Automation in Automotive Industry
Published on : Monday 03-04-2023
Experts debate how the automotive industry is taking its symbiotic relationship with automation to the next level.
Right from the time Henry Ford introduced the moving assembly line to keep pace with the growing demand for the Model T over 100 years ago, the automobile industry has been at the forefront of adopting technology. Early mechanisation led to automation by the 1950s. In fact, according to Encyclopaedia Britannica, the term automation was coined by D S Harder in the automobile industry sometime during 1946 to describe the increased use of automatic devices and controls in the mechanised production lines. Use of sensors and robots followed in the 1960s and more and more functions were taken over by electronics till digitalisation assumed near total control in the present times. But the quest for increased productivity and improved efficiency continues unabated. Today, the pressure to bring out new models is high. One of the challenges is retooling. So how would advanced automation mitigate this challenge?
“This is a tough challenge in the current environment where product design is driving the need for new models being launched more often. This makes the organisation much more market focused but at same time puts pressure on the manufacturing cost as most of the time retooling is needed to accommodate the new design. The retooling decisions today are not based on the number of cycles done but more from the point of view of whether the machine will support the changes in product design,” says Amit Saluja, Senior Director and Center Head, NASSCOM CoE, Gandhinagar. “This challenge has led to extreme focus on flexible manufacturing in the plants where enterprises can accommodate the product changes with minimum retooling time. Technology is playing a huge role in helping accommodate design changes in the manufacturing process,” he adds.
“As car makers ramp up their factories and production lines to produce the next generation of EVs they are initially making the decision to either convert existing brownfield plants to EV production, or to invest in entirely new Greenfield factories dedicated to EV production. Producing EVs in factories formerly used to make internal combustion engine (ICE) vehicles requires the reworking and retooling of entire assembly processes,” says Dick Slansky, Senior Analyst, PLM & Engineering Design Tools, ARC Advisory Group, Boston. “While some automotive companies will mix EV and ICE production using flexible manufacturing cells (at least for a while), the trend appears to be to build new factories for EV production, especially high-volume output. Also, this enables the auto manufacturers to incorporate the latest technologies for smart manufacturing (e.g., additive manufacturing, cyber-physical systems, advanced robotics, AI) from the ground up,” he elaborates
According to Rashmi Ranjan Mohapatra, CEO – Business Head, Parry Enterprises (Murugappa Group), the industry has taken a paradigm shift after the advent of Industry 4.0. Customisation is the key for the future. This is where individual customers' demand is coming to the fore. “Automation is the only way to balance the manufacturing cost. This helps to mitigate the cost of retooling. Also, it will adapt to the growing demands of customisation. This will also be a key differentiator in the business,” he emphasises.
Taking a more nuanced view, Darshana Thakkar, MSME Transformation Specialist and Founder, Transformation – The Strategy Hub believes that with manufacturing layout alterations in the plant, changes can be made without huge capital expenditure. With the cellular manufacturing process, robots can be redeployed in a ‘lift and shift’ process that gives life to an asset beyond its original purpose. She cites the examples of new technologies such as mega casting that enable combining multiple parts into one large die-cast piece, which can be quickly produced in a single process. By using additive technology with a 3D printer, the dies for such complicated mega parts can be made promptly, with precision. Then there are advances in robotics, with vision- and force-control-enabled robots interacting with AGVs. “With many such new developments, the moving line approach has become faster, with no requirement to stop or pause for specific critical assembly. With this approach requirement, floor-mounted shock pins are eliminated. Now it can ripple throughout the factory and make assembly lines less model specific, making it possible to use the same line or production cell for various models. That, in turn, leads to a smaller overall factory footprint, as well as significantly reduced capital expenditure,” she explains.
“Seamless integration of technologies like CAD, PLM, interactive 3D publications, MES, robots and IoT has enabled the automotive industry to launch new products regularly without much trouble in a relatively short period of time. It allows organisations to plan the product launch time frame, track the progress and take corrective actions to adhere to the schedule so that the new product is actually launched on the target date,” says Nilesh J Suryavanshi, Manufacturing, Engineering, Industry 5.0 (Cloud), Tech Mahindra. According to him, any CAD model changes for a specific model version are auto-updated at the shop floor in e-SOP and visual video instructions which are fully automated and eliminates any lag in information sharing between the R&D and production departments.
Since robotics is a highly favoured technology for many applications in automotives, what are the strategies for re-programming these robots quickly for new models of automobiles?
“Robots have become the lifeline of the automotive industry considering highly repetitive tasks needed to be done with a high amount of precision. The level of productivity and efficiency we have today in automotive manufacturing couldn’t have been possible without robots. Industrial robots are used in every operation in plants like welding, painting, assembly, inspection and even in material movement,” says Amit Saluja. “Reprogramming of robots has become a regular activity in the plants, which is extremely time-consuming. As per the analysis, the total cost of robot ownership is mainly the programming cost. Thankfully, industrial IoT and software are making this process easier.” For him, the most critical element in the whole process is having good connectivity and integration between the robots and systems to enable seamless data transfer and intelligent decisions, which is nothing but Industry 4.0.
For Dick Slansky, re-programming of robots for adaptable or flexible tasks is not necessarily an issue in today’s automotive production lines. Rather, taking robots out of their static work cells is becoming more the strategy for both adaptive and collaborative robotic tasking. “The workforce of the future in many industries, in addition to automotive, is machines and humans working together,” he opines.
Rashmi Ranjan Mohapatra concurs. “Robots are now going to be more flexible in the days to come. Reprogramming is much easier than in the past. The turnaround time is much less. The robot manipulators will be used heavily to simulate and use the program/s for multitasking,” he asserts.
“There are two ways of robot programming – either by guiding (online) or by offline programming. Most industrial robots are programmed by guiding a robot from point to point through the phases of an operation, with each point stored in the robotic control system. The other method is offline programming. Knowledge of specific programming languages, like C, C++, Python, Java, etc., is required for offline reprogramming,” says Darshana Thakkar. According to her, the simulation tool MATLAB is efficient and helpful for rapid reprogramming of robots. MATLAB is highly useful in designing the entire robotic system. It is widely used in the robotics industry as it is deeply rooted in the foundation and development of robots. The other one is RoboDK API, which is a compelling and cost-effective simulation and programming software for industrial robots and cobots. It supports more than 700 robots and more than 50 robot manufacturers, including robots like ABB, Fanuc, Yaskawa, Universal Robots, Motoman, Staubli, and more.
Nilesh J Suryavanshi says automotive industries are adopting industrial robots that comply with ISO 8373: 2012, which have the ability to reprogram multipurpose manipulators programmable in multiple axes, quickly changing the performed processes and desired motion route to launch new models in short duration. “Companies are building teams to achieve smooth changeover of robotics according to new models,” he adds.
Robots do perform many tasks efficiently, relieving humans from the drudgery of simple repetitive tasks. In future humans will be asked to perform the more complicated tasks using new technologies. How do companies plan to manage to upgrade the skills of humans alongside the new machines?
Amit Saluja believes automation and robots are not taking the jobs away, but shifting them from regular mundane physical work to more intelligent tasks. This is a big change for which manufacturing enterprises need to prepare themselves by upskilling and reskilling their workforce. “With so much focus on data acquisition in plants, we will need better analysis and decision making skills. Basic level of understanding of technology will also be critical in future; workers who are operating machines will need to be trained on operating computers and using software. While this appears daunting, in actual practice, it is not so difficult. It is just about changing mindset; workers do not have to learn software development, it’s more to gain understanding of the application of technology to the manufacturing challenges,” he explains. Drawing attention to the fact how NASSCOM has built an ecosystem where manufacturers can learn about the innovative solutions and work with solution providers to co-create customised solutions.
According to Dick Slansky, the emergence of collaborative robots or cobots is among the more promising technology trends in robotics in recent years and to fully understand the impact that cobots can have, it is necessary to understand exactly what they are. Cobots were created using advanced sensor and machine vision technology along with AI, to address the safety challenges posed by conventional industrial robots. “Robotics technology and capability has clearly entered the era of the intelligent machine. This new generation of robots is empowered with AI and machine learning allowing them to move beyond pre-programmed kinematics and motion to adaptive machines that can literally ‘think on their feet’. Not only are these robots smarter, but they will be mobile and able to function as human assistants, aiding their human worker counterparts in tasks across a broad spectrum of work in industry, warehousing, medical care, customer service, surveillance, and relieving humans of many mundane and time-consuming tasks,” he says, citing the examples of Boston Dynamics’ ‘Spot’ and ‘Atlas’ robots.
Rashmi Ranjan Mohapatra also believes cobots will bring in a different dimension of flexibility and adaptability. “We will have more usage of cobots in the next few years. It is like an assistant to the human operator,” he predicts. Equally important in his view is the need for organisations to have structured programs and keep upgrading the skills of the human capital in sync with the investment in capital equipment. “The Skill India movement has come as a boon for this. The institutes today also provide upskilling of the human capital and make them stay relevant in the tech life cycle,” he adds.
“To run highly automated assembly, plants need skilled engineers, programmers and maintenance teams to keep them running while constantly adapting to meet new technologies and feature consumer demands,” says Darshana Thakkar. She also believes companies need to continuously invest in developing the skills of their employees. Instead of giving monotonous tasks to workers, companies must employ skilled people to meet the rapidly increasing technology demand in the plant. The crucial challenges a company might face are technological obsolescence and the emergence of new features and technology. “Also, producers of high technology products and developers of the software need to devise specialised learning courses for the workforce of the user companies. Through joint efforts of academia, producers of technology, and the automotive manufacturers, we should jointly develop the ecosystem to maintain fast-paced learning of all the stakeholders,” she suggests.
“After Covid-19, automotive companies have reworked their strategies. Many of them are now planning for ‘Lights-Out manufacturing’ for some percentage of their production,” says Nilesh J Suryavanshi. “These companies are also focusing on reskilling and up-skilling their workforce to implement industrial automation, robotics, computer vision, technology design and programming, IIoT, AR/VR, etc., in their plants. Most of the repetitive tasks are taken over by robots,” he points out.
One of the key activities in quality assurance is visual inspection. How would automotive finishing lines plan to integrate robots, vision systems and specially AI into this task?
“Vision systems are very common in the automotive industry today and an integral part of the production system. Considering the high speed and precision needed to do the quality inspection, it is humanly impossible to do the manual visual inspection. Most common use cases are the inspection of component parts and sub-assemblies which could include engine parts, chassis, body parts like bumpers, beams and rails, seating systems and even electronic assemblies,” says Amit Saluja. “A lot of advancement has happened in the image processing algorithms over the last few years, thanks to AI and machine learning which has enabled building of high speed inspection systems with much more level of accuracy. Systems can detect the defects and do the measurements even up to microns level. Another good application of vision systems is sorting and detecting missing parts,” he adds.
“Vision systems and robotics have been integrated for decades,” agrees Dick Slansky. “The new component added to the mix that is enabling disruptive change is AI. Automotive companies like BMW have devised a methodology using real-time readings from sensors and vision systems in their paint shop that can be evaluated against a database of potentially damaging factors to allow for immediate adaptive and proactive measures to ensure that paint quality is not compromised and to support longer term process improvement,” he explains. According to him, AI is capable of identifying cause and effect relationships out of large complex and unstructured data points. The ability to analyse and apply collected data from past processes and apply this to a current situation is the key performance feature of AI.
“This goes without saying. ML (Machine Learning) and AI (Artificial Intelligence) will perform this task with better consistency for the future, says Rashmi Ranjan Mohapatra. “AI to distinguish OK or NG way better than conventional threshold judgement; decrease miss judgement of OK or NG resulting in higher yield; reduce workers and cost for inspection; increase throughput; reduce human dependent error; and eliminate human dependent quality check level,” he adds.
According to Nilesh J Suryavanshi, there is a growing adoption of computer vision, 5G and drones to transform to digital quality assurance; and drone based digital inventory tracking of finished goods in large sized plants. Computer vision, along with robotics, is implemented for paint defect analysis, raw material inspection, etc. “Automotive companies are adopting computer vision AI/ML at each stage including raw material inspection till the final product. Even Tier 1 and 2 suppliers are adopting it based on requests from automotive OEMs,” he points out.
“Vision systems, when coupled with advanced artificial intelligence and machine learning systems, their ability to spot minute defects can now match or even surpass experienced human operators. It is very much helpful in improving the quality and speed of welding operations,” says Darshana Thakkar. To conclude, here are a few examples:
i. Multi-Spectrum Lighting technology detects the slightest variations in colour and shading to detect contamination.
ii. Imaging and detecting parts with slightly different colors and shading, such as O-rings, gaskets, and parts that may come from multiple suppliers.
iii. Inspect whether a moisture-proof agent is applied correctly on ECUs
iv. To inspect the position of the fuses with the correct specifications, and
v. For bearing side inspection, if any crack or damage exists.
Note: The responses of various experts featured in this story are their personal views and not necessarily of the companies or organisations they represent. The full interviews are hosted online at https://www.iedcommunications.com/interviews)
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