Ever since the early 1900s when Ford Motor Company opened the very first assembly line, automation has drastically impacted the automobile industry. Not only has car manufacturing become quicker, easier, and cheaper, but it has also become safer for employees who are no longer required to work in hazardous conditions. The cars assembled by robots are also standardized and of higher quality, with less margin for error. This has made it great for factories, as automation allowed them to rapidly scale up like never before.
With technology becoming more advanced every year, it is safe to say that certain trends are emerging when it comes to impacting the automobile industry. So, let’s take a look at what could be in store for the future, and how that might overhaul the sector once again.
Increase in the use of Cobots
Although automated factories still have human employees and overseers, the field of robotics has advanced so much that it is now possible to have smaller robots safely collaborate alongside humans in certain tasks. These robots, dubbed cobots (short for collaborative robots), differ from their larger counterparts by being lightweight, rounded, and often taking the shape of an arm to help with moving and grappling items. Cobots are often equipped with sensitive sensors that allow them to ‘feel’, meaning if the robot is accidentally bumped or moved, it will immediately fall into a safety mode. This ensures that people nearby do not get injured, whereas traditional industrial robots would keep on continuing their tasks, potentially harming others around them.
Tasks that are mainly done by cobots are often repetitive, dangerous, or simple, while human employees handle more complex, creative tasks. Some of these tasks include:
Welding: Cobots can perform spot and arc welding autonomously, and with very few errors. This task suits cobots well as they have no risk of being burnt and will not tire despite doing repetitive tasks. As such, they can work rapidly and consistently, making for greater high-quality output. Painting and Coating: When it comes to dealing with larger objects like cars and other automobiles, cobots are great at speeding up the painting and coating process. As each car needs an even and consistent coat of paints, finishes, and polishes, using a robot makes the process more efficient, without compromising on quality. Assembly: Cobots are mainly used through the assembly process, whether it be lifting heavy cars, screwing in bolts, or fitting in tires. Furthermore, robotic arms will not tire, meaning there is less chance for errors to occur. Human employees can then focus on tasks they specialize in or are good at, such as designing a new car model. Quality Assurance: Cobots can be outfitted with cameras and sensors that allow the robot to ‘see’ its surroundings. These cameras can then autonomously spot whether a particular area has any defects or errors. This ensures all products are standardized and correctly produced, with customers less likely to return a faulty product, thus leading to less wastage.3D Printing Saves Time
Also known as additive manufacturing, 3D printing is becoming more common in the automotive field. While in the past it used to be relegated to hobbyists and niche projects, now heavier components and parts can be created using this method. The way 3D printing works is to create objects layer by layer from the ground up, which will impact how cars are assembled. For example, while welding is a necessary part of assembling an automobile, 3D printing can create parts quicker and cheaper than ever before, which will speed up the welding process and assembly line.
Companies such as Audi are using 3D printing to produce metal prototypes and spare parts, while Rolls Royce uses additive manufacturing to print plastic components. Other manufacturers such as Porsche and Hackrod have also followed suit.
Impact on Automotive Industry
When it comes to the different steps in the manufacturing process, there is no doubt that automation and artificial intelligence (AI) will impact results. Here are just a few of them detailed below:
Decrease in Equipment Failure
If an ordinary machine suddenly malfunctions in an automotive assembly line, the consequences can be chaotic. This would result in a bottled-up production line, with a product unable to be completed, therefore significantly impacting the factory’s bottom line. Whereas if AI was used, the robots could be programmed and taught how to recognize errors, diagnose problems as well as check if it is close to breaking down. This way, even if a robot fails unexpectedly, it is less likely to hold up the production line or harm nearby employees. This also means manufacturers can lower the cost of inspections and annual maintenance.
Fewer Quality Issues
Right now, most quality control is still being performed by human workers. However, this makes it slow, and errors can still remain undetected. Even normal automated machines can sometimes still be confused and make mistakes, especially if the lighting is not ideal or the product already has an irregular shape. However, AI can filter out these issues and focus only on detecting the defects. Moreover, AI such as machine learning can teach themselves and improve based on feedback received. According to statistics, AI machines are 90% more accurate in detecting defects when compared to humans. They can also be used to unearth the root cause of errors occurring, meaning that the mistakes are less likely to happen again.
Increase Productivity Among Employees
Advances in technology have allowed more machines to collaborate with humans safely. These robots can work with or alongside humans while reacting to changes in an environment. This means that factories no longer require robot-only areas, thus simplifying a factory’s design and build. Programming and instructing these robots are also simpler, so companies can save money and time when it comes to deploying them. Moreover, collaborative robots can assist with tasks that cannot be fully automated, thereby increasing a production line’s productivity.
Making Supply Chains Leaner
When it comes to making sure there is a close match between supply and demand, accurate forecasting is critical. As such, AI and machine learning can be used to produce accurate demand forecasts, by including real-time data. Their automated system is flexible enough to adapt and respond to any changes that might occur, especially when it comes to unforeseen events. Therefore, having an AI-based approach could end up reducing forecasting errors and overall unused inventories. This means manufacturers can easily save on costs by eliminating the purchasing, transport, and warehousing of unnecessary goods.
Ludovic Gauthier
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