Revansidha Chabukswar, Product design and development lead at AGC – Interviewing the Role of AI in Automotive Product Development: Transforming Industry Challenges into Innovative Solutions

In the rapidly evolving automotive industry, the integration of artificial intelligence (AI) is transforming how products are designed and developed. We had the privilege of speaking with Revansidha Chabukswar, the Product Design and Development Lead at AGC, to gain insights into the role of AI in this dynamic field. With a background in Mechanical Engineering and over 17 years of experience in product engineering for top automakers like Mercedes-Benz, Aston Martin, and Honda, Revansidha brings a wealth of knowledge to the table. In this interview, he shares his journey, the inspiration behind his specialization, and how AI is revolutionizing automotive product development. From AI-powered design tools to advanced manufacturing processes, Revansidha discusses the significant impacts AI has had on his projects and the challenges faced when integrating these technologies. Join us as we explore how AI is shaping the future of automotive innovation.

Can you share your journey and how you became the product design and development lead at AGC?

I majored in Mechanical Engineering, drawn to the field by my early fascination with and love for machines. During my undergraduate studies, I gained a strong foundation in core engineering courses such as Mechanical Element Analysis, Machine Design, Manufacturing Tools, Computer-Aided Design and Manufacturing, Automobile Engineering and Systems Design, Strength of Materials, and Theory of Machines. I also took specialized courses in Advanced Production Systems, Mechatronics, Cryogenics, Computational Fluid Dynamics, and Operations Research.

I have worked in the automotive industry for the past 17 years, specializing in product engineering for body-in-white, exterior, and glass components at several leading global automakers, including Mercedes-Benz, Aston Martin, Mahindra & Mahindra, Honda R&D Americas, Toyota Motor Engineering and Manufacturing North America, AGC Automotive Americas, and AGC Glass North America. I joined AGC as a Product engineer, where I was responsible for product design, development and management. As I gained experience over the years, I took on increasing responsibilities, and I am now the Product Design and Development Lead at AGC, leading the vehicle product design and development lifecycle.

My expertise involves designing and developing automotive glass products at AGC, in collaboration with cross-functional teams. I drive ongoing enhancements to products and processes, and leverage emerging technologies like generative design and artificial intelligence to improve product performance, quality, and manufacturing.

What inspired you to specialize in automotive product design and development?

As a young engineering graduate, I was drawn to the automotive industry due to its dynamic and technologically advanced nature. I was fascinated by the interdisciplinary nature of automotive product development, which combines mechanical, electrical, and software engineering, along with design, manufacturing, and supply chain considerations. Designing and developing automotive products, especially those that directly impact vehicle performance, safety, and comfort, such as glass and Body In white components was particularly appealing to me. The opportunity to work with cross-functional teams, cutting-edge technologies, and innovative materials and production processes further fueled my interest in this field.

Over the years, I’ve been inspired by the rapid pace of innovation in the automotive industry, driven by changing customer preferences, environmental regulations, and advancements in materials, manufacturing, and digital technologies like AI, generative design, and simulation. Applying these emerging technologies to enhance the design, development, and production of automotive components has been a rewarding challenge for me.

How has the role of AI evolved in the automotive product development industry during your career?

During the early stages of my career in the automotive industry, the use of AI was still in its nascent phase. At that time, the primary applications of AI were focused on automating routine tasks such as CAD modeling, simulations, and basic decision-making support systems. However, over the past decade, the role of AI has evolved dramatically, with a growing emphasis on enhancing and transforming the entire product development lifecycle. One of the key domains where AI has made a substantial impact is in the realm of automotive product development.

Research indicates that the integration of generative design and AI-based technologies within the automotive industry has led to improved product characteristics, accelerated development timelines, and optimized manufacturing workflows. Specifically, AI has enabled more accurate and efficient perception of user requirements, intelligent ideation and conceptualization, and data-driven decision-making throughout the product design and engineering stages. For instance, AI-powered simulations can now model complex physical phenomena, material behavior, and manufacturing processes with greater precision, enabling more accurate predictions of product performance and faster development iterations. Furthermore, the rapid advancements in sensor technologies and the growing adoption of autonomous driving features have further driven the integration of AI across various automotive subsystems.

Can you describe a specific project at AGC where AI significantly impacted the design and development process?

At AGC, we developed a new automotive windshield assembly process that incorporated an AI-powered vision system to automate the inspection of the bonding system. This enhancement improved the quality and efficiency of the manufacturing process.

Traditionally, the inspection of the bonding system during windshield assembly was a manual, time-consuming, and error-prone task. To address this, we implemented an AI-based vision system that employed deep learning algorithms to automatically detect the presence and quality of the bonding system. The AI-powered vision system was trained on a comprehensive dataset of images representing various bonding system conditions, including proper application, insufficient application, and improper application.

The integration of this AI-powered vision system into the production line yielded several beneficial outcomes:

• This AI-powered vision system substantially enhanced the accuracy and reliability of the inspection process, thereby mitigating the risks associated with quality problems and expensive product recalls.
• The integration of the AI-powered vision system streamlined the manufacturing workflow by automating a previously manual task, thereby enhancing productivity and reducing labor expenditures.
• The real-time data generated by the AI-powered system facilitated data-driven insights into the manufacturing workflow, thereby enabling continuous enhancements and optimization of the windshield assembly process.
• The adaptability of the AI-based system enabled seamless adjustments to accommodate changes in windshield designs or bonding system specifications, thereby ensuring the sustained effectiveness of the quality control process.
• The implementation of this AI-driven vision system demonstrated AGC’s dedication to adopting innovative technologies to improve product quality, manufacturing efficiency, and overall competitiveness within the automotive industry.

This project exemplified the transformative potential of AI-powered technologies within the automotive product design and development domain. It has served as a catalyst for the further integration of AI-based solutions across diverse facets of the company’s operations.

What are the biggest challenges you face when integrating AI into automotive product design?

A major challenge in incorporating AI into automotive product design and development is the inherent complexity and variability of the underlying data. Automotive products are exposed to a wide array of environmental conditions, operating scenarios, and user interactions, generating highly diverse and unstructured data. Effectively capturing, consolidating, and curating this data to train robust AI models poses a significant hurdle. Another critical challenge is the requirement to seamlessly integrate AI-powered systems within the established product development workflows and outdated information technology infrastructure.

• Data Management and Quality: The effective implementation of AI systems necessitates the procurement and curation of substantial volumes of high-quality, representative data. Amassing, refining, and preserving such data, with a particular emphasis on ensuring its cleanliness, accuracy, and alignment with real-world scenarios, poses a significant challenge.
• Safety and Reliability: Safeguarding the safety and reliability of AI systems is paramount in automotive applications. This necessitates rigorous testing and validation procedures to ascertain the accurate performance of AI under the full spectrum of driving scenarios. Lacking these assurances, the integration of AI-powered systems into safety-critical automotive components continues to be a significant challenge.
• Real-Time Processing: Automotive AI systems, such as those used in autonomous driving, need to process a vast amount of data in real-time and make instantaneous decisions to navigate safely. Achieving this level of responsiveness requires the development of highly efficient algorithms that can rapidly analyze sensor data, incorporate contextual information, and execute control commands with minimal latency. Additionally, the hardware powering these AI systems must be capable of parallel processing and high-speed computation to keep up with the dynamic nature of the driving environment. This necessitates the use of specialized hardware, such as graphics processing units or dedicated AI accelerators, which can provide the necessary computational horsepower to support the real-time processing and decision-making required for autonomous driving and other safety-critical automotive applications.
• Integration with Legacy Systems: Integrating new AI capabilities with older, legacy automotive systems can be a complex and time-consuming challenge. Many existing automotive systems were designed and built using outdated technologies, which can create barriers to incorporating advanced AI-powered features and functionalities. Overcoming these integration hurdles often requires extensive software and hardware modifications, as well as thorough testing and validation to ensure the seamless and reliable operation of the AI systems within the existing automotive infrastructure. This integration process can be further complicated by the need to maintain compatibility with legacy components, adhere to industry standards, and ensure safety and regulatory compliance. Navigating these complexities requires specialized expertise and a deep understanding of both legacy automotive technologies and emerging AI-driven solutions.
• Regulatory Compliance: Compliance with the extensive regulatory framework governing the automotive industry poses a significant challenge in integrating AI systems. Ensuring these AI-powered technologies adhere to all relevant safety, privacy, and security regulations across diverse geographic regions and jurisdictions is a critical requirement for their successful adoption.
• Cybersecurity: Automotive AI systems represent potential cybersecurity vulnerabilities that must be addressed. Rigorous security measures are essential to safeguard these systems against hacking attempts, thereby mitigating the risk of malicious interventions that could jeopardize passenger safety.
• Cost and Complexity: The implementation of AI-powered systems entails significant financial investments and technical complexity. This encompasses the procurement of advanced hardware, the development of sophisticated software, and the engagement of highly specialized personnel with the requisite domain expertise.
• Ethical and Privacy Concerns: The incorporation of AI within automotive design evokes complex ethical considerations, particularly surrounding decision-making processes in autonomous vehicles. Additionally, the extensive data collection by AI systems raises significant concerns regarding user privacy and the security of this sensitive information.
• Consumer Trust and Acceptance: Cultivating consumer trust in AI-powered automotive systems is essential. A significant portion of the population remains skeptical regarding the safety and reliability of AI technologies, particularly in the context of fully autonomous vehicles.
• Continuous Learning and Adaptation: Maintaining the capacity for continuous learning and adaptation within AI systems is a critical technical challenge. Ensuring these systems can dynamically update and enhance their performance based on evolving data and environmental conditions, without necessitating complete overhauls or system-wide restructuring, is a key area of focus.
• Interoperability: The seamless interoperability of AI systems with diverse components and systems from multiple manufacturers is critical for delivering a coherent user experience and ensuring the effective functionality of the overall system.

How do you foresee AI transforming the future of automotive product development in the next five years?

In the coming years, artificial intelligence is poised to play a pivotal role in transforming automotive product development across several key areas.

Firstly, the integration of AI-powered generative design tools will enable automotive engineers and designers to explore a much broader design space, catalyzing the creation of more innovative and optimized product concepts. These AI systems will be capable of analyzing extensive datasets encompassing user preferences, driving behaviors, and environmental factors to generate novel design proposals that are better aligned with evolving customer needs.

Secondly, the utilization of AI-driven simulations and digital twins will significantly accelerate the overall product development lifecycle, facilitating rapid prototyping and iterative refinement. These virtual environments will enable the testing and validation of product performance under a wide range of operating conditions, substantially reducing the need for physical testing and shortening time-to-market. Moreover, the incorporation of AI-based predictive analytics will enhance decision-making throughout the product development process.

Thirdly, the integration of AI will play a transformative role in optimizing automotive manufacturing workflows. AI-powered computer vision and anomaly detection systems will enhance quality control, identify defects, and facilitate real-time adjustments to production processes. Additionally, robotic systems integrated with AI will streamline assembly and logistical operations, leading to improved overall efficiency and productivity.

Finally, the continuous learning capabilities of AI will enable automotive products to evolve and adapt over their lifetime, with the potential to unlock new functionalities and enhanced user experiences through the software updates. By seamlessly integrating AI across the entire product development lifecycle, from conceptualization to manufacturing and beyond, the automotive industry can expect to see significant advancements in innovation, quality, and responsiveness to customer needs.

What skills do you believe are essential for aspiring product designers and developers to thrive in the AI-driven automotive industry?

As the automotive industry increasingly embraces AI, aspiring product designers and developers will require a diverse skill set to thrive in this rapidly evolving landscape.

Firstly, a strong foundation in both product design and software engineering is crucial. Product designers must possess a deep understanding of user needs, ergonomics, and the overall user experience, while also being proficient in the latest design methodologies and tools. Concurrently, expertise in software engineering, particularly in areas such as AI, machine learning, and data analytics, will be essential to translate design concepts into functional, AI-enabled automotive products.

Secondly, the ability to collaborate effectively across multidisciplinary teams will be paramount. Product designers and developers will need to seamlessly integrate with specialists in areas such as materials science, mechanical engineering, and electrical engineering to ensure the successful implementation of AI-driven features and capabilities.

Thirdly, a keen understanding of the automotive industry’s regulatory landscape and safety requirements will be vital. Aspiring professionals must be equipped to navigate the complex web of regulations, safety standards, and ethical considerations that govern the integration of AI within vehicles. Additionally, the adaptability to continuously learn and stay abreast of the rapidly evolving AI and automotive technologies will be a key differentiator.

Finally, the possession of creative problem-solving skills and a strong user-centric mindset will be instrumental. As AI-driven automotive products become increasingly sophisticated, designers and developers will need to think beyond traditional product boundaries and explore novel, human-centered solutions that leverage the full potential of these advanced technologies. By developing this multifaceted skillset, aspiring professionals will be well-positioned to contribute meaningfully to the transformation of the automotive industry, driving innovation and shaping the future of AI-powered mobility.

Can you discuss a time when a product development project did not go as planned and how you and your team overcame the obstacles?

The development of AI-powered automotive products often presents unique challenges that require a nimble and adaptive approach from the product design and development team. One such instance that I recall was the development of a new process for glass primer application. Initially, our team had proposed a solution that involved manual primer application on the safety component of the windshield glass, without any system to verify the presence of the primer on the component. However, during the validation phase, we encountered a significant issue – the primer application was inconsistent, with the primer sometimes missing from the component, leading to quality control problems. To address this challenge, our team recognized the need for a more robust and reliable solution. We decided to integrate an AI-powered computer vision system to automate the primer application process and verify the presence of the primer on the component in real-time. This transition required a significant shift in our approach, as it involved not only the integration of new hardware and software components but also the need to upskill our team members in the latest AI and machine vision technologies.

The implementation of the AI-powered computer vision system not only improved the overall quality and consistency of the primer application process, but also significantly increased the manufacturing yield. The automated verification of primer presence on the safety component eliminated the previous issues with inconsistent manual application, resulting in a more reliable and efficient production workflow. This technological integration not only enhanced the quality control measures but also boosted the overall productivity of the manufacturing operation. The successful implementation of this AI-driven solution was a testament to the agility and problem-solving capabilities of our product design and development team. This experience underscores the importance of maintaining a flexible and adaptive mindset when working on AI-driven product development projects.

How do you balance creativity and innovation with practicality and functionality in your designs?

Developing innovative and impactful automotive products necessitates a delicate equilibrium between creativity and practicality, which is a fundamental challenge. The foundation of our design approach is a deep comprehension of the end-user and their evolving requirements. We believe that authentic innovation stems from a profound empathy for the human experience and a commitment to enhancing it. By immersing ourselves in the lives and pain points of our customers, we can identify opportunities for transformative design solutions that push the boundaries of creativity while delivering tangible, functional benefits. Our design process seamlessly integrates visionary thinking and pragmatic problem-solving. At the conceptual stage, we encourage our team to explore bold, unconventional ideas, drawing inspiration from diverse sources and challenging preconceptions.

By leveraging AI-driven generative design tools, we can explore a broad design space and uncover innovative concepts that challenge conventional thinking. These AI systems, equipped with advanced algorithms and access to extensive data repositories, can rapidly generate and evaluate numerous design iterations, revealing unexpected and innovative directions that may have been overlooked by our human designers.

However, creativity alone is not sufficient; true design excellence demands a careful balance of form and function. Our team of multidisciplinary experts, comprising industrial designers, mechanical engineers, and software developers, collaborate closely to ensure that our creative visions are grounded in the realities of manufacturing feasibility, safety regulations, and user-centric performance requirements.

Our design approach involves an iterative process of prototyping, testing, and refinement to continuously optimize our products for both aesthetic appeal and practical functionality. This allows us to push the boundaries of innovation while ensuring that our final offerings are not only visually compelling but also highly usable, durable, and reliable. By seamlessly integrating creativity and technical expertise, we are able to deliver automotive products that captivate the senses, enhance the user experience, and establish new industry standards.

How do AI-powered Product Development systems differ from traditional Product Development systems?

AI-powered product development system differs from traditional systems in several key ways:

• Speed and Efficiency: Compared to traditional product development systems, AI-powered systems demonstrate significantly greater efficiency and cost-effectiveness through process automation and advanced data analytics. In contrast, conventional approaches often depend on manual tasks and subjective decision-making, which can be time-intensive and suboptimal.
• Data Utilization: Conventional product development approaches typically depend on manual data gathering and subjective interpretation, whereas AI-powered systems leverage large-scale data analytics to inform decision-making. AI-driven frameworks possess the ability to rapidly process and analyze extensive data from diverse sources, which can then be leveraged to guide the design and development process.
• Adaptability: AI-driven product development systems exhibit greater agility and adaptability compared to traditional approaches. These AI-powered frameworks are capable of rapidly assimilating new information and evolving market conditions, enabling a more responsive and flexible design process. In contrast, conventional product development systems often tend to be more rigid and may struggle to keep pace with the dynamic shifts in customer requirements and technological advancements.
• Quality and Precision: The integration of AI-powered systems has been shown to enhance precision in design, manufacturing, and quality control processes through the application of advanced algorithmic frameworks and real-time monitoring capabilities. In contrast, traditional product development methods may be more susceptible to inconsistencies and human errors, which can impact the overall quality and consistency of the final outputs.
• Scalability: AI-powered solutions demonstrate superior scalability, enabling organizations to more readily expand operations and adapt to fluctuations in demand. Conversely, traditional product development systems may encounter greater obstacles in scaling up production and associated processes.

What advice would you give to companies looking to implement AI in their product design and development processes?

As the automotive industry increasingly embraces AI, organizations seeking to implement these transformative technologies in their product design and development processes must approach the task strategically and holistically. Firstly, it is crucial for organizations to develop a clear understanding of the specific challenges and opportunities that AI can address within their unique context. This entails a comprehensive assessment of existing design workflows, identifying pain points, and recognizing areas where AI-driven solutions can drive tangible improvements, such as in product optimization, rapid prototyping, and decision-making processes.

Secondly, organizations must establish a versatile, cross-functional team that integrates expertise in product design, software engineering, and AI/machine learning. These professionals should possess not only profound technical proficiency but also the capacity to collaborate efficiently, cultivate cross-functional synergies, and advocate for the integration of AI throughout the design and development process.

Thirdly, organizations must prioritize the development of a robust data infrastructure and governance framework. Successful AI implementation necessitates access to high-quality, well-structured data that can be utilized to train and refine the algorithms. Establishing rigorous data management practices, ensuring data privacy and security, and cultivating a data-driven organizational culture will be crucial for realizing the full potential of AI-powered design and development.

Furthermore, companies must embrace a culture conducive to experimentation and continuous learning. Integrating AI into product design is a dynamic and evolving process, requiring organizations to be adaptable, iterative, and receptive to lessons from their experiences. Establishing clear feedback mechanisms, fostering an innovative mindset, and being open to both successes and failures will be essential for driving meaningful progress.

Ultimately, companies must thoughtfully consider the ethical ramifications of integrating AI into their processes and design their AI-based solutions in alignment with principles of fairness, accountability, and transparency. By proactively addressing these crucial considerations, organizations can effectively leverage the power of AI to enhance their product design and development capacities, culminating in the delivery of innovative, user-focused offerings that drive long-term competitive advantage.