On May 15, China Fibre Inspection Journal Co., Ltd. joined with Guangdong ChiuVention Instrument Co., Ltd. and Shenzhen Future Lab Technology Research Center Co., Ltd. The AI-Powered Innovation Conference on Testing and Inspection Technology and Equipment took place at the FuLi Mandarin Wanda Hotel in Dongguan. It had a grand debut. This event drew more than 250 textile testing experts and leaders from China and beyond. They came to show their full support.
This event features dry goods. Five speakers discussed key methods for textile smart testing instruments. They focused on achieving high repeatability and reproducibility. Topics covered include R&D ideas for smart textile testing tools and worldwide trends in testing functional fabrics. They also discussed setting up digital textile labs. This makes them more accurate, faster, and easier to trace. They explored the practical use of AI in smart testing as a final step. (This article will review the highlights of the event for you.)
The meeting had special guests. Wang represented the Dongguan Municipal Party Committee Political Research Office. Hou Chunting, Deputy Editor-in-Chief of China Fiber Inspection Magazine, was also there. They both spoke at the opening. They all showed confidence in AI for textile inspection and smart testing. They agreed that using AI is a clear industry consensus. It also points to the future of textile inspection and smart testing. 
（Wang represented the Dongguan Municipal Party Committee Political Research Office）
(Hou Chunting, Deputy Editor-in-Chief of China Fiber Inspection Magazine)
The five speakers at this conference cover five amazing topics. They will also share their smart testing insights with you.
No.1-Dr. Georg Schroeder, ChiuVention’s Strategic Management Expert
Presentation Topic – German Standards Help Chinese Companies Become Smart Instrument Leaders
In the textile industry, have you ever encountered such a problem? Lab A tests the same fabric, but Lab B rejects it. This “testing bias” harms the company’s credibility and could lead to large claims. How can we make textile testing results as accurate and reliable as car parts? Dr. Georg Schroeder is a German expert in strategic management. He once led a top testing company. He shared important methods for testing textiles. His speech focused on ensuring high repeatability and reproducibility. His core philosophy is that “Inspection is not ‘about right’, but must be ‘consistent’.”
Today, we will break down his speech. We’ll look at how to reach “zero error” in textile smart testing!
Why does textile testing need “automotive-grade” precision?
In the textile industry, “R & R” stands for repeatability and reproducibility. It is the gold standard for measuring testing reliability.
- Repeatability: It means using the same machine to measure the same cloth again and again. Are the results consistent?
- Reproducibility: Can different people and labs measure the same cloth and get the same results?
If R&R is not up to standard, it will appear:
- Customer complaints (“How come you failed last time?”)
- Standard dispute (“Your testing method is different from mine!”)
- Trade risks (export test reports are not recognized).
Dr. Schroeder says, “Textile testing instruments need to be ‘zero deviation,’ like in the automotive industry. This is key to gaining trust in the global market.”
The four keys to making the test results 100% reliable: How to achieve high R&R?
Dr. Schroeder shared the four core methods of German precision manufacturing:
1.1 Precision Manufacturing: “Zero Error” from the beginning of the parts.
- High-precision parts: Each part of the testing instrument needs strict tolerance control. For example, the clamping force of the fixture cannot deviate by more than ±0.1%.
- Standardized production: CAD/CAM systems ensure that every part is identical.
- Intense prototype testing: new instruments must complete the “3 x 3 x 3 test.” This means we will use 3 prototypes in 3 tough environments for 3 months to check for stability.
1.2 Measurement system: Regular “check-ups” to reduce errors.
- Gage R&R study: Different operators often measure the same sample with the same machine. This helps analyze error sources.
- Auto-calibration: The equipment calibrates itself at set intervals. For example, it performs laser calibration after every 100 tests.
- Environmental control is key. Keep the temperature stable between 15-40°C. Also, maintain humidity at or below 85%. If not, the test data will drift.
1.3 Production management: control each link with the “German standard.”
- SOP (Standard Operating Procedure): Write down each step to help everyone work the same way.
- “1+3” supply chain strategy: key parts are self-made. The team chooses at least three suppliers for non-core parts to avoid a ‘necklace’ effect.
- 100% factory inspection: Every instrument must pass a performance test before shipping. This ensures that there are no defects.
1.4 Quality Management System (QMS): Let “precision” become a habit.
- ISO certification: At least ISO 9001 (quality management) or ISO 17025 (laboratory competence).
- Continuous Improvement: Collect customer feedback regularly to optimize testing methods.
- Talent training: Inspectors should know how to run machines. They also need to analyze data and understand standards.
Real cases: How to optimize testing with R&R research?
Dr. Schroeder gave a typical example:
Problem: A lab found that technicians report very different tensile strength results for the same fabric.
Solution:
- Repeatability test: One technician measures 10 times with the same machine. Then, calculate the range of fluctuations.
- Reproducibility test: Have three technicians measure the same sample on their own. Then, check the differences in their methods.
- Optimization solution: The fixture clamping method was uneven. So, we updated the SOP. Now, all technicians must use a consistent method.
- Results: 70% reduction in detection deviation; customer complaints reduced by 50%.
Three proposals for the textile testing lab
Don’t look at prices; check the accuracy too. The high-end market pays for “reliable data.”
- A small field achieved a breakthrough. It is the first in a testing subdivision, like color fastness, to meet global standards.
- Join in setting standards. To lead in the industry, you need to communicate the standards clearly.
- Conclusion: Precision testing is the future of the textile industry!
The core of Dr. Schroeder’s speech was simple: “Testing is not ‘roughly the same,’ but must be ‘consistent’.” To earn the trust of the global market, labs, third-party testers, and instrument makers must go all out with R&R.
No.2-Alvin Lee, Former Vice President & R&D Director
“A good smart testing tool should act like a smartphone. The more you use it, the better it gets to know you!”
Shrinkage testing in textile labs can be a hassle. Manual measuring and tricky data recording take time. Plus, results can differ depending on who does the smart testing. How to subvert traditional testing with AI + IoT?
In his speech, Li Fumin, R&D Director of ChiuVention, shared the R&D story of the SmartShrink intelligent shrinkage tester. This instrument solves a big problem in the industry that has lasted for years. It also boosts smart testing efficiency. The traditional method takes six minutes to measure a sample. SmartShrink, but only needs five seconds!
The industry pain point: Why is shrinkage testing so difficult?
Traditional shrinkage testing has five major problems:
- Manual measurement relies on the technician’s experience; results fluctuate.
- Big data record: to hand-calculate, hand-copy the report, it’s easy to make mistakes.
- Cumbersome process: from measurement to report can take up to two hours.
- Unharmonized standards: different customers need different testing methods.
- Unable to link LIMS; data cannot be directly uploaded to the laboratory system.
Mr. Lee said, “The market lacks an efficient solution.” It’s not about taking a picture and measuring. It’s complex. You need to understand fabric traits, manage wrinkles, and adapt to various standards.”
SmartShrink core technology
Smart Testing Instrument: Fabric Shrinkage Tester
- Machine vision recognizes fabric edges while ignoring wrinkles and patterns.
- AI database collects over 100,000 fabric shrinkage data points. The more you measure, the more accurate your results.
Truly “user demand-driven”:
- One-click report generation that interfaces with the LIMS system without manual input.
- Supports 20+ international standards (ISO, AATCC, customer-defined).
- Scenarios fit the needs of garment factories, labs, and quality inspection groups.
- You can upgrade equipment remotely to meet new standards at any necessary time.
Why can’t peers imitate?
Alvin said that five manufacturers attempted to copy similar products. But they eventually left the market. The reason is—
- Only copy the hardware; ignore the AI database and algorithm iteration.
- Lack of IoT gene: unable to realize remote operation and maintenance/data docking.
- Slow response time: It is difficult to adapt quickly to new customer needs.
ChiuVention’s core strengths:
- Cross-disciplinary team: a fusion of textile engineers, AI algorithm experts, and IoT developers.
- Continuous evolution: monthly algorithm updates (e.g., new lace fabric detection module)
- Digital services: keep an eye on equipment status and warn you early about failures.
Mr. Lee talked about the R&D logic for other ChiuVention intelligent textile testing tools. These tools include the Smartindale and the HydroDetector. The HydroDetector is an AI-powered smart testing device. It finds results for hydrostatic pressure tests and more without any manual input. ChiuVention’s R&D team creates car engines. They focus on reliability and precision. They also collaborate with leading textile smart testing labs, manufacturers, and universities. This teamwork lets them share knowledge and introduce new ideas to textile testing.
Smart Martindale Abrasion and Pilling Tester (IoT / German-Engineered)
No.3 – Dr. Calvin Y.M. Lam, Textile Testing Expert
Speech topic –
- New Development Trends on Functional Fabric Testing Globally
- Managing Digital Textile Laboratory to be More Accurate, Efficient & Traceable
“Imagine sportswear that monitors your heart rate and curtains that purify the air. “These ‘black tech’ fabrics are a global hit!” New data shows the functional textiles market is set to top 52.8 billion U.S. dollars, or about 380 billion yuan, by 2033. It will grow at an annual rate of 6.5%.
How can we verify the “magical effect” of these fabrics? How to avoid the legal risk of “false propaganda”? Dr. Calvin YM Lam, a textile testing expert, shares his insights in this speech. He talks about three big tech revolutions. He also covers four important benefits of functional fabric smart testing.
Why has functional fabric smart testing become a “necessity”?
1.1 Consumers are voting with their wallets.
- Sports brands such as Adidas need their suppliers to have dual certification. This certification is for antibacterial and moisture-wicking features.
- Medical: Surgical gowns need to meet both blood permeability and breathability tests.
- Sustainable fashion: Brands such as ZARA use recycled polyester to help with sun protection.
1.2 No testing = high risk
- Legal risk: EU fines of up to 4% of sales for false advertising of UV protection.
- Crisis of confidence: A brand’s share price plummeted by 30% due to the leakage of “waterproof vests.”
Dr. Calvin YM Lam stated, “Functionality testing is no longer a bonus. It’s now a matter of life and death. It serves as both a tech barrier and a way to protect our brand.” moat and brand insurance policy.”
Global market map: Who is leading? Who is exploding?
2.1 Geographical pattern (2023 data)
- North America (35% share): high technical barriers, focusing on the medical/military industries
- Asia-Pacific (30% share, 7.5% growth): Chinese sports brands are driving testing demand.
- Europe (25%): most stringent sustainable certification
2.2 Material Competition
| Fiber Type | Market Share | Growth Driver |
| Synthetic Fiber | 40% | Smart wearables (e.g., temperature-controlled ski suits) |
| Blended Fiber | 35% | Antibacterial home textiles |
| Natural Fiber | 25% | Organic medical dressings |
2.3 Application Scenarios
- Apparel (50%): “Pressure Distribution Testing” is now trending for yoga pants.
- Healthcare (1st Growth Rate): “Strength Decay Curve” is gaining traction for biodegradable sutures.
In 2025, there are three major technology trends.
Trend 1: Smart textiles’ “dual testing” era
Traditional testing still needs to measure waterproof/breathable and other basic functions.
New item:
- Sensor durability: Is heart rate monitoring accurate after 100 washes?
- Will someone leak the body temperature data from smart pajamas?
Someone returned a military smart camouflage suit. It failed the electromagnetic shielding test.
Trend 2: “Hard Indicators” for Sustainable Certification
New Dimension of Testing:
- Filtration Efficiency vs Degradation Rate of Banana Fiber Masks
- Is the UPF value of recycled polyester lower than that of virgin materials?
Dr. Calvin YM Lam says, “GRS certification alone isn’t enough. You need testing data to show that the function doesn’t shrink!”
Trend 3: AI subverts traditional testing
- New AI application: puckering rating that simulates human vision with less than 0.5% error. Launching at ITMA 2025.
- Big Data Early Warning: Look at over 100,000 inspection reports to spot supplier quality risks early.
Key recommendations for textile enterprises
- Seize the medical track: medical textile testing gross margin as high as 60 %+.
- Set up a “dual certification” system. This includes functional certification for UV protection and sustainable certification for LCA.
- Invest in AI testing. You can cut labor costs by 50%. This is great for small and medium-sized labs.
In Dr. Calvin YM Lam’s second talk, he shared how to create a next-gen smart lab. This lab meets ISO 17025 standards and uses Internet of Things (IoT) technology. The following is a summary devoid of any engaging elements.
ISO17025 certification of the 3 major technical pain points and solutions
1.1 Personnel operation bias
Typical problem: When grading color fastness, the difference between different technicians’ judgments of “gray card 3.5 level” reaches ±0.7 level.
IoT solution:
- AI visual aid system prompts the grading deviation in real time.
- Video tracing of the whole operation process
1.2 Out of control equipment status
Tearful case: a laboratory, due to the lack of temperature and humidity records, the whole batch of down fluffiness tests was canceled.
IoT must do items:
- Tensile testing machine installed sensors, real-time monitoring of the load error
- Constant temperature and humidity box automatic alarm (beyond the range of 23 ± 1 °C)
1.3 Difficulty in sample traceability
Industry status quo: 75% of laboratories still use paper records of sample flow
- Reform program: each sample RFID labeling
- Cell phone scanning code to check: storage conditions/testing progress / responsible person
IoT landing in four major scenarios
Scenario 1: Intelligent monitoring of equipment (clause 6.4)
- The traditional way IoT upgrade program’s direct benefits is unclear.
- Monthly manual calibration, real-time monitoring, and predictive maintenance: equipment downtime reduced by 60%.
- Paper record data uploads to the cloud without requiring manual intervention. We cut audit prep time by 80%.
A third-party lab tested flame retardants for Nike. The IoT system detected a 0.5°C temperature fluctuation in the combustion box. So, they stopped the test to prevent incorrect reporting.
Scenario 2: Automatic Environment Regulation (Clause 6.3)
Key technology:
- Distributed temperature and humidity sensors (1 per 10 square meters).
- Linkage with the HVAC system. Automatic regulation.
- Measured data: color fastness test environment compliance rate increased from 72% to 98%.
Scenario 3: Transparent testing process (clause 7.8)
Customer’s favorite feature:
- WeChat real-time push testing progress
- The electronic report comes with the original data curve diagram.
- A foreign trade company earns the title of “preferred supplier” for ZARA.
Scenario 4: Quality Data Mining (Clause 8.9)
High-order application:
- We analyzed three years of data. We found that the pH test deviation rate on Thursday afternoon was 30% higher. This was due to a missed shift handover.
- Automatically generated radar chart of lab health.
AI and the IoT can boost textile testing labs.
Core value
AI and IoT can greatly improve the ISO/IEC 17025:2017 management system used in textile testing labs. This upgrade includes:
- Technical capabilities: enhancing testing accuracy and professionalism.
- Operational efficiency: optimize workflow and increase testing speed.
- Data integrity: ensure the authenticity and reliability of testing results.
- Compliance guarantee: meets all international standards.
Five Technical Advantages
These innovative technologies meet the key needs of ISO standards for inspection data:
- Accurate: AI algorithms drop human judgment bias.
- Reliable: Real-time monitoring by IoT devices ensures data quality.
- Traceable: Blockchain technology enables full-process traceability.
- Transparent: visualize the testing process; verify the results.
- Efficiency in Process Monitoring: An intelligent system operates 24/7.
Choose ChiuVention SmarTexLab’s intelligent textile testing solution to realize all the above empowerment.
No.4 – Relax Yan, Senior Product Manager at Future Lab Technology Research Center
Speech topic: Digitalization of Textile Laboratory
Manager Yan kicked things off by defining a real digital laboratory. Unlike a traditional “information laboratory,” it focuses on the full process. This includes receiving samples, planning tests, making samples, testing, and generating electronic reports. It merges three streams: data flow, operation flow, and sample flow into one digital twin. This setup enables automatic data collection. It also ensures an easy flow and clear visualization of lab operations. The digital solution manages the entire process. It covers sample reception, test planning, sample preparation, testing, and electronic reporting. It also creates a digital twin of data flow, operation flow, and sample flow. This setup allows for automatic collection, easy flow, and visual analysis of lab data. He then launched a detailed introduction to each of the above points. We summarize the core content as follows:
Four key stages of laboratory evolution.
L0 Traditional laboratory:
- Manual processes are often used.
- Serious information silos arise from system dispersion.
- We store test records in Excel.
- WeChat and email serve to communicate.
L1 Informatized laboratory:
- Basic system online.
- Paperless office
- Using LIMS to manage samples and reports.
L2 Intelligent Lab:
IoT devices handle over 70% of processes. They support big data-driven decisions. AI rates color fastness without human intervention. AGV robots deliver samples.
L3 Future Lab:
- Autonomous operation of the whole process.
- Self-adaptive change of the environment.
- Humans limit their responsibility to handling exceptions.
- Digital twin lab simulation test results.
- Blockchain depository report.
The three core elements of digital transformation.
Reconfiguration of data flow
- Three streams combined: Sample flow (using RFID tracking), Operation flow (via electronic SOP), Data flow (through automatic collection)
- Key breakthrough: The QLIMS system digitizes all 22 steps, from sample reception to the generation of reports.
- The intellectualization of equipment
Four types of terminals:
- PDA handheld terminals: on-site instant data entry (e.g., fabric defect photography)
- AGV robot: 24-hour sample transportation, error rate < 0.1%
- Intelligent warehousing: Find any historical sample in 3 seconds. This beats the old way, which took about 15 minutes.
- Environment monitoring: PM2.5, temperature, humidity, and 16 other parameters, AI linkage change.
Management paradigm upgrade
- Digital Kanban: Provides a real-time view of 12 KPIs. These include “inspection timeout rate” and “equipment use rate.”
- Cost control: Use the capacity analysis model to find the ROI of adding people instead of equipment.
No.5 – Neil Lee, General Manager at Future Lab Technology Research Center
Speech topic: AI-driven and empowered textile inspection changes
Neil, the general manager, introduced us to the topic of AI. AI is now used in many industries. It helps in e-commerce, smart Q&A, warehousing, logistics, and textile checks. He also shared future possibilities for AI in clothing inspection. We standardize this process to make textile testing easier. To improve the process, it’s helpful to break it down. This can link to AI or automation tech changes. Textile and clothing inspection uses various sensory tests. This makes it perfect for AI image recognition. The inspection process creates a lot of data. It also needs text analysis, which is perfect for AI technology.
Application challenges include:
- Textile and apparel products are flexible materials.
- Clamping samples and transferring them can be tough.
- Automating these processes is harder.
- Models struggle with generalization.
- Fashion attributes lead to strong product innovation and personalization.
- This creates a big need for AI training samples, making training difficult.
Immediately after that, he introduced the case of AI applied to fiber analyzers:
AI helps identify fibers like wool and cashmere. It uses deep learning and a neural network. This helps to extract features and classify animal fibers in a correct manner. Several layers of “neurons” make up the neural network.” It pulls out fiber features one layer at a time. These features include scale thickness, spacing, and arrangement patterns. The algorithm trains in several rounds. It learns over time to remember and recognize the visual traits of different fiber types. As the network layer goes deeper, the features change. They move away from specific shapes. They weigh the key indicators to emphasize them and clear out any noise. AI can quickly tell if a fiber is wool or cashmere. It does this by looking at all the fiber’s details for accurate classification.
Core Functions
- Complete automatic fiber quantitative testing
- Fiber fineness measurement
- Follow international testing standards.
- Efficiency Improvement: 10 times more efficient than manual testing.
AI Inspection Process
- Data collection: The microscope collects fiber images.
- Data labeling: manual labeling of fiber features (training set)
- Model training: neural network learning fiber classification rules
- Practical application: automatic identification of unknown fiber samples
Technical Advantages
- Accurate classification: A multi-layer neural network extracts features, including contour and scale arrangements.
- Dynamic weighting: We assign higher weights to important features (e.g., scale thickness).
Typical application scenarios
- Wool/cashmere composition testing
- Quantitative analysis of blended fabrics
Experts in testing met at SmarTexLab to explore how AI boosts textile testing.
Five speakers shared insightful content. Guests then visited ChiuVention before heading to the SmarTexLab intelligent textile lab. There, they saw a range of smart testing tools. Guests gained a better grasp of AI in textile testing. They saw its appeal up close.
AI technology is advancing at a swift pace. Textile inspection and testing have entered a new era. Now, it focuses on precision, efficiency, and intelligence. AI is improving how we detect things and changing lab skills and industry standards. It works with image recognition, deep learning, automatic classification, and trend warnings. Algorithms help us understand the tiny details of a fiber. So, when we analyze test data, we gain a clear view of the product’s quality. In the future, AI will be more than a tool. It will act as the “second brain” for textile testing labs. This change will help us use less manpower while handling complex tasks. Also, it will allow us to respond faster to market demands. For every industry practitioner, embracing AI is not a choice but a compulsory course. Technology is changing fast. Standards are evolving, too. The lab’s core competitiveness is improving.
Let’s use technology as our engine and intelligence as our fuel. We can improve textile testing to be more accurate, efficient, and trustworthy! Let’s join forces!
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