Many industries are interested in technologies that facilitate automation, and the automotive industry isn’t an exception. It’s a dynamic industry with new inventions and technologies always cropping up.
Thus, manufacturing companies need to be flexible and constantly adapt to new changes to keep up with the trends. Automation is a growing concept. According to Fortune Business Insights publication, it’s expected to hit $326.14 billion in 2027.
The following are some of the automation technologies used in the automotive industry:
- Collaborative Robots
- Machine Vision
- Cognitive Computing
- Artificial Intelligence for Autonomous Cars
Here are the top 6 uses of automation in the automotive industry you need to know.
- Physically Automates Tasks and Operations
You can’t compare automation to human labor in a car factory. You can automate machines to conduct activities like painting, welding, fabrication, and painting. Unlike before, where the industry used traditional machinery for selling or funding, automated bots are preferred.
The automated robotic processes manage customer requests in different corporations.
The software robots are responsible for a variety of activities. Examples are: running diagnostics, issuing warnings, and arranging service appointments. In the future, robots will be tasked to sell vehicles at dealership spots.
- Data-Driven Automation
It’s essential to know the market data before making data-driven decisions in the automotive industry. The knowledge helps in working intelligently, reducing costs while ensuring the business grows.
The data technology is used to manipulate, capture, interpret, engage, and conduct operations in the automotive industry. You can use data to anticipate events as they unfold. It’s easier to make informed decisions when you know what’s to come.
Here are some reasons why data-driven automation is important.
- Improves efficiency – It’s more accurate than manual data entry.
- Gathers data insights – Access and centralize data about dealers, vehicles sales, business insights, and company partners.
- Implements dynamic stocking and pricing – It creates an AI engine that recommends vehicle pricing depending on trends.
- Offers a consistent experience – It doesn’t matter whether one uses IOS, Android tablets, the web, or a desktop. Users have a consistent experience when accessing data throughout the company’s eco-system
To make sure your data-driven automation works, you have to test it. What happens when you are not great at automation testing? You can always easily learn automation testing from the various guides found online.
Automation testing checks if the software is functioning correctly before releasing it for production. It makes your work easier and saves you time.
You can tailor the testing to meet your goals and needs. It helps you understand if you’ll get the results that you desire.
- Collaborating with Humans to Accomplish Tasks
Commonly known as cobots, collaborative robots work independently without human intervention. When a human enters its space, it pauses the operations it was conducting. This ensures that humans are safe during production.
They usually handle the challenging portions of a task. When laborers perform several functions at once, the cobots give them the space to handle the tasks and shut down after completing the work.
Cobots are divided according to their functions. Here are the examples:
- Safety Monitored Stop Robots
- Hand Guiding Robots
- Speed and Separation Monitoring Robots
- Power and Force Limiting Robots
Speed and costs are integral parts of the automotive industry. Robots can be used to do any risky and recurring work. They operate existing development operators and cells. They eliminate the need for infrastructure costs or extra structural costs.
- Machine Inspection
Cleaner and more robust vehicles are a must in automotive. There is a need to produce quality and durable machines. Companies have to inspect their cars to meet the intended expectations and match the set price. Machine vision technology helps them satisfy such needs.
Machine vision technology contains high-end technologies, hardware and software products, expertise, and integrating systems. The technology acts as a lens in the automobile development chain. It uses imaging techniques like;
- Hyperspectral imaging
- Line scan imaging
- 3D surface Imaging
- X-ray imaging.
- Infrared imaging
Coax press (a smart sensor containing frame) takes images and monitors all the surfaces you should inspect. An example is the engine’s body.
- Cognitive Computing in IoT Connected Cars.
Cognitive Computing are technology platforms that are based on signal processing and artificial intelligence. These platforms use different aspects like:
- Human language processing
- Machine learning
- Dialogue and narrative generation
- Human-computer interaction
Connected cars use the internet to communicate with one another. In the process, they build easy, safe, and non-intervening traffic.
Companies like BMW and IBM are in the process of combining Cognitive Computing and IoT in inventing autonomous cars that can communicate with each other.
They want the vehicles to recognize and link driving patterns to an emotional response to human drivers in different scenarios. An example is applying breaks before a collision to prevent accidents.
They are yet to test this technology. However, it would be a massive step for the automotive industry if it becomes a success.
- Use of Artificial Intelligence to Automate Cars
Image source: pixabay
Driverless or self-driving cars use different levels of artificial intelligence. An example is the Tesla model that has its own driverless car hardware called autopilot.
AI works by first creating and storing the internal map of its environment (locality, street, or region). It uses smart sensors like radar or sonar.
Artificial Intelligence then processes these inputs and plots an excellent route to take. It then sends the instructions to vehicle actuators controlling the steering, braking, and accelerations.
AI uses object discrimination, predictive modeling, obstacle avoidance algorithms, and coded discrimination concepts. These concepts assist the vehicle in following the traffic rules and navigating past different obstacles.
Organizations like Bosch and NVIDIA continue to develop and improve Machine learning to improve AI.
There are different ways automation is used in the automotive industry. Automation eases work and offers exhilarating experiences that didn’t exist before. It allows for flexibility while allowing companies to be competitive in the market.
With automation, there’s increased productivity and better quality of products. It’s excellent that the automotive industry is embracing the change that comes with automation. The growth is visible and better things are yet to come.