Engineers Need PLM—Not Just Simulation—to Replace Prototypes

Simulation is touted as the way to reduce physical prototypes, but it is not enough.

Let’s be clear: the need for physical prototypes will not disappear when engineering new products. Prototypes can, however, be time-consuming to build and expensive to maintain. On top of that, they do not always allow for end-to-end product validation. Virtual models and digital simulations are now the norm when it comes to innovation, especially when designing and engineering complex products.

Virtual and physical worlds are coming together through multiple applications, from gaming to digital manufacturing, product prototyping and innovation. PLM and simulation are the key to reducing physical prototypes. (Image courtesy of Bigstock.)

Virtual and physical worlds are coming together through multiple applications, from gaming to digital manufacturing, product prototyping and innovation. PLM and simulation are the key to reducing physical prototypes. (Image courtesy of Bigstock.)

Every OEM and engineering organization thrives on optimizing product design and performance, developing future product development platforms to scale their product portfolio. When running through successive innovation cycles, data management practices play a key part in driving virtual and physical product engineering and validation. Hundreds to thousands of digital simulations contribute to problem solving and holistic product optimization.

For example, as cited in the January 2023 edition of the Porsche Engineering magazine, Humberto de Campos do Carmo, Senior Manager Vehicle Concepts and Package at Porsche Engineering, highlighted the essential role of computer-aided engineering (CAE). “We keep on refining development further and further until we get a digital study of the vehicle as a whole,” he said. Simply put, product integration begins with concept development and virtual prototyping, throughout the product development lifecycle.

In this post, I elaborate on the role of the product lifecycle management (PLM) discipline to drive innovation and reduce the reliance of physical prototypes.

What are Prototypes?

Vicki Sauter, professor of Information Systems at the University of Missouri – St Louis, has defined prototyping as “the process of building a model of a system. In terms of an information system, prototypes are employed to help system designers build an information system that [is] intuitive and easy to manipulate for end users. Prototyping is an iterative process that is part of the analysis phase of the systems development life cycle.”

Prototyping is about solving problems, experimenting and decision-making (i.e., testing new ideas, refining and narrowing down options across products, variants and platforms). There are multiple types of prototypes based on purpose and product maturity:

  • Concept prototypes help test new ideas, proving feasibility and functional application; these include visual prototypes to communicate to several stakeholders such as potential customers or future investors.
  • Production intent prototypes help drive both feasibility and viability, de-risking solution elements or supporting feasibility verification of a working model.
  • Pre-production prototypes include all relevant features, processes and other characteristics, validating quality conformity standards and compliance.
  • Production prototypes help to test manufacturing or assembly processes and final product readiness prior to market launch and mass-production.

Physical vs Digital Prototypes

Physical prototyping is necessary to test, verify and validate product performance and quality certification. Virtual prototypes (a.k.a. Digital Twins) help represent product behaviors and their visual mock-ups. They can include 1D, 3D and mathematical models, as well as the integration of virtual and physical elements across the product lifecycle.

Virtual prototypes include simulations to test product and process designs across functions: from mechanical to electrical engineering, software development and hardware-software integration. Virtual twins provide valuable insights into product quality, stress, kinematics, fatigue, clash, interference, thermal, safety, weight, cost or other sustainability factors. Most complex simulations such as stress or 3D thermal analysis require significant data as input, and in turn generate a large amount of output.

Across products and industries, digital prototyping typically takes place prior to or even in lieu of physical prototyping. This involves tracking various artefacts and data objects which characterize the product across its lifecycle, or within a specific phase, such as simulating and optimizing the manufacturing process. Simulations help with the verification and validation of physical characteristics, materials and product requirements, leading to design, quality and/or cost improvements.

Physical prototypes also generate experimental data to correlate with virtual simulation output from digital models. They can also drive new data pattern identification and potentially lead to new virtual model definition.

Product Data Management

The ability to generate and consume simulation input/output is essential to inform the decision-making process in product innovation and production planning. This involves the ability to track product changes, from requirements to materials, physical properties, design components, supplier deliverables and more, across product development and deployment cycles.

The ability to manage qualitative and quantitative feedback from prototype experimentation is important. Throughput can be accelerated and made more accurate through more advanced learning models, such as AI algorithms and ML technologies. Driving such feedback loops from one iteration to another is part of the PLM conundrum: how do engineers funnel changes across multiple perspectives while maintaining a consistent integrated product? PLM is about performing change impact analyses and managing implications.

Due to potential rising complexity, prototypes can be deceiving over time if they remain static or are not continuously integrated into the decision-making process. PLM practices and platforms help drive embedded change management, enabling the combination and evolution of multiple models through a data-led integrated ecosystem.

VR and AR opportunities

Beyond AI and ML models, prototype digitalization includes the use of new technologies to drive operational and innovation-related decisions:

  • 3D printing initially started with rapid prototyping applications, though it has also matured as a small production enablement option, especially for complex component manufacturing.
  • Virtual reality (VR) is directly leveraging digital twins and simulation models to facilitate prototype validation. It is also opening the door to new business applications.
  • Augmented reality (AR) is powered by combining data from the virtual and the physical worlds, therefore driving new types of real-time models and associated decisions.

PLM is not about eliminating prototypes; it is about making them digital, driving towards faster, better and cheaper experimentation, product development and operations. But keep in mind, legal compliance and quality validation will always require some level of physical prototyping, though this will be a more common factor in certain industries and markets.