AI in Industrial Manufacturing: AI-based Quality Control and Defect Detection

I’m excited to write this next piece! And I'm glad to announce we have an AI-generated podcast available on this piece too!

After the overwhelmingly positive response from my last post on “AI in Financial Services: AI Personalized Financial Services” - and the now numerous requests I’ve received to speak at Supercomputing 2024 in Atlanta next week on how AI in industry (like how AI can aid in Digital Twins and Simulation for manufacturers, how AI can help manage Research Data in universities, and how AI can assist in improving patient predictive analytics in healthcare) – I’ve decided to write my next piece here on AI-based quality control and defect detection for industrial manufacturers.

It's so interesting how AI can be a vehicle to deliver our customers a better product, deliver a better experience, and deliver better service – if we just take a moment to slow down and look at how to do it.

Again – we’re all learning here, so if you’re in the industrial manufacturing space, weigh in with comments – we all want to get better, and to improve fast.?

And we do that TOGETHER.

So let’s dive in.

What’s happening in the industrial manufacturing space

The industrial business space, particularly the area of the market for manufacturers who produce goods or capital equipment for use in manufacturing and construction, is witnessing rapid evolution in 2024 - customer demands for heightened efficiency, precision, and reduced downtime, fueled by the global drive toward automation, digital transformation, and Industry 4.0 advancements.

In the past, many businesses experimented with digital and automation solutions, but now there is a stronger push toward integrating AI-enabled solutions for quality control, predictive maintenance, and end-to-end supply chain optimization.

Customers are now expecting vendors to provide solutions that are highly automated, adaptive, and capable of making data-driven adjustments in real-time. The heightened focus on quality control and defect detection reflects this shift, with businesses expecting solutions that integrate seamlessly with existing production lines while delivering value beyond traditional inspection methods.

What is Quality Control?

Quality control (QC) is a systematic approach to ensuring products meet specific standards before they reach the end customer.

In the industrial business space, quality control represents a commitment to producing goods that are tested thoroughly to avid defects, to meet regulatory standards, and to exceed customer expectations. ?When effective, QC minimizes waste, reduces rework, and helps maintain brand integrity. ?

And that results in better customer satisfaction, reduced production costs, better adherence to regulatory compliance, and better product consistency.

What is Defect Detection?

Defect detection involves identifying flaws in products at any stage of production, ranging from minor imperfections to severe faults that could compromise safety or functionality.

In the industrial business vertical, defect detection is particularly crucial as products must meet stringent standards for durability and safety. Effective defect detection identifies issues such as cracks, misalignments, and other imperfections that can lead to equipment failure or safety hazards.

Implementing effective defect detection means lower risk (which can damage your brand), better operational efficiency with less defects impacting production, and longer product lifespan when your products have less defects.

QC and Defect Detection Today

Traditional quality control and defect detection methods often rely on manual inspections and rudimentary automated checks. These methods are prone to human error, fatigue, and inconsistencies, especially as the complexity and volume of products increase.

Conventional inspection equipment may struggle to identify micro-defects or subtle surface imperfections that could later compromise product integrity. And these methods are time-consuming and limit production speeds. They don’t have the predictive capabilities to identify defect patterns over time, which AI-powered systems can accomplish by analyzing historical data.

?Implementing an AI-based quality control and defect detection, especially when powered by deep learning and computer vision, can inspect products in real-time on the production line with far greater precision. ?AI systems can identify defects down to the microscopic level with near-perfect accuracy, reducing errors and increasing product reliability. Automated defect detection minimizes the need for manual inspections, lowering labor costs and improving efficiency.? And AI can provide immediate feedback, allowing for quick adjustments that reduce downtime and improve production continuity.

The Stack for AI-based Quality Control and Defect Detection

I’ll touch on each of the layers of the stack, but will be brief.? Drop me a DM or email me at [email protected] if you want to chat more about any of these layers.

- Compute: ?The 英伟达 H100 accelerator offers unparalleled performance for deep learning and computer vision tasks, processing large volumes of image data in real time. And the support for transformers and large neural networks enhances its capability in complex defect detection scenarios. The GB200 accelerator (Grace Blackwell) delivers high-bandwidth memory, essential for managing vast datasets and maintaining performance in resource-intensive environments. These accelerators reduce processing time for defect detection algorithms, enabling real-time inspection with zero lag.

- Storage and Data Acceleration:

- DDN Exascaler provides a high-performance storage system that supports the rigorous demands of AI workflows. Its scalability allows businesses to handle petabyte-scale data, essential for training and running large deep learning models. It is engineered to handle data-intensive applications, delivering exceptional throughput and low latency. This ensures rapid processing of high-resolution images and sensor data, which is crucial for real-time defect detection. ??And as your manufacturing operations grow, EXAScaler is proven to seamlessly scale to accommodate increasing data volumes and processing demands, maintaining consistent performance without significant infrastructure changes. ?Because DDN EXAScaler is optimized for AI and machine learning workloads, providing efficient data access and management, the integration accelerates the training and deployment of AI models for quality control applications.

- The upcoming DDN Infinia solution complements Exascaler with intelligent data management, enabling data to flow seamlessly from storage to compute layers, facilitating rapid data retrieval and model training with zero bottlenecks. ?It’s suited for managing diverse workloads, including analytics, and distributed data applications, offering a unified solution that balances performance with ease of management.? It provides you with built-in multi-tenancy and advanced security measures, ensuring data protection and isolation across various workloads and users.

- Networking: NVIDIA InfiniBand drives low-latency, high-throughput connectivity between compute and storage. This model of networking is highly recommended IMHO. This network’s ability to handle massive data transfers makes it ideal for real-time quality control applications, where any delay could disrupt production. InfiniBand is designed to provide ultra-high bandwidth and low latency, allowing for the rapid processing of large volumes of data. This is crucial for AI-driven quality control, where high-resolution images and sensor data need to be processed in real-time to detect defects and anomalies effectively. InfiniBand also enables a scalable infrastructure that can support increasingly complex AI models and higher data volumes as manufacturing operations grow. With its ability to connect numerous nodes seamlessly, InfiniBand can expand with minimal impact on performance. RDMA allows data to move between servers without involving the CPU, reducing bottlenecks and improving processing speeds. This is particularly beneficial for running AI algorithms efficiently, leading to faster model training and inference in defect detection systems. Finally, InfiniBand networks come with advanced error-checking and recovery capabilities, ensuring data integrity and reducing downtime. For a manufacturer, this translates to more reliable quality control operations with minimal interruptions, ultimately leading to better product quality and reduced waste.

- AI Middleware and Software:

- NVIDIA NVAIE (NVIDIA AI Enterprise) offers a suite of optimized AI libraries and frameworks for deploying AI applications, including deep learning for defect detection. ?NVAIE provides a suite of AI tools and frameworks, including the NVIDIA TAO Toolkit, which simplifies the training and deployment of AI models for defect detection, enabling manufacturers the ability to fine-tune pretrained models with minimal coding, accelerating the development process. NVAIE is also optimized for NVIDIA GPUs, delivering the computational power necessary for real-time analysis and processing of high-resolution images and videos. This ensures efficient and accurate defect detection, even in complex manufacturing environments.? It is designed to integrate seamlessly with existing manufacturing systems and workflows, facilitating a smooth transition to AI-driven quality control without significant disruptions.?

- NVIDIA’s CUDA accelerates computations across GPUs, ensuring the AI models are fully optimized for NVIDIA hardware. CUDA enables parallel computing by allowing AI models to run on NVIDIA GPUs, which is crucial for quality control solutions as described here, where high-resolution images and real-time processing are essential for detecting defects quickly and accurately.? CUDA is also supported by leading deep learning frameworks such as TensorFlow and PyTorch, which manufacturers can use to build defect detection models. With CUDA, these frameworks run faster on NVIDIA GPUs, allowing manufacturers to train models more efficiently, reducing the time it takes to develop and fine-tune AI algorithms. Finally, In a quality control setting, detecting defects as they happen is critical to minimizing waste and rework. CUDA’s GPU acceleration powers real-time inference, enabling defect detection models to process live data from production lines without delay, ensuring that defective products are flagged immediately. (CUDA is well-documented and supported by a large developer community – so you can find great resources and help as well).

- NVIDIA NIMS simplifies model orchestration and deployment, allowing manufacturers to manage their entire AI infrastructure from a single interface, simplifying the complex process of monitoring and maintaining AI workloads across multiple GPUs and nodes. This centralization ensures seamless operation of quality control systems, especially in large-scale manufacturing environments.? NIMS provides dynamic resource allocation, enabling the efficient use of available GPU resources across different AI tasks. For quality control applications, this means AI models can run continuously or on-demand, depending on production needs, ensuring that GPU resources are not wasted and are always available for defect detection tasks.? NIMS also provides real-time health monitoring of AI hardware, helping manufacturers detect potential issues before they disrupt quality control processes. With predictive maintenance capabilities, NIMS can prevent downtime by alerting teams to potential hardware failures, ensuring continuous operation of defect detection models.

- NVIDIA TRT-LLM and Triton Inference Server streamline inference processes, handling the deployment of deep learning models across multiple systems, making real-time defect detection feasible even at large scales.

?- Application Software: Here is where we implement software for defect classification, anomaly detection, and performance monitoring. Open-source solutions like OpenCV and TensorFlow can be incorporated alongside commercial software to build customized applications that match specific defect detection needs.? Some commercial options include

- MATROX Imaging Library (MIL), which offers a comprehensive library for image analysis, pattern recognition, and machine vision applications, and tools for image capture, processing, analysis, and interpretation, with strong support for industrial automation.?

- Cognex VisionPro and Cognex Designer, which offers a suite of tools for high-accuracy image analysis, commonly used in manufacturing and automation, as well as robust image processing, inspection, and identification capabilities, including barcode reading and object detection.

- NVIDIA Triton Inference Server, which offers an optimized for production-level deployment of deep learning models, supporting frameworks like TensorFlow, PyTorch, ONNX, etc, as well as high-performance inference capabilities and can be deployed across various platforms.

- H2O.ai, which offers AI and machine learning platform offering tools for automated machine learning, deep learning, and predictive modeling, as well as strong support for structured and unstructured data, including image data.

?? - These applications can interface with the production line, monitoring products in real-time and flagging defects instantly. Data analytics software provides insights into defect trends, helping businesses identify root causes and optimize production processes.

The bottom line

For manufacturers in the industrial sector, especially those producing goods or capital equipment, implementing an AI-based quality control and defect detection system could dramatically enhance production efficiency, product quality, and operational cost savings. By catching defects earlier, you can reduce waste, rework, and returns, all of which contribute to lower production costs and improved profit margins. And high product quality and consistency enhance customer satisfaction and loyalty, which can lead to repeat business and a stronger brand reputation.?

And here’s an important aspect I see a number of interested customer overlooking: as your AI-based system expands, your AI stack should offer scalability that grows with your needs, supporting future data and processing requirements without a major overhaul.

Without a clear view to implementing this, you run the risk of implementing alternatives that may require additional infrastructure or support, ending up with higher overall operating costs due to inefficiencies in data handling and potential storage limitations. ?Or worse - inferior data throughput and latency might reduce the effectiveness of AI models, impacting quality control and leading to a higher rate of undetected defects.

A final word

As part of my role at DDN, I partner heavily with my server, infrastructure, software and services alliances – many who you know, and many who you work with and / or buy from.?

If you’d ever like to talk, whiteboard, etc with me and/or your server vendor of choice, your systems integrator of choice, etc – drop me a line at [email protected], let me know who to pull in, and I’ll take care of arranging everything.

Let’s improve our customers’ experience with AI.

More to come. :-)

Until next time,

JOSEPH

[email protected] ?