Currently, there are four automation technologies in the automotive industry that are most used and with which companies in this sector are digitizing their production processes.
Automation technologies in the automotive industry have integrated options such as collaborative robots, robotic arms, the Internet of Things (IoT) and Artificial Intelligence (AI) into the manufacturing process. These robots are found producing car parts, such as chassis, powertrains and other components, except for a few simpler parts that can be made by human workers.
New automation technologies in the automotive industry combine advanced robotics with automation technologies and learning modules, performing jobs with high precision and increasing industrial productivity.
In the world of the vehicle industry, machine vision provides a significant number of tools that will help the factory, both the inspection of components and the correct assembly of its parts, during assembly on the automotive production line.
So, to know more about the four most widely used automation technologies in the automotive industry, let’s detail each of them: Machine Vision (VM)
The need to make cars safer, more reliable and more robust to justify price points is pushing automakers to adopt machine inspection. In this way, the Machine Vision (VM) system is one of the automation technologies in the automotive industry that satisfies this need, by providing an automatic internal inspection method of the machine.
The focus of the VM is to carry out automatic, image-based inspection and analysis for automatic inspection, process control and robot guidance. This technology is also known as “computer vision”, for handling a large source of numbers of high-quality technologies, software and hardware products, integrated systems and, of course, experience.
Of the most widely used automation technologies in the automotive industry, the VM functions as the viewing eye of the automotive production process by using imaging processes, including conventional imaging, hyperspectral imaging, infrared imaging, line scan images, 3D images of surfaces, and X-ray imaging.
Smart cameras, or smart sensors with frame capturers, are used with custom interfaces to record, or capture images, of the surface to be inspected. There are also digital cameras capable of direct connections to a computer, through different internet interfaces.
With the VM system, the cameras capture images of the surface of the car component to be inspected. For example, the body, or fins, of an engine. These images are then analyzed and processed by specialized analysis software, which mostly uses the principle of Finite Element Analysis in its work. A VM system helps automakers save money, justify price points, and emerge as strong competitors.
The second of the four most widely used automation technologies in the automotive industry are collaborative robots, which are generally called Cobots, and are machines that work independently, without the need for human presence in their workspace.
A Cobot uses machine learning to pause all its operations, when a human worker enters its space. So why are they called “collaborative” even though their functions are the opposite? Cobots really help human technicians handle a large portion of the work when an order requires multiple functions to be performed at once. For example: The Cobot will allow the worker to work on it, and after it shuts down, when the work of the latter is finished. Not all cobots are created equal; some are designed to stop, while others are not. According to ISO 10218, there are four types of Cobots base in the functions:Supervised safety stoppage.Manual guide.Speed monitoring.Separation and power and force limitation robots. Artificial Intelligence (AI) for driverless/autonomous cars
Artificial Intelligence (AI) is another of the most widely used automation technologies in the automotive industry. This technology works by creating and storing an internal map of the environment (street, locality or region) using intelligent sensors such as radar, sonar and/or laser. It then processes these inputs, plots the most plausible trajectory, and sends instructions to the vehicle’s actuators that control acceleration, braking, and steering.
With the programming of coded driving protocols, obstacle avoidance algorithms, predictive modeling, and smart object discrimination (i.e., knowing the difference between a bicycle and a motorcycle) the car will follow the rules of the road, and avoid obstacles. Cognitive computing in IoT connected cars
Cognitive computing (CC) is another of the most widely used automation technologies in the automotive industry. CC is a technology platform based on artificial intelligence and signal processing.
These platforms encompass and utilize machine learning, reasoning, human language processing, speech and object, human-computer interaction, dialogue, and narrative generation, among other intelligent traits. While cars connected to this automation technology, are vehicles that use the Internet to connect and communicate with each other to create safe, easy and non-intervention traffic.
To date, several automotive factories are combining CC and IoT to invent autonomous cars that communicate with each other, while recognizing and linking driving patterns with the emotional response of their human drivers, during all possible scenarios (such as applying brakes at the right time, with the prudence deserved, to avoid accidents).
Vehicles with these types of automation technologies would prove to be much more advanced than driverless cars, this, in case the technology is successfully approved and replicated.
Some successful exercises with the IoT platform have enabled automakers to develop a cloud-based service to connect remote OBDII devices and vehicles, manage vehicle diagnostics and driving behavior data, integrate data with enterprise systems, and develop new applications for connected and innovative vehicles.
The robotic world is becoming increasingly involved with human life to ensure safer and more accurate processes. With the application of some of these intelligent platforms and the automation of production, taking into consideration goals such as greater visibility of the automotive supply chain, we may not be approaching a, not too distant, new industrial revolution.
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