Industrial AI: Revolution of AI for manufacturing, logistics and supply chain
Published March 20, 2024
In the era of AI revolution, Industrial Artificial Intelligence (AI) has emerged as a transformative technology in the manufacturing and industrial sector. But what exactly is Industrial AI, what it is transformative and what should you know before embarking on an Industrial AI project? This article aims to demystify the concept and outline key considerations to help you start your journey on the right foot.
What is Industrial AI
At its core, Industrial AI refers to the application of artificial intelligence in industrial settings to solve specific problems and automate industrial processes. It encompasses the use of deep learning, computer vision, and other AI technologies to optimize production processes, enhance operational efficiency, improve safety, and reduce costs.
Unlike general AI, which focuses on creating systems that can perform general intellectual tasks that a human being can, Industrial AI is tailored specifically to address the unique challenges and needs of the industrial sector.
Revolution of Industrial AI
In recent years, AI has been undergoing a transformation with breakthroughs in generative AI, Large Language Models (LLMs), reinforcement learning, and Large Vision Models (LVMs) and many more. These inventions have opened many doors for automating manufacturing, logistics and supply chain processes using AI.
Traditionally, AI's application in industry was constrained by its integration into bulky, hardware-intensive systems, such as machine vision setups for visual inspection and object counting. These systems were not only costly and required elaborate setups, but they also demanded considerable space and were difficult to scale.
However, the landscape is rapidly changing with the latest advancements in AI technology. Vision AI for instance, is far more advanced today than what it was a few years ago and we can use an machine vision camera along with a vision AI algorithm to automate processes like product counting, defect detection, visual inspection, predictive maintenance etc without the need for cumbersome machinery and extensive setups. These machine vision cameras can be strategically placed and connected to a server that processes the video streams, offering a more flexible, cost-effective, and scalable solution to traditional AI applications.
Capability of Industrial AI in 2024
As we journey further into the 21st century, the industrial technology landscape is undergoing a dramatic transformation, largely driven by artificial intelligence (AI). The year 2024 marks a significant turning point, with Industrial AI showcasing remarkable capabilities that redefine efficiency, accuracy, transparency and speed across various sectors.
Defect detection using Machine Vision and Visual Inspection
Computer vision AI is revolutionizing visual inspection and defect detection across various industries by automating the analysis of visual data with exceptional precision and speed. Leveraging advanced algorithms and deep learning models, computer vision systems can accurately identify and classify defects in products, from tiny surface imperfections to structural anomalies, which are often missed by the human eye. This technology enhances quality control processes by providing consistent, real-time inspection, reducing human error, and increasing overall efficiency and reliability in manufacturing and production environments.
Coordinate Measurement Machine: Measure Product size, dimension, length, depth, diameter etc. using AI.
Computer vision AI and advanced image processing algorithms are revolutionizing Coordinate Measurement Machines (CMM) by enabling precise dimension measurements of products, components, and parts without operator intervention. This technology automates manual processes in metrology labs within the manufacturing industry, accurately measuring distances between various components of a product, such as electronics and cold plates. It also facilitates the measurement of height, width, and depth using sophisticated AI and image processing techniques, significantly enhancing efficiency and accuracy.
Object and Product Counting
Sophisticated Vision AI algorithms can accurately count products, components and small objects such as vials, syringes, tablets in real-time using machine vision cameras. It eliminates the need for complex, large and expensive machines for counting in the production line, assembly line and warehouses.
Industrial Vision AI for Visual Inspection in Quality Control
Vision AI has emerged as a cornerstone technology for quality control. Equipped with high-resolution cameras and sophisticated AI algorithms, this technology enables detailed inspections of products at unparalleled speed and precision. Defects such as cracks, scratches, dents, wrong shapes, color issues and many more can easily be detected using Industrial Vision AI. This breakthrough significantly lowers defect rates, ensuring that only the highest quality products reach consumers.
Predictive Maintenance using Industrial Vision AI
Downtime is one of the key challenges in manufacturing. With the capacity to process and analyze vast amounts of video and sensor data in real-time, AI algorithms can now predict equipment failures and maintenance needs with unprecedented accuracy. This foresight not only reduces downtime but also prolongs equipment lifespan, resulting in significant cost reductions and heightened operational efficiency. Industries from manufacturing to energy are harnessing these predictive capabilities to proactively address potential issues, minimizing the risk of costly interruptions.
Autonomous Robotics: Advancing Manufacturing and Logistics
The evolution of autonomous robotics, powered by sophisticated AI, has significantly advanced in 2024. Robots now perform intricate tasks with greater independence, from complex assembly operations to autonomous warehouse navigation for inventory management. These AI-driven robots collaborate with human workers, augmenting human capabilities and undertaking repetitive or hazardous tasks, thereby enhancing safety and productivity. This symbiosis is paving the way for fully automated production lines and logistics operations, signaling a new era of industrial efficiency.
Label Detection, Reading, and Validation
A well trained AI system can identify, read, and validate product labels, ensuring accuracy in packaging and compliance with regulations. This application is crucial in sectors with stringent labeling requirements, such as pharmaceuticals and food processing.
Foreign object detection
Foreign object detection in production lines is a critical aspect of manufacturing, ensuring product safety and quality while minimizing the risk of costly recalls and reputational damage. With the integration of advanced Vision AI technology, manufacturers now possess the capability to identify and remove foreign objects from production lines with unparalleled precision and speed. This technology utilizes high-resolution cameras combined with sophisticated algorithms to scan products in real time, detecting anomalies that deviate from the norm, such as metal fragments, plastic pieces, or other unintended materials.
What do you need to know before you start an Industrial AI project
Clearly Define the problem that you to solve
AI can solve many problems within production and assembly lines. Industrial Vision AI can analyze videos, images and sometimes outperform the human eye. Look at your process and list down the processes where a camera can be installed and AI can analyze video streams to bring insights such as object detection, counting and incident detection.
In most of the project AI training is needed with your dataset
Industrial AI initiatives differ markedly from conventional AI projects, primarily due to the bespoke nature of their algorithms. These projects often require tailor-made training to address specific challenges using unique datasets, which are typically not available in public domains. Consider, for instance, a pharmaceutical company aiming to automate the counting of prefilled syringes. The specific images of the facility's syringes, essential for training an AI model, wouldn't be found online. Therefore, to achieve high accuracy in counting prefilled syringes, the company must develop an AI model trained exclusively on its proprietary image data. This custom training approach underscores the necessity for a tailored dataset to solve distinct problems within industrial applications.
You need to gather the image/video dataset
Before starting the project, you need to capture and gather the images,videos to train the AI model. For example in defect detection, you need to collect defective images of your products and prepare the dataset to train the AI model. The accuracy of your AI model will depend on the quality of images that you use to train your AI model.
Find out the strategic location where you can install cameras
Identify the location where you can install the camera. Please ensure the camera has the full view of the area where the object or incident will occur. You may need multiple cameras based on your use case and problem that you are solving. You also need to procure high resolution cameras for your project. Instead of having a large machine, cameras are always a better option. You can talk to Industrial AI experts to get more information on what camera you should use.
Decide where to process the video streams? On a cloud or on a On-prem server
Processing video streams for Industrial Vision AI applications can be done either locally at the manufacturing facility using an On-premises server or through a secured cloud service. Each of these options comes with its set of advantages and disadvantages.
Secured Cloud Processing:
Pros:
- Scalability: Cloud services can easily scale up or down based on demand, offering flexibility and efficiency.
- Low downtime: Secured cloud like Azure, AWS, GCP has low downtime. For sensitive process, you can use one of these cloud to run your industrial AI apps
- Maintenance: Cloud is easy to maintain. It can be maintained, monitor remotely. It also reduces the burden on internal IT teams.
Cons:
- Data Security Concerns: Transmitting sensitive information to the cloud can raise security concerns, though reputable providers offer robust security measures.
- Cost: To run a large AI application, you may need an expensive GPU server and per-month billing can pinch your pocket.
- Dependency on Internet Connectivity: Requires a stable and high speed internet connection, which can be a limiting factor in areas with poor connectivity.
- Latency: Depending on the cloud provider's location, there can be latency issues that affect real-time data processing needs.
On-premises Processing:
Pros:
- Data Security: Keeping the data processing on-site can enhance security, as sensitive information does not have to travel over the internet.
- Cost: If you have large AI models that need high capacity GPU servers, you can purchase them with a one-time initial cost. This will be cost effective in a log-run.
- Control: Businesses have full control over the hardware and software, allowing for customizations tailored to specific needs.
- Performance: Processing data locally can reduce latency, offering real-time analytics and faster response times for critical applications.
Cons:
- Scalability: Scaling up requires physical hardware additions, which can be slower and a burden on the internal IT team.
- Uptime: If anything goes wrong in the hardware level, it is difficult to make the system running within a short time.
- Complexity: Requires in-house expertise for setup, maintenance, and troubleshooting, which can add to operational complexities.
Get help from experts
Seeking expert guidance for industrial AI projects can significantly enhance the project's success rate. consultancy firms specializing in Industrial AI and offer specialized services in the industrial AI implementation can provide tailor-made solutions for your project. They can help in evaluating the feasibility, developing strategies, and implementing AI technologies effectively.
The capabilities of Industrial AI in 2024 are not merely evolutionary; they are revolutionary. By enhancing predictive maintenance, quality control, autonomous operations, sustainability, and customization, AI is setting new paradigms of efficiency and innovation. As industries continue to embrace and integrate these advanced AI technologies, the potential for growth and transformation seems boundless. The future of industrial operations is here, and it is powered by AI, heralding a new era of productivity and innovation that will shape the industrial landscape for years to come.
