TRIBUNE – The challenge of decarbonizing vehicles is in many ways a colossal challenge for manufacturers. A subject on which looks Ruchir Budhwar, vice-president Europe of Infosys, the consulting company in IT.
At the COP26 summit, nations pledged to drastically reduce greenhouse gas emissions in order to mitigate climate change. With this in mind, major automakers are stepping up their e-mobility programs to meet decarbonization mandates by 2050. With global demand for electric cars growing, automakers need to embrace digital to revise the manufacturing ecosystem.
Generative design: the key to product optimization
The computer-aided design, engineering and manufacturing (CAx) models of the 1980s contributed to the excitement of product designers and brought about a profound change in automotive design and development. Generative design is transforming CAD software, including through artificial intelligence (AI), machine learning (ML), and virtual simulation. It replaces linear design and engineering processes with parallel cycles of design, evaluation, validation and optimization. This approach dramatically accelerates R&D, product engineering services, certification and time to market for electric vehicles.
Electric vehicles require advanced design and engineering frameworks to optimize traditional manufacturing and take advantage of emerging practices. Generative design algorithms extract textual and visual data from input files and apply machine intelligence to align core systems with EV performance and quality requirements. The solutions take into account structural constraints, technical parameters, functional specifications and aesthetic considerations, with the main objective of minimizing vehicle weight.
In 2018, General Motors pioneered generative design by partnering with Autodesk. This technology generates hundreds of organic and performance combinations based on user specifications, such as durability, material and construction type. For example, an AI-generated design solution for a seat bracket reduced weight by 40% and increased strength by 20%.
A digital twin to analyze scenarios
Generative algorithms explore a multitude of potential solutions for design and engineering goals, whether it’s frame weight, load capacity, or end-of-life value. Digital tools allow product teams to optimize and test design variants in real-world conditions. Concretely, a digital twin simulates the entire product life cycle as well as the manufacturing environment; allowing AI-generated design options to be tested without the need for prototyping and testing.
On top of that, generative AI allows automakers to explore alternative manufacturing techniques. 3D printing, for example, can replace injection molding or machining to produce parts. Additionally, generative design and advanced manufacturing technologies enable mass customization.
Robotic automation for repeatability
Traditional machining systems, such as CNC machine tools, used by automotive manufacturers stand ready for an upgrade. Robotic automation powered by AI and computer vision enables manufacturers to establish a build-to-order ecosystem with lean production setups. Robotic control systems seamlessly integrate design, engineering, and production processes to drive manufacturing efficiency.
Advanced automation minimizes downtime and improves efficiency at electric vehicle production sites. EVs can be assembled faster and cheaper. Lean manufacturing systems ultimately streamline inventory investments, reduce waste, and optimize the use of production resources. Hyundai Motors has taken full advantage of electrification and automation to enter the Japanese automotive market.
Industrial robots can be easily programmed to perform a range of tasks – welding, deburring, spray painting, material handling, packaging and waste disposal. Programming robotic automation systems based on real-time operating conditions lends itself to improving critical functions such as quality control. Robots combine machine intelligence and computer vision to assess materials on the assembly line as well as finished products against specifications. Inspection and testing performed by robots proves to be more accurate than statistical quality control systems, regardless of production volumes.
Blockchain for product history
Automakers phasing out internal combustion engines need to minimize the environmental footprint of electric vehicle batteries. In this respect, the minerals and precious metals contained in used batteries can be recovered, reused and recycled. This requires manufacturers to build circularity into their business model and provide transparency for closed-loop battery supply chain management. Blockchain technology acts like a great digital textbook that enables immutable traceability and validation throughout the supply chain – from raw materials to the second life of a battery, including its lifespan. through recovery and reuse. This traceability encourages sustainable practices in the value chain of electric vehicles. It encourages stakeholders to accelerate ethical sourcing practices and minimize the carbon cost of operations.
European automotive companies can set the benchmark in the electric vehicle revolution by adopting an ecosystem approach and building digital capabilities to create resilient value chains.
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How can European automakers take the lead in the electric revolution?
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