Audi
Canvas Category OEM : Automotive
The AUDI AG stands for sporty vehicles, high build quality and progressive design – for “Vorsprung durch Technik.” The Audi Group is among the world’s leading producers of premium cars. To play an instrumental role in shaping the transformation as we head into a new age of mobility the Company is implementing its strategy step by step.
Assembly Line
Audi Electric Motor Engine PRODUCTION - Car MANUFACTURING Process
Audi drives the future of manufacturing
The production of the Audi e-tron GT illustrates what’s possible in this new paradigm. It went from design to production in record time — without a physical prototype.
Virtual reality prototypes are just the beginning. Thousands of assets in the Böllinger Höfe plant operate under the aegis of Audi’s Edge Cloud 4 Production (EC4P), with critical networking and security support from Cisco. And by connecting “brownfield” legacy hardware — that is, existing devices like cameras, robots, Internet of Things (IoT) sensors, and field busses — the transformation did not demand a complete factory overhaul. It did, however, benefit from the efforts of Cisco’s Customer Experience team to bring the vision to life.
“Audi and Cisco shared the vision of getting rid of the compute hardware from the shop floor and virtualizing it,” Loeser concluded. “That’s one of the big reasons why we are actually deploying this today.
Smart Manufacturing at Audi
Some 5,300 spot welds are required to join the parts that make up the body of an Audi A6. Until recently, production staff used ultrasound to manually monitor the quality of spot welds based on random sampling. Now, however, engineers are testing a smarter way of determining weld quality. They are using AI software to detect quality anomalies automatically in real time. The robots collect data on current flow and voltage on every weld. An AI algorithm continuously checks that those values fall within predetermined standards. Engineers monitor the weld data on a dashboard. If a fault is detected, they can then perform manual checks.
Audi Production Factory Tour 2022
Factory Robots! See inside Tesla, Amazon and Audi's operations (supercut)
Industry 4.0 and the Automotive Industry
“It takes about 30 hours to manufacture a vehicle. During that time, each car generates massive amounts of data,” points out Robert Engelhorn, director of the Munich plant. “With the help of artificial intelligence and smart data analytics, we can use this data to manage and analyze our production intelligently. AI is helping us to streamline our manufacturing even further and ensure premium quality for every customer. It also saves our employees from having to do monotonous, repetitive tasks.”
One part of the plant that is already seeing benefits from AI is the press shop, which turns more than 30,000 sheet metal blanks a day into body parts for vehicles. Each blank is given a laser code at the start of production so the body part can be clearly identified throughout the manufacturing process. This code is picked up by BMW’s iQ Press system, which records material and process parameters, such as the thickness of the metal and oil layer, and the temperature and speed of the presses. These parameters are related to the quality of the parts produced.
John Deere and Audi Apply Intel’s AI Technology
Identifying defects in welds is a common quality control process in manufacturing. To make these inspections more accurate, John Deere is applying computer vision, coupled with Intel’s AI technology, to automatically spot common defects in the automated welding process used in its manufacturing facilities.
At Audi, automated welding applications range from spot welding to riveting. The widespread automation in Audi factories is part of the company’s goal of creating Industrie 4.0-level smart factories. A key aspect of this goal involves Audi’s recognition that creating customized hardware and software to handle individual use cases is not preferrable. Instead, the company focuses on developing scalable and flexible platforms that allow them to more broadly apply advanced digital capabilities such as data analytics, machine learning, and edge computing.