AI for Defect Detection in Manufacturing Industry: A Comprehensive Guide

Overview of AI Defect Detection 

AI-driven defect inspection systems are powered by computer vision, deep learning and related technological advancements that assist manufacturers to enhance quality control and reduce the rate of commonly occurring errors. Over the years, these systems have revolutionized methods of detecting and analyzing product-related defects in real-time with great speed, accuracy at reduced costs and have found various industrial applications such as in smart manufacturing 4.0 environments, logistics and inventory control. They improve production yield, process control and lower the amount of waste generated in usual circumstances. With the increase in product quality, customer satisfaction increases, leading to lesser warranty claims and after- sales defects. Unpredictable and varied faults and inefficiency in alignment of parts during assembly have been drastically reduced as more and more defects are recognized and classified. The AI damage detection market was valued at US $3.5 billion in 2021 and is expected to grow to US $5 billion by 2026 at a CAGR of 7.5% during this period. 

Market size of industrial defect detection from 2018 to 2032 

Types of AI Damage Detection 

AI models are trained on large datasets and are tuned as per business goals to deliver visual inspection, audio inspection, vibration and chemical analyses. A detailed context of the same is given below – 

1. Image or Video-based Defect Detection: It involves machine vision systems that utilize cameras and image processing algorithms for inspecting product defects such as shiny, transparent, complex surfaces or cosmetic flaws on intricate surfaces, inaccuracies in dimensions, visual anomalies etc. that are near to invisible to the naked eye. Deep learning models such as Deep Convolutional Neural Networks (DCNNs) learn to identify complex patterns, subtle defects and offer higher accuracy and efficiency than manual methods of quality assurance. 

2. Sensor-based Defect Detection: AI-driven acoustic sensors, photometers, colorimeters and thermographic cameras (static or mounted on drones) can detect high-frequency waves or abnormal frequency and amplitude of vibrations that are emitted by cracks, leaks etc. in the product or machinery. Machine Learning (ML) algorithms trained on supervised vs unsupervised learning, enable such types of AI damage detection that can be used to reveal mechanical defects, signs of wear and tear in rotating equipment like pumps, fans, compressors, motors, generators, abnormal noise during functional testing of wind turbines that generate renewable energy and other products, which may lead to potential hazards.  

3. Spectroscopy-based Defect Detection: It involves infrared thermography that detects variations in temperature on the product’s surface by using infrared cameras. Ultrasonic testing is another method for identifying internal anomalies in materials where AI-driven system analyzes ultrasonic signals by using high-frequency cameras. 

4. Chemical Analysis: This type of industrial defect detection recognizes irregularities in chemical makeup, reactions produced and properties of manufactured materials and products. It is used for quality control of various food products, chemicals, ingredients etc. which are delivered to schools, universities, research, healthcare and defense facilities, and prevent product failures, hazardous combustive reactions and unnecessary recall costs. 

5. Data-driven Defect Detection: By using historical data of working of machinery, equipment installed in manufacturing plants, ML models analyze trends, patterns and forecast their failure for predictive maintenance. Algorithms such as Isolation Forest, One-Class SVM and Autoencoders are widely used now a days to detect anomalies. Text-based defect detection uses Natural Language Processing (NLP) to analyze maintenance logs, client feedback and inspection reports to indicate defects. Integrated multimodal systems combine multiple types of data like images, sensor-based, text etc. to improve accuracy and reliability of the comprehensive AI defect detection approach. Generative Adversarial Networks (GAN) can generate synthetic data to augment training dataset in case of rare defects. Different operating conditions can be simulated by using Computer-Aided Design (CAD) to predict potential problems in manufacturing. 

Implementation of AI Anomaly Detection 

AI-based vision inspection for manufacturing works with the help of AI pattern recognition, where the system finds similarity in defect patterns in real-time with its training data. It records and actively learns regarding new defective variants encountered in the process by employing deep neural networks, which significantly improve its ability to detect cracks, weld flaw, object anomalies etc. Such non-destructive testing (NDT) methods that completely rely on defect detection using image processing have shown exceptional accuracy in defect detection. Given below is a detailed account of their working – 

1. Data Collection: Large datasets are gathered from multiple sources like video records using intelligent video analytics etc. and preprocessed for better input quality, as they directly impact the model’s performance. The data cleaning process removes inconsistencies, outliers and irrelevant data that affect its results. ML algorithms annotate images and video frames with respect to types of defects observed as a part of supervised learning. This can also be done by video or image annotation services providing companies. 

2. Development: Based on business requirements, pre-trained deep learning models may be used, or models can be custom developed for AI anomaly detection. Certain factors to be considered during the training include dataset volume, costs involved, lighting conditions, size and volume of objects or products under inspection, flaws, type and categories of defects and image or video resolution. CNNs are considered ideal for visual defect detection, and they can easily process pixel data, maintain spatial hierarchy between them, detect shape and texture-based anomalies. In case of low amount of data labeling and annotation, and unexpected types of defects, Autoencoder models that utilize unsupervised learning technique can be used, in which anomalies are detected by learning compressed representations of objects with the help of computer vision object detection and measuring the reconstruction loss. 

Types of defect detection in manufacturing 

3. Evaluation: Testing datasets are used to test the developed DL model, its performance accuracy and gaps in recognizing different types of defects. The model undergoes continuous improvement with algorithm refinement, constant updates and detailed documentation of statistics, and adjustments in training. 

4. Deployment: Once the working of the model is validated, it is initially deployed on a pilot scale for real-time defect detection using image processing.  The system is connected to machinery in the production line by using IoT gateway solutions. The data received and analyzed by the same are stored in local servers or by cloud migration services for further improvement in accuracy. The statistics generated need to be analyzed periodically for defect pattern recognition and fine-tuning of DL algorithms. 

Applications of Industrial Defect Detection 

Let us discuss further some of the sub-industrial applications of AI-based defect inspection, per say, manufacturing of vehicles, machines, FMCG products, clothes, equipment, medical device development services etc.  

Automotive 

In automotive manufacturing companies, AI-based visual inspection system can be used for detecting miniscule flaws such as paint blemishes and irregular paint coatings on parts, surface scratches, dents, errors in printing of chassis or vehicle identification number etc. The system is enabled with deep learning algorithms that are trained to analyze thousands of images to identify flaws, defects, and differentiate between acceptable and unacceptable parts before assembly. Another procedure of quality control begins as the vehicle parts pass initial NDT and reach the assembly line, which is followed by final inspection of finished product and failure prediction of the same. Audio-based AI anomaly detection is used to detect changes in engine noise and sound patterns of rotating parts like rotors, AC compressors, prevent mechanical failures and ensure the highest standard of manufacturing. 

Healthcare 

AI-based defect detection in manufacturing of healthcare equipment, instruments, prosthetics and machines has been adopted by industry leaders to ensure accurate alignment and working of the same as they closely affect human lives. Few examples include manufacturing of da Vinci surgical systems by Intuitive Surgical, MAKO robotic surgery arm by Stryker, MRI scanners and X-ray machines by Siemens, CT scanners by Philips, LINAC radiation therapy machines by Elekta, PET diagnostics scanners by GE Healthcare, ECMO by Medtronic, dialysis machines by Baxter, anesthesia machines by Getinge and many more. Computer vision in healthcare is used for functional testing which involves detection of abnormalities in medical images such as X-rays, MRI etc. produced by some of the machines and identifying burrs on surgical instruments. Chemical analysis is used to test the effectiveness of antibody strips that detect diseases in blood, urine tests and medical conditions like AI breast cancer detection during diagnostic procedures, while OCR techniques can identify label or container defects in pharmaceutical manufacturing plants to ensure healthcare industry regulations are followed. 

Aerospace 

Considering the costs involved in the manufacturing of aerospace equipment, machinery etc., computer vision for defect detection plays an important role in inspecting aircraft components for cracks, corrosion spots, hairline fractures and material fatigue. Few examples include manufacturing of commercial airplanes like Boeing, helicopters like H-60 Black Hawk by Sikorsky, Bell 429, military aircrafts like Eurofighter Typhoon, cargo aircrafts like Antonov An-225 Mriya, jet engines like Trent XWB by Rolls Royce, Merlin Engines by SpaceX, communication and observation satellites like Inmarsat and Sentinel Series by Airbus, spacecrafts like Atlas V by ULA, Rosetta by ESA, International Space Station (ISS) by NASA, space probes and rovers like Mars Curiosity Rover etc.  

Retail 

Visual inspection software can be used for automating textile manufacturing with advanced optical character recognition (OCR) capabilities that can be used to identify misprinted artwork on clothes, brand logos, incorrect stitching, inconsistent textures, low fabric quality, unmatching colors etc. They are useful in ensuring the integrity of circuit boards, identifying missing components, classifying damaged delicate devices, minimizing errors and reducing e-waste in consumer electronics and products. Few companies that have deployed AI damage detection during product inspection on production line, packaging line and prior to delivery, include Alibaba, Baidu, Philips, Whirlpool etc. Few types of neural networks like Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) are beneficial for continuous mass production processes such as manufacturing of textiles and wires, where temporal data like sequences of images, sensor readings are necessary for identifying anomalies. 

Advantages of computer vision for defect detection 

Others 

These systems can be used to automatically identify surface irregularities and air bubbles in glass panels and aluminum extrusions manufacturing. Any deviations from the required dimensional specifications can be identified. AI-powered UAV land surveying, or inspection of areas or buildings under construction is very useful in avoiding accidental pipeline gas leaks, damage in infrastructure etc. They can identify potential issues in manufactured solar panels in the Energy industry by spotting cracks, soiling effects, malfunctions in power output and reduced product life span during functional testing. 

Conclusion 

The companies involved in the Manufacturing industry are continuously striving to achieve more efficient and accurate results with real-time defect detection. Computer vision for defect detection has been playing an important role in supporting their end goals at reduced costs and has been aptly considered a game-changer. AI is able to process vast amounts of sensor and vision-obtained data at enormous speeds which is practically not possible through manual labor. This has paved the way to precise operations aided by predictive maintenance for enhanced defect detection and considerable profits in this industry. KritiKal can assist you in developing and deploying advanced AI-based defect detection as per your requirement. In this blog, we came across some major companies, some of which have been served by us, that have deployed this technique as a part of their standard manufacturing procedure as it is becoming the common norm these days.  

KritiKal’s deep learning-based technology platform provides fast and accurate solutions for AI-based defect inspections like barcode reading, 360° inspection, 3D imaging, robotic guidance, and surface inspection. Our AI-enabled machine vision technology with accurate defect classification capabilities requires significantly less training time as compared to rule-based machine vision systems and supports decision-making on the fly. Let us help you stand out from the crowd with our state-of-the-art technologies. Please email us at sales@kritikalsolutions.com to discuss the same. 

Lalit Chakradhari

Lalit Chakradhari currently works as a Lead Engineer in the Software System division at KritiKal Solutions. He is proficiently skilled in handling various software platforms and working with different types of software and programming languages. He has consistently delivered high-quality projects and helped KritiKal in catering to some major clients.