Artificial intelligence (AI) is an evolving technology that is still growing, but it is undoubtedly getting better. 

For all we know, factories might not even need lights 20 years later, because most of them will be operated by AI. We see a lot of R&D happening within the AI framework, giving good results. Besides, we see newer frameworks coming in. 

AI-based visual inspection, too, has been growing by leaps and bounds, reshaping automotive inspection. It helps examine detailed defects in vehicles, providing automotive OEMs the opportunity for accuracy and cost-savings. 

One such company focusing on AI-based visual inspection is Lincode Labs, whose AI-backed visual inspection solution, Lincode Visual Inspection System (LIVIS), is its current focus. The company was started with complete research and understanding of the top challenges that manufacturers globally face. After interviewing close to 100 customers, 86 percent of them said that quality inspection happens to be their biggest challenge.

“We were intrigued by this and went to various quality inspection people and identified the technology they were using,” says Rajesh Iyengar, Founder and CEO, Lincode Labs, and goes on, “We went on to find out that the technology hasn’t changed for two decades and there were a lot of false calls in it. That’s when Lincode stepped in and built a product around specific challenges focused on the automotive industry.”

Automotive OEMs, too, look at specifically reducing these false calls and improving productivity, which Lincode has helped solve through its AI-backed visual inspection solution. “The industry standards were 150 to 200 false calls per million inspections. So, in our case, we are doing it in zero to four parts per million,” Iyengar cites.  

Iyengar further reveals that due to this, 80 percent of their customers are repeat orders. “That’s because they are completely happy with the inspection process and the way the inspection is automated,” he mentions.  

LIVIS
Traditional vision systems cannot catch up with AI, as Iyengar says. He avers, “LIVIS stands out because we have built it as a platform. The scalability becomes easier if you’re going to deploy it across multiple factories and locations. But also, the foremost important thing is that it is completely made as a product. Thus, AI is commoditised. With the LIVIS platform, we can bring the cost and time to deployment down.” 

Lincode’s role in the automotive industry
What’s interesting is that even if Lincode caters to the manufacturing industry as a whole, it first addressed the automotive industry. The company researched the market size of various manufacturing companies and the automotive industry took the top spot, with close to USD 542 billion of global value.  

“We started with the automotive industry but pivoted later,” Iyengar tells us and continues, “So, instead of looking at just the automotive or any other industry, we turned our attention to steel, metal, plastic, glass etc. We basically went to the surface and saw how steel and metal are produced today. Whether it’s a CNC machining or forging or casting process, these are major processes used for any industry across the globe involving steel and metal. We understood that steel and metal are dealt with in the same way globally. Therefore, it made sense to go to the surface and into these kinds of defects specifically, and then generalise that and start building a model towards it. This, plus making AI as a product, has made deployment easier across the globe.” 

R&D centre in Bengaluru
Lincode recently opened a new R&D centre in Bengaluru, which also has a significant role to play in deploying the company’s solution across the world.

Stressing on the fact that evolving models are important in AI, Iyengar states, “It’s a continuous process; it’s not that you just build a model and you’re set. We have a big roadmap in the product development, and the Bengaluru R&D centre is going to play a major role in that. We are going to conduct deep research with various data collected across the globe and do various testing with that.” 

Staying ahead
What’s more, Lincode recently closed a funding round in December last year. Catering to a constantly evolving industry like the automotive, Lincode, too, strives to make sure that its visual inspection solution stays ahead and is put to use. “There are about more than 600 parts in a car and each part is segregated – like the structure, wiring, engine components etc.,” Iyengar shares and continues, “These segregations are made so that we can target the sector of the product. For example, when it comes to engine blocks, there is a specific model with a huge data set around engine blocks. This is how we stay ahead of competition.” Iyengar also adds that their trials with various use cases made them understand that inspection alone is not important but also the way the inspection is done. 

Essential skill sets for AI vision systems
Leveraging AI-based visual inspection solutions in the automotive industry is bound to increase productivity, and the cost of labour will also come down because of automation. “Today, most manufacturers use secondary inspection, which can be cut off straight away. This will improve their productivity and also reduce the risk of delays,” Iyengar enlightens. 

Moreover, AI vision systems come with their share of essential skill sets to bring out the best in the automotive industry. Iyengar states that, in general, skilling is required for the factory people. “This could be at various levels,” he puts across and adds, “It could be for the operators, the IT administrator or even the software development team. Hence, deep training is required, which can be somewhat cumbersome because it could be a bit challenging for the operator. So, an IT person might be needed in order to help the operator every time there is a downtime.” 

Covid-19 and AI-backed visual inspection
Such training or skills could certainly come in handy, because Iyengar claims that the need for AI-backed visual inspection solutions in the automotive industry has increased since the Covid-19 pandemic. “Unplanned shutdowns happened during Covid, because of which employees could not report and manufacturing could not continue properly,” he responds and adds, “Hence, a lot of investments are happening because of this. In fact, even now, a lot of employees are still not reporting and the labour problem has become global. It has become tough to get skilled workers. This has led to the adoption of autonomous manufacturing for automotives, where AI is going to play a big role.”

Meeting industry requirements
For an industry that is an economic force globally, AI-based visual inspection is certainly meeting the high-quality requirements of the customers of the automotive sector. Plus, we already see companies like Volvo using the technology. Safety surpasses any requirement, and this requirement can be fulfilled if quality is top-notch. And quality will be at its best if automotive manufacturers can perform production quality inspections in the most efficient way. (MT)

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Toyota Kirloskar Motor (TKM) has signed a Memorandum of Understanding (MoU) with Wipro 3D to create a Centre of Excellence (CoE) for additive manufacturing. The facility will be located at the Toyota Technical Training Institute (TTTI) in Bidadi, Bengaluru. The partnership is intended to facilitate skill development and the integration of 3D printing technologies into production environments.

The centre will provide students with exposure to industrial applications of additive manufacturing, including rapid prototyping and the development of production aids. Wipro 3D will provide technical expertise and training modules covering internships, apprenticeships and workshops. The curriculum will also incorporate digital manufacturing and resource optimisation as part of an emphasis on Industry 4.0 technologies.

By leveraging these manufacturing capabilities, the initiative aims to reduce lead times and improve assembly line efficiency. The TTTI, which focuses on vocational education in trades such as mechatronics and welding, doubled its intake to 2,400 students in 2023. This collaboration aligns with the institution's objective to build technical talent for the automotive sector.

G Shankara, Chief Strategy Officer, Toyota Kirloskar Motor, said, "Our Human Resource Development philosophy at TKM follows core principles of Toyota such as, Continue the Quest for Improvement, Show Respect for People, under which we thrive hard to develop individuals in the Latest Technology of the New Age Era of automotive field. We are also committed to nurturing skilled talent and strengthening India’s manufacturing ecosystem. This collaboration will play an imperative role in nurturing future-ready talent, while contributing meaningfully to the Government’s Skill India Mission.”

Yathiraj Kasal, Business Head and General Manager, Wipro 3D, added, “This association reflects our commitment to strengthening India’s manufacturing ecosystem through capability building and innovation, while creating industry-relevant learning experiences.”

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TomTom has introduced Unified Speed Restrictions, a new service providing updated speed limit data for global regulatory compliance and Advanced Driver Assistance Systems (ADAS). The service is designed to help vehicle manufacturers exceed the minimum requirements of Intelligent Speed Assistance (ISA) regulations.

The service integrates multiple static and live data sources into a single output. By combining various inputs, the system provides continuous updates to vehicle software to ensure speed limit identification remains accurate across different driving environments.

Data sources utilised include:

• Unsigned speed limits: Based on regional road classifications.
• Roadside sign recognition: Camera-based detection of physical signs.
• Probe data: Aggregated information from connected vehicles.
• Variable speed limits: Real-time data from electronic overhead gantries.

Beyond safety compliance, the service supports automated driving functions by providing data for predictive path planning and smoother vehicle manoeuvres.

The solution is available as an API or pre-integrated within the TomTom ADAS SDK. The SDK is modular, allowing manufacturers and Tier 1 suppliers to incorporate the data into existing software stacks without vendor lock-in. This architecture is intended to reduce development costs and accelerate the deployment of predictive assistance features.

Manuela Locarno Ajayi, SVP of Product Engineering, TomTom, said, “Accurate and trusted speed information is foundational to road safety, regulatory compliance and automated driving at scale. With Unified Speed Restrictions, we are equipping automakers with a globally consistent, future‑ready foundation that reduces complexity, enabling higher levels of automation.”

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Detroit Engineered Products (DEP) has introduced DEP AIWorks, an engineering platform designed to integrate machine learning with physics-based simulation. The launch follows the conclusion of a five-city industry conclave held across Bengaluru, Delhi NCR, Hyderabad, Pune and Chennai.

DEP AIWorks is built as a physics-agnostic and tool-agnostic environment, allowing it to function across various datasets and engineering domains. The platform combines neural networks and physics-informed models with computer-aided engineering (CAE) solvers to provide predictive and generative capabilities within the product development lifecycle.

Core features of the platform include modular architecture, operational speed and ecosystem compatibility.

The platform is intended for use in the automotive, aerospace, energy, manufacturing and telecommunications sectors. It supports various stages of development, from early design exploration to manufacturing validation. By utilising data-driven learning alongside physics-based validation, the system aims to improve engineering productivity and accelerate decision-making cycles.

Radha Krishnan, President & Founder, DEP, said, “DEP AIWorks reflects the next step in how engineering organisations will adopt AI, not as a standalone tool, but as an integrated part of the product development lifecycle. By combining decades of simulation expertise with advances in AI, we are enabling teams to move faster while maintaining engineering rigor and reliability.”

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German tier 1 supplier ZF has introduced SolarBoost, a retrofittable solar panel system designed to support the 24-volt on-board electrical systems of city buses and coaches. The technology generates electricity during vehicle operation to recharge batteries, intended to reduce fuel consumption and maintenance requirements for fleet operators.

The system reduces the load on the drive engine by providing an alternative power source for on-board systems, which are traditionally supplied by the alternator. According to ZF, the additional energy can reduce fuel consumption by up to 3.5 percent, depending on weather conditions and application profiles.

The company states that key benefits for operators include battery longevity, as continuous recharging extends battery life. ZF reports potential savings equivalent to one battery per vehicle per year.

Furthermore, it enhances uptime by reduced requirement for stationary battery recharges and lower maintenance frequency. The system includes Bluetooth connectivity, allowing operators to track energy generation in real-time via a mobile application.

SolarBoost utilises a plug-and-play architecture designed for installation in an operator's own workshop using standard tools. The process does not require drilling into the vehicle structure or extensive rewiring, allowing for fleet-wide scaling with minimal disruption to service.

The hardware is engineered to withstand vibrations and weather conditions associated with heavy-duty transit. ZF provides a 5-year warranty and repair kits to support the long-term durability of the flexible panels.

The product is positioned as a scalable solution for bus operators to meet environmental targets. By utilizing renewable energy for electrical loads, the system assists in reducing the carbon footprint of intercity and urban transport fleets. It aligns with ZF’s broader strategy to deliver innovations that improve vehicle efficiency while supporting climate-friendly mobility.

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