Industrial Automation Magazine India

Taking robots out of their static works cells is becoming more the strategy

Published on : Monday 10-04-2023

Dick Slansky, Senior Analyst, PLM & Engineering Design Tools, ARC Advisory Group, Boston. 

How will advanced automation mitigate the challenge of retooling for new models?
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. 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. 
 
The difference in composition between battery powered EVs (BEVs) and ICE vehicles determines production and automation requirements. The two primary differences between BEV and ICE production are Powertrain and Power Electronics. The powertrain production (transfer lines and assembly) of an ICE vehicle is replaced by the BEV powertrain which is essentially an electric motor(s) and a battery pack. BEV powertrains have very few moving parts and do not need exhaust systems, alternators, fuel injectors, starters, and many other components. Because there are much fewer parts the assembly process for BEV powertrains will be simpler. 
 
However, the power electronics components of a BEV (DC/DC and AC/DC converters, electronic controllers, wiring harnesses, HVAC systems, battery management systems, etc.), are much more complex than an ICE vehicle and will more than make up for the simplicity of BEV powertrain assembly and installation. Driver assist and autonomous systems require much more electronics and sensors as well as the PCBs and circuitry. BEV manufacturing uses a new generation of wiring harness automation and wiring looms to automate this process as much as possible. Additionally, some of this automation is AI powered, allowing for much more adaptability and ‘learning’.
 
The common wisdom that BEVs are less labour intensive in the assembly stage than ICE vehicles is inaccurate. In fact, the labour requirements for assembling BEVs and ICE vehicles are comparable. Though BEVs require no assembly of fuel or exhaust systems, they do require manufacturing of high-voltage wiring converters and inverters, installing motor-charging units, and connecting battery cooling systems, and much more electrical and electronic circuitry. Additionally, parts of the BEV manufacturing process require greater attention to quality control adding more complexity to the process. Expect to see more advanced automation applied directly to the unique requirements of BEV manufacturing.
 
Irrespective of BEV manufacturing, assembly areas and production lines in automotive are using AI apps in several ways. These include a new generation of smart collaborative robots and human-machine interfaces. 
 
What are strategies for re-programming robots for automotive production?
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. 
 
How do companies plan to upgrade the skills of humans working alongside machines?
One of the more promising technology trends in robotics in recent years has been the emergence of collaborative robots or cobots. To fully understand the impact that cobots can have, it’s necessary to understand exactly what they are. Essentially, they are a specialised group of robots designed with a particular set of technologies that enable them to interact with human workers in a shared workplace. Using advanced sensor and machine vision technology along with AI, these machines were created to address the safety challenges posed by conventional industrial robots. A protective stop is activated when a cobot comes in contact with or is in a certain proximity of a human worker, ensuring that the motion of the cobot is halted and does no harm to the person. 
 
One of the basic concepts of cobot is for humans and robots to be interdependent and safely focus on what each of them does best. This makes cobots a good solution for acting as assistants to workers and trades people, performing tasks in a cooperative way that would be too difficult for either the worker or the robot. It can truly be a synergetic relationship. The basic difference between cobots and conventional robots is not so much about the technology used, but more about the application and the type of tasks they perform. Cobots are not meant to replace their human counterparts, but to collaborate directly with them to improve productivity and enhance the safety of the human worker.
 
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. (See Boston Dynamics “Spot” and “Atlas”)
 
How would automotive finishing lines plan to integrate vision systems, robotics, and AI? 
Vision systems and robotics have been integrated for decades. 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. 
 
Machine learning algorithms using pattern matching techniques can access a database that includes all the possible undesirable defect instances in the paint process and match those with any instances found in the real-time process. Vision systems are a component of this quality assurance process. The painting equipment can then be adjusted on-the-fly to adapt and correct the potential defect. This same methodology can be used to control the presence of dust and other contaminants in the paint booth. 
 
In the area of body-in-white assembly, these same systems can be used to ensure proper fit and fair and gap control of assembled body components. Rather than a vision system, metrology is enabled by laser measuring devices and sensors. ML algorithms are used to determine the optimal fit condition and use adaptive control to optimise robotic kinematics for the proper fit. Again, this can be an on-the-fly adaptive process optimisation. 
 
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 one factor alone makes it technically superior to standard re-programming or human monitoring. AI can be used in concert with advanced analytics at countless points in the assembly and production process. AI and ML add significant value in the analysis of structured data such as sensor data and time series, and in the evaluation of unstructured data such images and videos. 
 
What kind of automation electronics are suitable for hot, dusty, and bumpy conditions? 
First, the reference here is to electronic componentry that is on-board the vehicle and not to automation electronics used in the production process. The automotive industry has considerable experience in producing ruggedised electronic componentry for military spec applications where the vehicle is designed for harsh off the road conditions. Most recently, the army has introduced the JLTV, the electrified successor to the Humvee. The new AbramsX is the first electrified main battle tank. The car makers will be applying their engineering expertise in ruggedised electronics to their EVs across all models, especially off-road recreational vehicles.
 
EVs in general have much more electronic circuitry than ICE vehicles even given the computerised driver assist and semi-autonomous systems found in today’s ICE vehicles. Power electronics alone significantly increases the amount of wiring, connectors, and electronic control units (ECUs) found in the typical EV. Powerful battery packs require additional electronics to control battery environment conditions like heat and especially cold weather conditions that are detrimental to battery performance. Battery pack circuitry is required to be ruggedised simply by the amount of energy involved (capacity) and the risk of fire and explosion. 
  
(The views expressed in interviews are personal, not necessarily of the organisations represented)

Dick Slansky's responsibilities at ARC include directing the research and consulting in the areas of PLM (CAD/CAM/CAE), engineering design tools for both discrete and process industries, Industrial IoT, Advanced Analytics for Production Systems, Digital Twin, Virtual Simulation for Product and Production. Dick brings over 30 years of direct experience in the areas of manufacturing engineering, engineering design tools (CAD/CAM/CAE), N/C programming, controls systems integration, automated assembly systems, embedded systems, software development, and technical project management. Dick provides technical consulting services for discrete manufacturing end users in the aerospace, automotive and other industrial verticals. Additionally, he focuses on engineering design tools for process, energy, and infrastructure.