Detroit Engineered Products (DEP) is strengthening its global innovation footprint by expanding R&D centers in India and advancing its AI-driven MeshWorks platform. With a strong emphasis on sustainability, cross-industry applications, and nurturing next-generation engineering talent, DEP is enabling faster, smarter, and greener product development. Positioned as a key enabler across diverse sectors—from automotive and aerospace to renewable energy and healthcare—the company is charting new frontiers in CAE innovation. Basant Sharma, Vice President, Detroit Engineered Products (DEP), in an exclusive interaction with Abhineet Kumar of Elets Technomedia, shares insights on DEP’s expansion strategy, technology focus, and the future of engineering.

Edited excerpts: 

DEP is expanding R&D facilities and satellite offices in Indian locations such as Mysuru, Coimbatore, and Tirupati to enhance innovation and client servicing. Could you share what specific regional strengths (e.g., talent pools, academic linkages, or industry presence) guided these location choices?

DEP is expanding its R&D centers and satellite offices in Mysuru, Coimbatore, and Tirupati/Sri City to boost innovation and client service. Each location was chosen for its unique strengths that contribute to a robust R&D network

Mysuru has a rich pool of software and CAE talent, supported by top engineering institutes and Karnataka’s ESDM cluster, making it perfect for model-based development, simulation, and embedded cybersecurity. Coimbatore is known for its precision manufacturing environment, a strong network of auto-component SMEs, and a solid mechanical engineering academia. This makes it ideal for lightweight structures, mechanical design, and mechatronics integration. Tirupati/Sri City benefits from a thriving electronics manufacturing cluster, proximity to IIT Tirupati and IIIT Sri City, and excellent logistics through Chennai’s ports. This location is optimal for electrification systems, HIL/SIL testing, and sensor integration.

Together, these locations create a cost-effective, high-capability triangle: digital engineering in Mysuru, mechanical prototyping in Coimbatore, and electronics validation in Tirupati. This setup allows DEP to innovate rapidly and deliver across various domains.

DEP’s proprietary MeshWorks platform integrates AI/ML-driven CAE tools for smarter design optimisation and simulation. How do you envision scaling these AI-enhanced capabilities to address emerging sectors like renewable energy, aerospace, or electric mobility?

DEP is already extending its AI/ML-enhanced MeshWorks platform to sectors like renewable energy, aerospace, and electric mobility by leveraging its “physics-grade AI” solutions. This accelerates design optimisation through hybrid physics–machine learning models and multi-objective optimisation, addressing industry-specific needs efficiently.

In Renewables, it can optimise wind blades, solar structures, and battery enclosures for energy-efficient solutions. In Power Electronics, it can optimise thermal performance, electromagnetic performance, and vibration performance. In Aerospace, it can speed up/optimise aero-structural design, electric propulsion, and noise reduction. In electric mobility, it can improve battery pack design, lightweighting, and thermal management.

This expansion relies on modular plug-ins (Auto Trainer, Auto Predict, Auto Parametrised, Auto Optimisation), deep CAD/CAE/PLM integration, hybrid and transfer learning strategies, and compliance-ready validation workflows, supported by partnerships with solver vendors, universities, and hardware providers. The approach promises to cut design cycles by up to 90%, reduce solver runs, and deliver certified, manufacturable designs faster while enabling sector-specific AI capabilities that adapt to each industry’s data, safety, and IP constraints.

You recently emphasised the role of sustainability in DEP’s CAE approach, focusing on energy-efficient design and material optimisation. How are these priorities translating into client projects—and how do you measure their long-term impact?

DEP’s sustainability-driven CAE approach is already shaping client projects by embedding energy-efficiency and material optimisation targets directly into the early design and simulation phases, so these become hard engineering constraints rather than afterthoughts. For example, in mobility programs, MeshWorks-driven topology/shape/size optimisation has delivered double-digit weight reductions without compromising safety; in renewable energy projects, it has enabled longer-life wind blades with less material waste; and in industrial equipment, it has reduced thermal loads and power consumption through AI-assisted design iteration.

We measure the long-term impact using a combination of engineering KPIs and business outcomes. Key KPIs include weight reduction percentage, lifecycle CO₂ savings, recyclability index, and energy efficiency gains. Business outcomes focus on reduced total cost of ownership and faster green standard certification. Additionally, DEP employs digital twin monitoring and predictive analytics to track energy performance, durability, and maintenance savings over time, using this data to refine future sustainability outcomes.

In your recent interview, you spoke about the CAE industry’s rapid evolution involving AI/ML automation and sustainability. From your perspective as VP, what are the top three shifts (e.g., skills, tools, business models) that engineering leaders must anticipate in the next five years?

From my perspective as VP, the next five years will bring three defining shifts that engineering leaders must prepare for:

A. Skills shift — Engineers must transition from traditional CAE tool operation to mastering AI/ML-assisted workflows. This requires combining domain expertise with data science literacy, understanding uncertainty quantification, and managing AI-proposed solutions where engineers make the final decisions.

B. Tool shift — The CAE stack will evolve towards unified platforms integrating physics simulation, AI surrogates, and digital twins. Tools need to be solver-agnostic, cloud/hybrid deployable, and equipped with modular AI plug-ins to adapt to emerging sectors.

C. Business model shift — Clients will increasingly expect “design-as-a-service” or “optimisation-as-a-service,” paying for outcomes like weight reduction or energy efficiency rather than engineering hours. This will necessitate new pricing models, stronger IP/data governance, and enhanced collaboration between OEMs, suppliers, and software providers.

DEP works across varied sectors—from automotive to biomed and heavy engineering. Could you highlight an example where CAE innovations initially intended for one industry—say, automotive—successfully crossed over and created value in another domain like healthcare or marine?

DEP’s mesh morphing and parametric optimisation, first used in automotive, was adapted for biomedical implants to quickly tailor designs to patient-specific scans without re-meshing and optimising for performance using DEP’s MeshWorks parametrisation and optimisation techniques. This cut customisation time from weeks to days and improved performance predictions—demonstrating how an automotive innovation can deliver high-impact results in healthcare and even marine propeller optimisation.

Also Read: KELTRON – A Glorious Legacy in India’s Tech Evolution

Given DEP’s global expansion and technology-driven focus, what strategies do you prioritise for nurturing CAE talent (e.g., skill development, diversity, cross-functional teams)? How do you ensure your engineering teams stay equipped for future challenges? (Insight based on DEP’s evolving scale and innovation culture.)

At DEP, we prioritise nurturing CAE talent through skill development, diversity, and cross-functional teamwork. We emphasise continuous learning by offering internal AI/ML and advanced simulation bootcamps, partnering with universities, and providing access to the latest tools. This ensures our engineers stay current with new technologies like digital twins, physics-informed ML, and multi-physics optimisation.

Diversity and cross-functional teams are vital. By bringing together individuals with backgrounds in mechanical, electrical, data science, and other fields, we foster creative problem-solving and accelerate innovation across different sectors. To prepare our teams for future challenges, we focus on rotation programs, mentorship, and exposure to projects in multiple industries. This helps our engineers gain both deep and broad knowledge in CAE applications.

Moreover, our culture encourages experimentation, knowledge sharing, and the early adoption of AI-driven workflows. This keeps our teams agile and ready to tackle evolving engineering challenges globally.

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