Applications of robotics in the automotive industry
Credit: Bert van Dijk/Getty images.
ABB has unveiled the world’s first robot-painted car
In May 2022, ABB collaborated with two artists, India-based Advait Kolarkar and Dubai-based digital design collective Illusorr, to launch the world’s first robot-painted car. ABB’s PixelPaint technology created Advait’s monochromatic design and Illusorr’s tri-colour geometrical patterns on the car without human intervention. ABB’s IRB 5500 paint robots are equipped with 1,000 nozzles in the printer head and claim to complete highly complex artworks in less than 30 minutes.
PixelPaint technology allows the paint to be quickly applied in a single application. It enhances manufacturing sustainability by eliminating the need for masking materials and extra ventilation, which lowers emissions while saving water and energy. PixelPaint technology, coordinated with ABB’s RobotStudio software, ensures that the paint head tracks very closely to the vehicle body. Consequently, the paint is fully applied to the car with no airborne misting. A single application can quickly apply different paint colours to the car.
This can help automobile manufacturers reduce production time and cut costs by up to 60%. The PixelPaint technology can also capture intricate and elaborate detail that would be impossible to achieve by hand.
ABB has also got its robotics claws in the EV market. In April 2022, launched two robots - IRB 5710 and 5720, which can provide speed and flexibility for material handling in EV battery production. The robot's improved speed and precision, as well as its sturdy architecture, can improve productivity and performance with higher uptime. EV designs are typically complex and components, including batteries and semiconductors, are often bulky or fragile.
To eliminate production errors, these criteria necessitate technologies that can provide optimum precision and consistency. ABB's new robots are designed to help EV manufacturers by increasing material handling speed and adaptability. The robots are suited for applications such as plastic moulding, metal casting, cleaning, and spraying. In 2023, the company intends to introduce new process applications like cutting, welding, and dispensing.
Ford’s robotics portfolio is continually growing
Ford has adopted robotics in several different ways. In 2019 Ford adopted half a dozen Universal Robots devices to touch up its vehicles' finish. Ford also added collaborative robots to its assembly line in Cologne, Germany. The co-bots work alongside Ford engineers to put the finishing touches on Fiesta vehicles, from smoothing textural inconsistencies to vacuuming.
Ford also developed and deployed a self-driving factory robot in 2019 to deliver parts quickly and efficiently to workers on the shop floor. The self-driving robot, dubbed Survival, is the first of its kind to be deployed at the company’s facility in Europe. The factory robot is entirely designed and built in-house by Ford engineers.
As of 2022, Ford's assembly line in Cologne has adopted a cobot, Robbie, for its employees with reduced mobility. The company decided it would like to keep Robbie following a successful 18-month trial. In 2022, Ford also adopted a KUKA robot, Javier, to operate its 3D printers.
Javier can automatically manufacture automotive parts using 3D printing, reducing human interference while increasing throughput. According to Ford, Javier is accurate in its motions, only takes short breaks to recharge its batteries, and completes tasks on time. Javier can also use the printer data to enhance its performance over time, resulting in more accurate outcomes and a lower margin of error. It can also lower the price of custom-printed items.
BMW adjusts its factory logistics with Nvidia
In BMW’s manufacturing units, more than 30 million parts are being shifted and fitted daily from around 4,500 suppliers. About 230,000 parts are organised to produce 10,000 built-to-order cars every day. BMW allows customers to customise the car at their convenience, which is the biggest contributing factor to the huge logistics requirements.
Manufacturing customised cars, on multiple models, with higher volumes, at one factory line requires advanced computing solutions. To better manage this, BMW partnered with Nvidia to harness AI and robotics technologies to create customisable and rapid manufacturing. BMW has adopted five AI-enabled robots with functions, including object detection and motion planning, to help assemble customised products on the assembly line.
Nvidia has embedded neural network capabilities within these factory robots to aid them in perceiving their environment, detecting objects, and navigating autonomously. The company claims that these robots are trained on both real and synthetic data using Nvidia GPUs (graphics processing units) to render ray-traced machine parts in various lighting and obstruction conditions to supplement real data. Various spare parts are delivered to the plant in containers of different sizes, which are then transported to the assembly line.
All five robots intend to support factory logistics in production operations. It includes Stationery SplitBots that pull out a whole batch of plastic boxes and transport them to the warehouse, ensuring appropriate placing for automated storage. It can differentiate up to 450 containers using AI. Mobile PlaceBots unload tugger trains and place the loaded cargo onto shelves.
The bots use image recognition systems to classify the load carriers and the ideal gripping point using input from sensors and cameras. PickBots have robotic arms and are deployed to sort small parts from supply racks using the same technology as SplitBots to calculate the right grip point. SortBots are fitted with robotic arms to separate empty boxes for re-circulation from the supplier area.
Autonomous smart transport robots (STRs) have a unique neural network that recognises obstacles and promptly recommends alternate routes. Using the self-learning feature, STRs adapt to the surrounding situations accordingly.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
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