Automation and Robotics have become important aspects when talked about the global Industrial sector. Especially, automotive, infrastructure and packaging industries are using aluminium as one of the key materials for manufacturing of relevant components, Rahul Prajapat, Founder and CTO, TVARIT.

Driving the world towards sustainable and zero waste manufacturing is something that holds as a vision for a young and passionate technocrat like Rahul Prajapat, Founder and CTO, TVARIT. He is responsible for the Technology and Go to Market activities of the TVARIT GmbH. Prajapat having strong expertise and huge insights in the segment of Smart factory, Industry 4.0, and Metal manufacturing, under his leadership, TVARIT has substantially grown the business and major rollouts of TVARIT’s platform TiA are currently ongoing.

AlCircle: How is Industry 4.0 changing the dynamics of the global aluminium and metal industry from TVARIT’s point of view?

Mr Rahul Prajapat: The global aluminium and metal industry is facing severe challenges such as high scrap rate, low production due to unplanned downtime, high energy consumption, just to mention a few. To overcome these challenges, TVARIT visions itself to drive the world towards sustainable and zero waste manufacturing by using a Hybrid AI model combining modern AI technology with metallurgical and production process knowledge which is used to provide a solution for predictive maintenance, predictive quality, predictive energy and AI-assisted production planning. This hybrid AI and machine learning technology is developed by integrating manufacturing process knowledge into Ai and machine learning models in terms of machine maintenance schedule, product geometry and logic rules based on production engineers.

AlCircle: How TVARIT is driving the aluminium industry with its AI solutions?

Mr Rahul Prajapat: In order to understand the implications of our AI solutions, it is first obligatory to know the various day-to-day manufacturing challenges faced by the Aluminium Industry.

Deviations in the chemical composition of the material such as H2 content and geometrical characteristics (percentage of crown) are repeatedly causing quality deviations of the aluminum coils during the production process. This is resulting in a high reject rate as well as additional efforts and therefore increased cost due to rework or discounted sales.

No real-time information on the multi-stage manufacturing process is available and the quality of the batch can only be determined after the evaluation of laboratory tests, which are only accessible after two days on average. Therefore, the potential performance level of the entire production is limited.

Solution:  In order to address these challenges we have developed a hybrid AI and machine learning technology by integrating manufacturing process knowledge into AI models in terms of machine maintenance schedule, product geometry and logic rules based on production engineers. This Hybrid AI model provides a solution for predictive maintenance, predictive quality, predictive energy and process optimization or technology enriches the AI model with manufacturing process knowledge. Hence it can be scaled to various materials and processes with minimal effort. We use cognitive machine intelligence to further reduce scaling efforts and can start with a small sample of data. This gives us a unique advantage against our competitors.

The TVARIT Industrial AI platform (TiA), displays the results of the data analysis and the AI model in a clear and customizable dashboard. This enables quality managers, process engineers and manufacturing engineers to gain actionable insights based on the captured process parameters which are processed by the software in real-time. Notifications and alarms ensure that anomalies and potential failures are detected during the production process. In addition, the concrete recommendation for optimal process parameters provided by TiA helps to stabilize product quality. This is reflected in shorter lead times, a reduction of the scrap rate by up to 75% and by over 25% increased machine up-time, higher overall effectiveness, and thus increased productivity.

AlCircle: Can you please outline TVARIT’s investments and further expansion plans concerning industry 4.0?

Mr Rahul Prajapat: TVARIT’s main focused geographical markets are Germany and Europe. But we are also expanding and targeting other regions like India, Japan and USA with our solutions and offerings. The industrial market segment we are targeting is the metal processing industry (Automotive industry, Piping and structural equipment manufacturing). The main target processes are low pressure die-casting, welding technology, injection molding, bayer process and cold forming. Also, areas like remaining tool life in the (milling and drilling) market segment as well as hot forming are focused in our go to market approach. TVARIT generally focuses on four core areas i.e., predictive quality, predictive maintenance, predictive energy and AI-Assisted Production Planning. We believe in core research, always trying to build a consortium of Industry-academia research relationship to solve the problem and thus can be scaled to multifarious applications. We have an academia relationship with worlds top notch research Institutes such as Stanford University, TU Darmstadt, IIT Bombay and IIT Delhi. So, high efforts are invested in core R&D to build the product right from scratch. TVARIT is headquartered in Germany and has a research and development center in India. In future, TVARIT plans to extend operations in the US and the important markets in Asia like Japan, South Korea and Taiwan for both strategic business expansion and investment purpose.

AlCircle: Automation and Robotics are revolutionary in the global industrial sector including aluminium in regard to improvising efficiency and productivity. What is your assessment of this factor?

Mr Rahul Prajapat: Automation and Robotics have become important aspects when talked about the global Industrial sector. Especially, automotive, infrastructure and packaging industries are using aluminium as one of the key materials for manufacturing of relevant components. Main hurdles of these industries are to reduce the scrap rate and energy consumption. Automation and robotics have started rescuing these industries. The advanced technology has now conquered the long spent human efforts by completing the task effectively and is available 24/7 without any additional labour costs. In addition to this, with the aid of automation and robotics, it becomes very easy to implement AI technology which helps the manufacturing industries to reduce scrap rate, increase machine efficiency, improve production planning and save energy. The AI technology also helps to reduce the human caused errors as these human errors are associated with considerable time consumption and money-waste. And why these technologies shouldn’t be credited as they provide a resulted benefit on the operational cost by overtaking the human enforced assembly lines in limited workplaces. Hence, even small business owners can benefit of them although it comes with an unavoidable initial cost because the return on investment has more weightage. The new Industry revolution will enforce some of the most unexpected implications on humans and businesses. It all requires having a detailed understanding of your own operations and applying the right AI technology to the most required area for beneficial results.

AlCircle: What is your opinion about Industry 4.0? Has Covid-19 stimulated the industry 4.0 movement? If yes, please detail it.

Mr Rahul Prajapat: Industry 4.0 is a new buzz word among manufacturing industries. But we also know that it was industry 3.0 that led the introduction of computers and logic-based systems to automate manufacturing processes. Even though manufacturing processes were automated, human input is still required to manufacture and monitor them. This is where Industry 4.0 differs. The aim is to not only automate the manufacturing process, but also to automate it without human intervention. e.g. In steel manufacturing manual inputs are provided and changed during the manufacturing process, which is essential for maintaining the required grade and quality of the product.
There are usually three big problems in manufacturing Industry- cost reduction pressure, ESG Regulation and process experts and machine operators retiring which is the major concern. The solutions like predictive quality, predictive maintenance, predictive energy and AI assisted Production Planning can help overcome the major challenges and lead to sustainable manufacturing.

Even though Industry 4.0 comes with a huge set of benefits, but the current pandemic has affected the industry 4.0 movement. The pressure on manufacturing and production companies caused by the ongoing Corona crises has forced them to go new ways to increase efficiency and to remain profitable. Slumping demands, disruption of supply chains and a lack of staff cause a severe reduction of capacity and profit. This in return puts pressure on manufacturing companies, forcing them to further cut expenses through measures like short-time work, which again lowers capacity and therewith cash flow.

New challenges require new solutions. And besides cutting expenses, there is one way of increasing profit: Increasing efficiency. A higher degree of efficiency can be attained through an improved ratio of useful work by a machine or in a process to the total energy expended or taken in. In a nutshell, by increasing machine-uptime and reducing waste.

To achieve this, manufacturing companies can make use of one of their most important assets – an asset that, in many manufacturing companies, is still used very little: Production and machine data. The data collected from machines and quality inspections throughout the production processes contain information about the root causes of quality defects and machine breakdowns. Leveraging sophisticated algorithmic AI modules, accurate real-time predictions of quality defects or machine breakdowns can be made and optimal process parameters to increase the quality and to prevent machine breakdowns can be assessed. A variety of industrial use-cases have proven that scrap-rate reductions of over 75% and by over 25% increased machine up-time can be realised with the help of AI. The ongoing pandemic is presenting unacquainted challenges for industries. These are difficult times for manufacturers, but they will eventually pass. However, it is now the time to use all available assets and to leverage the capabilities of digitalisation to not only faces these challenges but to use it as an opportunity to move forward.