The automotive industry is famous for its use of robots in manufacturing cars. The industry introduced robots way back in the 1960s. Since then, there has been a steady rise in the adoption of different robotics and automation technologies, enabling automakers to be more efficient and productive.
The cumulative effect of having robots at different parts of the manufacturing process becomes evident when you measure the time it takes a company to make a car. Toyota, for instance, can make a fully functioning car in about 17-18 hours, thanks to the use of robots in their manufacturing processes such as stamping, welding, painting, assembling, and more.
Let’s understand the key applications of robots that enable high-speed automotive manufacturing.
Cars are made up of several different body parts put together. Welds are used to ensure proper bonding between these body parts to provide structural integrity.
Instead of performing these welds manually, robotic welding speeds up the process drastically, while also ensuring the precision and quality of each of the welds. In addition, these robots are beneficial to cope with production demand, since they can operate 24/7 without any fatigue.
With imaging and proximity sensors, these robots can articulate their arms to make the weld at the right spots, with the right angle and pressure. Today's robots can handle varied types of welding such as arc, TIG, laser, MIG, ultrasonic, plasma, and spot welding.
Machine vision is a relatively new technology in robotics that has witnessed significant investments from the automotive industry. Machine vision is the technology that helps robots to understand their surroundings better.
As a result, the technology will allow factory robots to become more efficient at identifying parts and making intelligent decisions without needing human intervention. Robots equipped with machine vision are also valuable for the quality control process to spot defects in the product.
Machine vision is made possible using both imaging sensors and AI. The robot can also be trained using machine learning to better distinguish between specialized parts.
A typical Toyota car has around 30,000 parts. However, when you look across the board, every manufacturer has to put together more or less the same number of parts to roll out a vehicle. While human labor is still involved in assembling these parts, robots are increasingly reducing their burden.
Automotive assembly requires putting together various parts of a car. This involves electronics, machinery like engines, and other parts such as lights, fenders, and more. Robotic assembly uses robots to put together these parts. Today, most automakers employ robots in their assembly line.
An assembly process requires putting together the same parts over and over again. For humans, repeating the same motion for a more extended period results in muscle strain and fatigue. However, robots are free from such ailments. They can easily lift parts that are upwards of 1,000 pounds and continue with the same motion without the risk of injury or fatigue.
Automotive painting involves a tremendous amount of precision and care. The paint coat thickness must not be too thin to reveal the primer, or too thick to cause paint runs.
Robotic painting involves using a robot to apply paint on the vehicle’s surface. The robotic arm is programmed to evenly coat the car surface with a precise amount of color with each pass, ensuring a high-quality surface finish of the vehicle exterior.
Automotive manufacturing plants often span multiple acres, making logistics a challenge. If production targets are to be met, each assembly station must get access to the parts quickly. Robots are an ideal replacement instead of occupying humans for this repetitive job of getting raw materials from the inventory and bringing them to the assembly line.
AMRs or Autonomous Mobile Robots are the popular robots increasingly used for logistics. These robots consist of sensors like LiDAR to help them safely navigate their surroundings, and can be programmed to avoid obstacles to find the safest route to their destination.
It is also easy to keep track of each robot while delivering materials to the assembly stations, providing better control over the production schedule and ensuring the safety of humans working within the factory premises.
While robots are widely used in the automotive manufacturing industry, the advancements in sensor technologies and the accessibility of AI and machine learning will lead to new possibilities and use cases. New, advanced AGVs will likely help manufacturers in meeting the requirements of producing electric and self-driving cars. With intelligent decision-making capabilities, the robots of tomorrow will be capable of enhancing the quality control processes and enabling automakers to improve production times and profitability.