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AUTOMOTIVE INDUSTRY Automation applications in the automotive industry

Sep 14, 2021

Rohan A Manikpurkar, Engineer – Controls Engineering, SUEZ Water Technologies & Solutions - The automotive industry is adding more sophisticated robots, looking to 3D Printing and incorporating other advanced technologies. The business, however, is developing on the automation front. Despite the fact that automation is already very well recognised in the automotive industry, OEMs, tier one, two and three suppliers can still profit from continuing enhancements in this field. Many automotive companies across the world are presently focusing on automation as a weapon for competition on a global market. This article by SUEZ Water Technologies & Solutions (the company provides the solutions by different water technologies with automated plants) focuses on applications of automation in different areas of the automotive industry.

Automotive automation technology is constantly advancing. Most repetitive, monotonous and dangerous tasks have been simplified through automation. With advancements in robotic autonomy, Artificial Intelligence (AI) and Machine Learning (ML), some industrial robots now use vision-like sensor technology to help them perform intricate tasks and adapt to a changing environment. Robotic automation in automotive manufacturing will play an integral role in the shift from combustion engines to electric vehicles since robots can be programmed to meet higher demands and perform new roles while meeting production criteria. This will especially make newer vehicles and technology more affordable for the general public sooner. In fact, the auto industry alone accounts for nearly half of all industrial robotics sales in North America.

Collaborative robots (cobots), robotic arms and Internet of Things (IoT), coupled with AI, are already producing a large part of the automobile chassis, power trains and other components in some companies, saving efforts that can be made by human workers. Now pervading the automotive industry, robots are handling even the most complex manufacturing tasks and completing them several times faster than human workers. Advanced robotics, combined with automation technologies and learning modules, are performing jobs with more precision than ever and increasing industrial productivity. It is barely possible to tabulate an exhaustive list of all the intriguing marvels erupting from the most brilliant, industrious minds in the industry.

The fourth industrial revolution or Industry 4.0 is going to be the next big transformation in the manufacturing industry. Industry 4.0 will be a seamless integration between manufacturing and computerised control systems. It will involve cyber-physical systems that involve sensors throughout the system, from start to finish. All this implementation has not only increased output and reduced downtime but has also significantly increased profits for auto manufacturers. Here are the five most advanced automation technologies used in the automotive industry:

  • Machine vision

  • Collaborative robots

  • Lighting technology

  • Artificial Intelligence for driverless/autonomous cars

  • Cognitive Computing in IoT connected cars

Machine vision

The need for safer, more reliable and robust automobiles to justify price points is pushing automakers to adopt machine inspection. And Machine Vision (MV) helps them fulfil this need by providing an automated internal machine inspection method.

The automotive industry was one of the earliest industries to have adopted MV to carry out its imaging-based automatic inspection and analysis for automatic inspection, process control and robot guidance. This technology works as the eye of the automotive production process using imaging processes, including conventional imaging, hyperspectral imaging, infrared imaging, line scan imaging, 3D imaging of surfaces and x-ray imaging.

Smart camera or smart sensors with frame grabbers are used along with interfaces, such as Camera Link or CoaXPress (or custom interface) to record or capture images of the surface to be inspected. Digital cameras capable of direct connections to a computer via FireWire, USB or Gigabit Ethernet interfaces are also used by several companies. These cameras capture images of the surface of the automobile component to be inspected (say, the body or fins of an engine). And these images are then analysed and processed by specialised analysis software, which mostly use the principle of Finite Element Analysis in their working. MV helps automakers save money, justify price points and emerge as strong competitors. National Instruments, Cognex, Datalogic, Optotune and ViDi Systems are some of the top most companies whose machine vision systems are preferred by large carmakers.

Collaborative robots

A cobot uses Machine Learning to pause all its operations when a human worker enters its space. Cobots actually help human technicians by handling a large part of the job. When a certain job requires multiple functions to be done at once, the cobot will allow the labourer to work on it and later shut down once the latter’s job is done. However, not all cobots are made equally. Some are designed to stop while others are not. As per ISO 10218, there are four types of cobots based on functionalities – safety monitored stop, hand guiding, speed & separation monitoring and power & force limiting robots.

Universal Robots, Rethink Robotics, KUKA, ABB Yumi, F&P Robotics and Fanuc are some of the large companies that design, produce and supply cobots. KUKA and Universal Robots are currently being supplied to automotive companies, such as Tesla, to build cars, car-building robots and also assembly lines. Using cobots in such settings can put carmakers light years ahead in the race for speed and productivity in manufacturing.

Lighting technology

Advancements in automotive lighting technology have ramped up in recent years. Many new models of cars are adding adaptive lighting technology that will auto-adjust to lighting conditions, without blinding other drivers on the road. This new tech has reduced the incidence of night-time accidents by lighting more areas of the road ahead and to the sides. 5-chip LED lights also boast ‘around the corner’ lighting while laser headlight technology can let one see up to 600 metres ahead of oneself on the road, doubling the distance of regular LED headlamps. 5-chip adaptive LED tech illuminates objects on the edge of the road as well as approaching vehicles. The key players in the automotive lighting market are Continental AG, Valeo S A, Ichikoh, Robert Bosch GmbH, Koito Manufacturing and Stanley Electric.

Artificial Intelligence for driverless/autonomous cars

Artificial Intelligence system is defined as, “any system that perceives its environment and takes actions that maximise its chance of success at some goal.” And this is true for the on-research driverless or autonomous or self-driving cars that are using various levels of AI. Circling back to Elon Musk, Tesla has developed its own driverless car hardware called Autopilot that is currently being used on all Tesla models. And ironically, Musk, as per reports, wants Level 5 automation in all Tesla models.

Artificial Intelligence in cars works by first creating and storing an internal map of the surroundings (street, locality or region) using smart 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 which control acceleration, braking and steering. Coded driving protocols, obstacle avoidance algorithms, predictive modelling and smart object discrimination (i.e., knowing the difference between a bicycle and a motorcycle) help the car follow traffic rules and navigate past obstacles. AI software in the car is connected to all the sensors and collects input from Google Street View and video cameras inside the car. The AI simulates human perceptual and decision-making processes using deep learning and controls actions in driver control systems, such as steering and brakes.

Major players, such as NVIDIA and Bosch are playing a major role in developing and improving deep learning or Machine Learning to improve AI.

Cognitive Computing in IoT connected cars

Some companies, such as IBM (Watson AI) and BMW are combining Cognitive Computing (CC) and IoT to invent autonomous cars that communicate with each other while recognising & linking driving patterns to the emotional response of their human drivers during all possible scenarios (such as to applying brakes a moment before collision to avoid accidents).

These would prove to be way more advanced than driverless cars if the technology is successfully tested and replicated. An example of IoT platform is Thing Worx, on which automakers can develop a cloud-based service for connecting to remote OBDII devices and vehicles, manage the vehicle diagnostic & driving behaviour data, integrate the data with enterprise systems and develop new innovative connected vehicle applications.

Ushering in a new renaissance

The main challenge of vehicle manufacturing industries is to make the balance between order-winning criterion of cost, time and availability of product, without compromising the quality.

Here, we have discussed about the application of advanced automation in different areas of vehicle manufacturing industries. These top five automation technologies used in the automotive industry among others are ushering in a new renaissance with robotics and Artificial Intelligence at its heart.

Image Gallery

  • Autonomous or self-driving cars that are using various levels of AI

  • Rohan A Manikpurkar

    Engineer – Controls Engineering

    SUEZ Water Technologies & Solutions

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