The era of digital technology has revolutionized the universe of engineering design, especially in product design. The new set of innovative digital models allowed organizations and engineers to replace traditional methods, tools, technologies, and systems with faster and more scalable virtual solutions. However, engineering organizations have invested money and resources to develop these solutions thereby enhancing efficiency and cost-effectiveness.
Considering adaptability and innovation, engineering teams are now able to develop competitive digital models that meet changing market scenarios and adapt to evolving technologies. In this way, the entire way of product design, though following a set of standardized processes, has changed dramatically.
The evolutionary architectures and hyper-digitization unlocked new potential zones for organizations in the engineering process and global markets. The rise of new level programming where a computer runs thousands of simulations and comes up with the best design as per business requirements. These related optimization approaches with integrated machine learning models can outperform engineering brains.
Product design using deep learning models isn’t optimal yet!
Although machine learning models and automation programs have exceptional artificial brains for product design, the uncompromised factor is the human touch. Also, there are significant limitations as today’s applications or products are data-rich and computationally intensive. As a result, today’s systems can only deliver little optimizations. But companies always tend to involve expert engineering teams to plan the product design and outcomes for their projects. Also, it’s a challenging exercise if the talent is scarce or there’s a lack of expertise.
As your competitive advantage is totally dependent on innovation, the speed at which the product works, adaptability to changing scenarios, and scalability, it’s a must-make decision to eliminate traditional approaches. This is where engineering teams must collaborate to plan their approach towards product design – in terms of optimizations and technologies to be used to achieve the desired outcomes.
An alternative route for faster product design roadmap
A few global unicorns are exploring an alternative that promises an increase in speed and effectiveness of automated product design along with optimizations. Such an alternative is also advantageous as the product addresses complex engineering problems. This new approach is dependent on AI/ML models to solve the most challenging computing problems and meet desired business goals. We name these models – Deep Learning Surrogates (DLS).
The primary advantage of having DLS technology is that it reduces computing complexity, increases speed, and runs the processes faster when compared to traditional approaches. This DLS technology translates into many significant advantages for organizations, reduces the engineering process time, cuts costs, and the intensity of the engineering effort required. And deep learning algorithms allow organizations to explore multiple parameters for product design while optimizing the overall performance.
Overview of DLS
A DLS process is a digital design optimization approach. The engineering team will define the product characteristics and various design options are projected that would reflect the desired outcomes of the product. Once training is complete, the deep learning model will function faster.
The preeminent benefit of deep learning workflow is speed. The product’s capability to process, simulate, and compute all types of data is unique to projects. Based on the business requirements, the simulations are performed in design space with advanced search algorithms derived from the AI ecosystem. Increasing speed means – engineering teams can tackle much larger projects and optimize on a parallel line across multiple domains.
However, the use of deep learning mechanisms in product development involves complexity when adapting for companies that go beyond technologies. All kinds of new resources are allocated across the organization and a new infrastructure must be installed to achieve the same.
Application of deep learning models in real world
Many organizations are making efforts to integrate/built-in new capabilities in their products and build a portfolio of enriched data-intensive applications with the best-in-class user experience, the quick transmission of data, simple and encrypted communication, and many more. All these design specifications and features are new digital design approaches. The engineering teams continuously strive for defining the constraints and figuring out the desired performance of the products.
With multiple design options and based on the strategic vision, multiple design options are created using deep learning workflows.
The deep learning workflows in product development gives options by studying the future market scenarios and with a little human brain involved, a neural network is linked between the systems that results in maximizing business potential and solving technological complexities.
Moreover, the use of deep learning mechanisms has come into practice in large organizations. Though they are in the experimental phases, the approach is turning out to be effective with quick turnovers. For developing a design model in a project, the time prolongs for weeks and sometimes months together. But the deep learning models cut these time and effort costs by 50% according to Mckinsey and company research.
Transforming the future with deep learning models
The application of deep learning in product development is in the experimental phase. Once organizations see their product design as a holistic success, leading enterprises will build standard engineering processes for multiple product categories. In addition to expanding its use cases in various domains, the research continues to integrate these models across various wings. The potential approach toward this implementation is to work on existing products with real-time data and see the level of design approaches that deep learning models are generating that are matching to your business goals.
In contrast, optimal optimizations in product design and development, improved performance, advanced algorithms to deal with complexities, involved lower costs, and increased competitive advantage are the primary outcomes with the use of deep learning models.
