Embracing a data-driven approach to physics-based engineering
Electrification and digitalization are omnipresent in all aspects of modern life –from how we drink coffee to how our food is produced and to the complex behind-the-scenes processes of how we manufacture products.
Two broad, strong factors are driving the move to electrification and digitalization.
On the one hand, sustainability promotes innovation towards decarbonization and reduced environmental impacts, facilitating the move away from fossil fuels and lower emissions. This has been particularly significant in the transportation sector with all manner of electric vehicles, as well as VTOL drones. On the other, technology enables and accelerates electrification with improved connectivity via more reliable networks and cloud infrastructure, more powerful edge devices, growth in data centers, and a proliferation of low-cost portable electronic devices. Consumers expect seamless connectivity, at all times, whether with cell phones, smart watches, or bio sensors. Designers and engineers cannot ignore this growing need.
Electrification and digitalization have contributed to accelerating the pace of change in engineering and manufacturing. This acceleration is a double-edged sword that both helps to make great things possible by augmenting engineering and improving operations but also creates challenges, such as increased firefighting and recalls, due to shorter cycle times. With accelerated design and engineering cycles, the need for better solutions for quality assurance and manufacturing defect detection has also increased. Added to this is the unprecedented complexity of merging electrical and mechanical systems with software in the loop and a layer of data-driven AI in the loop. At every step, teams must manage more risk and ensure greater collaboration with PLM solutions. They must also leverage ML-Ops to truly benefit from the changes to the traditional sequential engineering, manufacturing, operations, and end-of-life product cycles.
Traditionally, engineering departments were separately responsible for selecting and adopting their tools of choice, independent of what other departments were using. This silo approach persists but is increasingly seen as an obstacle to essential data sharing across functions, as information moves from engineering to manufacturing to operations, and back. Low-code/no-code platforms are a quick, agile approach to freeing teams from their silos. The data can remain in the system where it is authored, while providing read-only access to other teams. Data access is essential and should be the early focus. Consolidating tools would also bring benefits but the early digitalization transformation focus of any product development organization should be to provide data access to all cross-functional teams.
Digitalization does not solely affect product and after-sales usage. It also has a profound effect on the engineering and manufacturing teams behind the products. To pave new avenues forward,
engineers must extend their in-depth physics-based understanding by becoming data-driven. Often, this brings a fundamental shift from a mostly deterministic physics-based engineering process to an increasingly probabilistic way of influencing the design.
Maya HTT has both the physics-based understanding and the industrial AI expertise to support engineers and manufacturers in becoming industry leaders.
Ready to see what electrification and digitalization can do for your engineering and manufacturing? Contact a Maya HTT expert today.