry, conventional textile processing methods such as manual fabric inspection may become obsolete.?To reap the benefits of performance-enhanced processing, businesses must comprehend the groundbreaking nature of AI Visual Inspection. It is a fundamental enabler for firms seeking to stay ahead of the competition and transform their operational processes.?It is only when companies understand how groundbreaking AI Visual Inspection is, that they will be able to benefit from performance-enhanced processing.

Tremendous Significance of AI Visual Inspection for Fabric：What is AI Visual Inspection Research?

Detailed Introduction of AI Technology：

Artificial intelligence (AI) is a computer science field that involves mimicking human intelligence behaviors for data analysis, learning modeling, and other tasks with minimal human intervention. It includes two subsets, machine learning and deep learning, which differ mainly in learning methods and algorithmic tools used to drive learning.?The input features for machine learning are artificially intervened, which may result in biases or omissions, but the requirements for the dataset are not strict. Traditional machine learning algorithms mainly include linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and naive Bayes methods. In contrast, deep learning is based on large and complete datasets, where computers learn, infer, and model themselves using algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN), continuously optimizing the model. CNN is a type of feedforward neural network algorithm that includes convolutional calculations and has a deep structure, while RNN is a commonly used recurrent neural network structure that performs well in processing and predicting sequence data.

According to relevant studies, different ways AI is being applied to the textile industry, but the merger of AI applications is still in its infancy and the companies and makers in the textile industry are still experimenting with various avenues, thus gaining the research of AI Visual Inspection and Textile Processing in a significant quantity and quality.

Representative Researches of AI Visual Inspection for Fabric Inspection

"Automated Fabric Defect Detection and Classification Using Convolutional Neural Network"

The "Automated Fabric Defect Detection and Classification Using Convolutional Neural Network" is a research paper authored by Gargi Sharma and Dinesh Goyal. The paper proposes a novel approach for fabric defect detection and classification using Convolutional Neural Networks (CNNs), a type of deep learning model.

The primary objective of the research is to develop an automated system that can accurately detect and classify fabric defects, which is a critical task in the textile industry. Traditional fabric inspection methods rely on human inspection, which is time-consuming, labor-intensive, and prone to errors. The proposed system aims to address these challenges by using computer vision and deep learning technologies to automate the process.

The proposed approach consists of three stages: image preprocessing, feature extraction, and defect classification. In the image preprocessing stage, the fabric images are normalized, resized, and converted to grayscale to improve the accuracy of the defect detection algorithm. In the feature extraction stage, features such as edges, corners, and texture are extracted from the fabric images. Finally, in the defect classification stage, a CNN model is used to classify the defects based on their type and severity.

To evaluate the performance of the proposed approach, the authors conducted experiments on a publicly available fabric defect dataset. The results of the experiments showed that the proposed system achieved high accuracy rates for both defect detection and classification. The CNN model was able to classify defects with an accuracy of over 99%, which is significantly better than traditional methods.

Overall, the "Automated Fabric Defect Detection and Classification Using Convolutional Neural Network" paper presents a promising approach for automating fabric defect detection and classification. The use of deep learning and computer vision technologies could significantly improve the efficiency and accuracy of fabric inspection, reducing the cost and time associated with manual inspection.

Revolutionizing Industries: How Companies Are Utilizing AI Visual Inspection?

According to a recent report about machine vision market size research from the GRAND VIEW REASERCH, the global market for machine vision, with a valuation of USD 16.89 billion in 2022, is poised to exhibit a compound annual growth rate (CAGR) of 12.3% from 2023 through 2030.?The market's expansion is attributed to the surging demand for quality inspection and automation across diverse industrial verticals, with the automotive, food and beverage, pharmaceutical and chemical, and packaging segments witnessing a need for vision-guided robotic systems. Moreover, the upsurge in demand for application-centric machine vision systems is expected to drive the adoption of this technology over the forecast period. Machine vision systems rely on computerized observation, inspection, and scrutiny of work performance through video cameras, digital signal processing, and analog-to-digital conversion. The data obtained is then analyzed by the computer to yield the desired output. Resolution and sensitivity are two key facets of any machine vision system, with resolution enabling object differentiation and sensitivity facilitating object detection, even in low-light or invisible wavelength conditions.

And the market research report by MarketsandMarkets?shows that?the global AI in manufacturing market size is expected to reach USD 16.7 billion by 2025, growing at a CAGR of 57.2% during the forecast period.?The report indicates that the increasing demand for industrial automation and the growing need for quality inspection and control are the major drivers of the AI visual inspection system market. Until this point, numerous enterprises have endeavored to integrate the AI Visual Inspection into their manufacturing processes, management procedures, and even the supervision of their corporate content.

Example 1: Foxconn

As per a recent news report, Foxconn, a colossal producer of electronic goods, has implemented a state-of-the-art AI-based visual inspection system to enhance the efficiency and precision of their manufacturing process. This?system deploys advanced computer vision and machine learning algorithms to scrutinize electronic components, particularly printed circuit boards (PCBs), and curtail the need for human oversight.

The AI-based visual inspection system functions by assimilating images of PCBs and juxtaposing them with an extensive repository of standardized images. Upon detecting any inconsistencies, the system promptly notifies and labels the PCB as faulty, effectively removing it from the production line for thorough inspection and correction.

Foxconn's embrace of this technological advancement is propelled by the exigency to cut production costs and augment quality control. The conglomerate anticipates that the AI-based visual inspection system will intensify productivity by diminishing reliance on human scrutiny, which is typically sluggish and less precise. Furthermore, the system is poised to enhance the excellence of their merchandise, resulting in fewer returns and reduced expenses associated with defects. Reports indicate that the AI-based visual inspection system has already been efficaciously deployed in numerous Foxconn factories located in China, and the corporation plans to extend its implementation to other factories imminently. Despite the potential shortcomings of the system, such as the likelihood of false positives or?the?inability to detect all types of defects, Foxconn is convinced that the benefits far surpass the risks.

Example 2: BMW

BMW has successfully integrated an AI-based visual inspection system in its production facilities to conduct meticulous quality checks on car components, including engines and transmissions. The system adeptly captures comprehensive images of the components and utilizes advanced deep learning algorithms to unmask potential flaws and discrepancies that evade human visual scrutiny. By identifying and flagging these defects early in the production process, BMW can guarantee that its?cars adhere to the most exacting quality benchmarks. Moreover, the system furnishes BMW with valuable insights into its?manufacturing process, allowing them to pinpoint areas that are most susceptible to defects and proactively implement remedial measures to avert them.

Example 3: SUNTECH Textile Machinery

SUNTECH Textile Machinery has designed and developed an AI Automated Visual Inspection System?called ST-Thinkor?that has garnered extensive adoption across a diverse range of industries, spanning fabric production, garment manufacturing, home textile production, electronic and industrial glass fiber production, automotive interior and airbag manufacturing, and non-woven fabric production, among others.?Notably, the system's versatility is highlighted by its seamless integration with an array of machinery, including conventional fabric looms, glass fiber looms, fabric inspection machines, laminating machines, stenter machines, shrinkage machines, and non-woven machines.?Furthermore, its adaptable design permits it to accommodate an assortment of fabric types, ranging from?denim, wool, and chemical fibers to cotton, leather, automotive interiors, glass fibers, airbags, and non-woven fabrics.

ST-Thinkor AI Automated Visual?Inspection System

ST-Thinkor AI Automated Visual Inspection System boasts a comprehensive suite of advanced technologies that allow it to accurately identify a wide array of textile defects, ranging from broken ends and filling to knots, holes, dirt, fuzziness, weft shrinkage, and beyond.?With its unparalleled ability to detect any type of irregularity, this state-of-the-art solution ensures optimal production quality and enhanced customer satisfaction.

Things You are Curious about ST-Thinkor

1.?Inspection Accuracy:

Our system achieves an impressive accuracy of 0.1mm, which can be customized to fit your specific requirements. This means you can rest easy knowing that even the tiniest flaws won't escape our advanced detection mechanisms.

2.?Reached Detection Rate:

Conventionally, inspecting fabric defects manually can only make the defects detection rate 90% highest, while our AI Automated Visual Inspection System achieves?a 95%?defects detection rate,?with continuous learning capabilities enabling?it to enhance its defect identification processes and optimize flaw detection, giving you the ultimate in quality control.

3.?The Most Inspection Speed:

Typically, the inspecting speed actually depends on the machine types our system applied and the camera numbers, and the?normal inspecting speed of a natural person can be reached to 10-15m per min,however, at ST-Thinkor AI Automated Visual Inspection System, customers can choose the perfect cloth inspection speed to suit their needs. Our system is equipped with state-of-the-art 50-megapixel high-speed cameras that offer exceptional imaging capabilities, enabling fully automated inspections at speeds of up to 150 mm/min. Plus, with 24-hour real-time monitoring, you can be sure that every inch of your fabric is thoroughly inspected.

4.?Equipped Cameras Quantity:

We equip a corresponding number of high-speed cameras according to the fabric width and the required inspection accuracy.?For instance, a 1.5m width fabric can be covered by four high-speed cameras, achieving a fully automatic inspection with an accuracy of 0.4mm at a speed of 60m/min. And the more cameras you add, the higher the inspection speed and accuracy you can achieve!

5.?Defects Reporting Management & Reporting Data:

Our centralized management system allows for the convenient saving and tracing of defect reports. The system's advanced information-sharing functions enable you to acquire data from various detection systems via a central computer, ensuring accurate and timely reporting. Essential details such as the type of defect, defect score, coordinates, and their corresponding location on the defect map are included in the report, along with comprehensive information about the fabric roll, such as production date, fabric type, length, width, and the responsible person.

6.?ERP/Mess Communication:

Our system boasts seamless integration with top-tier factory management software, including ERP and MESS. This means you get comprehensive management tools that allow for the easy display of coordinates and defect images. By incorporating this data into a fabric defects map, our product optimizes cutting and enhances overall performance, making it the perfect choice for any textile production line.

SUNTECH Textile Machinery?stands out as a preeminent manufacturer of textile machinery that blends innovation with optimization, streamlining the production process and elevating efficiency. Firmly committed to quality, innovation, and customer satisfaction, SUNTECH?Textile Machinery?has cemented its status as a reputable brand in the textile industry, presenting a diverse assortment of cutting-edge solutions that cater to the needs of a broad range of clients. From advanced fabric inspection and packaging systems to high-speed weaving machines, SUNTECH?Textile Machinery’s comprehensive portfolio of state-of-the-art equipment equips textile manufacturers with the tools to enhance productivity, reduce costs, and remain competitive in the dynamic marketplace of today. Experience the full potential of SUNTECH?Textile Machinery and take your textile production to the next level.

Experience the future of textile inspection with SUNTECH Textile Machinery！

>>> https://www.suntech-machinery.com/