Industry 4.0 and Industrial Automation: A Practical Guide
Industry 4.0 is the fourth industrial revolution in the manufacturing industry, which includes automation of industrial processes through production technologies based on digital systems.
Industry 4.0 will transform industry by making it more efficient, sustainable, secure and innovative.
With the implementation of Industrial Automation, there are many benefits that can be achieved for manufacturers including improved product quality with reduced errors, increased throughput rates with reduced inventory levels and lower energy consumption levels due to optimized machine utilization rates.
In this article, we will provide a guide to three emerging trends in Industry 4.0: Computer Vision, Increased Sensor Fidelity, and Improved Robotics.
Computer Vision is the ability of industry machines to capture and analyze images or video, identify objects in those images, process that data with software algorithms, and then take appropriate action.
With industrial equipment such as cameras, artificial intelligence can see assembly lines and analyze the contents of such lines; at this point computer vision is primarily used for tracking parts along conveyors but increasingly is used to monitor quality control.
There are two types of approaches to Computer Vision in the industrial sector:
- Traditional image processing
- New Deep Learning techniques
Traditional image processing uses a series of filters and mathematical operations to detect features in an image. For use cases with a limited problem space, traditional image processing can be highly effective.
However, many organizations are evaluating Deep Learning as a replacement for traditional image processing.
Deep Learning techniques yield a more robust approach to quality control. These techniques are based on Convolutional Neural Networks (CNNs), which take training data as an input and teach an algorithm to classify the desired phenomenon.
“Artificial Intelligence”, “Machine Learning”, and “Deep Learning” are generally synonymous terms, with Deep Learning denoting a specific deep convolutional approach of Artificial Intelligence or Machine Learning.
Deep learning networks are very effective in recognizing objects and patterns in images. The use of deep learning networks can result in improved accuracy, faster processing times, and the ability to learn and identify new objects.
For industrial applications, Computer Vision powered by Deep Learning is often a superior alternative to traditional image processing.
Increased Sensor Fidelity
The advent of Industry 4.0 means that industrial systems are getting smarter through embedded sensors. These sensors can monitor real-time conditions like temperature, vibration, pressure etc., enabling them to make decisions autonomously (without human intervention).
This enables manufacturers to better manage supply chains by identifying problems before they happen while also improving product safety standards.
Every year, sensors are improving both in quality and easy-of-deployment. Cameras, for example, are continually improving in resolution allowing Computer Vision algorithms to spot smaller and smaller defects.
Sensors are also becoming multispectral, allowing for automated quality control in multiple electromagnetic domains. Multispectral sensors include:
These sensors have the advantage of ‘beyond-human’ vision, meaning that they can see defects or anomalies that a human simply can’t see.
Most importantly, the cost of multispectral cameras is dramatically decreasing, and over the next five years we expect to see these cameras reach a compelling price point for many industrial organizations.
As sensors become increasingly integrated into the manufacturing environment, there are become more and more opportunities to digitize processes to improve efficiency.
Perception is a fundamental building block to the next generation of Industrial Automation, and it would behoove companies to adopt sensor technology sooner rather than later so they can keep pace with AI technology.
As much as software plays an important role in industrial digitization and Industry 4.0, hardware is also critical. Improved robotic hardware such as pick-and-place robots allow engineers more flexibility when designing automation solutions.
Robotic arms, for example, have evolved from being used primarily in manufacturing organizations with low-volume, high complexity products and processes to more versatile manufacturing systems which are capable of performing multiple tasks on demand without downtime or other interruptions.
The increased precision of industrial robotics means that digital initiatives can now improve efficiency and affect more products within a facility.
By supercharging mechanical robots with AI and other leading-edge technologies, your factory can realize efficiencies in its production process, whether that’s improved quality control or enhanced pick-and-place movement.
It is also important to mention that computing power has improved substantially in the past several years. Graphical Processing Units (GPUs) capable of performing millions of computations per second can now be embedded directly into factory environments in a form factor that is incredibly compact.
GPUs enable AI perception and other computations which go back to the argument of driving efficiency and automation in a factory.
Going forward, autonomous robots capable of moving throughout a factory will become more prevalent.
Several large industrial companies are already developing these robots are part of a ‘lights-out’ factory digitization initiative, and as costs decrease, we expect small and mid-sized manufacturers to begin adopting these more advanced robots.
The three trends of Computer Vision, Increasing Sensor Fidelity, and Improved Robotic Hardware will help power the adoption of Industry 4.0.
At Simerse, we help companies achieve their Computer Vision aims and help them utilize advancements in Sensor Fidelity and Robotic Hardware.