Interview: VISHWANATH RAO, MD, Altair India: ‘AI makes product designing cost effective’

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October 27, 2021 12:45 AM

Historically, the field of product design and engineering has found itself behind other industries in terms of AI and ML adoption due to the volume and nature of the data required.

Unexpected downtime can significantly impact tangible and intangible operating costs.Unexpected downtime can significantly impact tangible and intangible operating costs.

Advancements in the fields of Artificial Intelligence (AI) and machine learning (ML), combined with the increased availability of robust simulation, testing, and field data sets has made engineering data science a critical component of the modern product development lifecycle. Altair is a leading technology company that provides software and cloud solutions in the areas of simulation, high-performance computing (HPC), and AI. In a recent interaction, Vishwanath Rao, managing director, Altair India, explains to Sudhir Chowdhary how technology can help manufactures save time, cost and create better products. Excerpts:

What are the challenges in product design that Altair sees manufacturers facing today?
Historically, the field of product design and engineering has found itself behind other industries in terms of AI and ML adoption due to the volume and nature of the data required. The design of three-dimensional products often requires highly complex 3D CAD models, FEA meshes consisting of millions of elements, high-fidelity simulation of multiple physics, and optimisation runs exploring multiple variants of a design. These add up to a glut of data and too often, little in the way of an enterprise-level roadmap for how to utilise it, share it with other groups, or even if and where to save it at all.

Advancements in the fields of AI and ML, combined with the increased availability of robust simulation, testing, and field data sets has made engineering data science a critical component of the modern product development lifecycle. Computer-aided engineering (CAE) augmented by AI is offering manufacturers the ability to discover machine learning-guided insights, explore new solutions to complex design problems through physics and AI-driven workflows, and achieve greater product innovation through collaboration and design convergence.

How can modern technology help manufacturers create better products?
Our AI technology in design generation, design exploration and design optimisation helps product designers explore a broader population of customer pleasing, high-performing, and manufacturable new product design alternatives. Automating repetitive tasks using ML intuitively performs direct modeling for geometry creation and editing, mid-surface extraction, surface and mid-meshing, mesh quality correction, combined with efficient assembly management and process guidance.

Simulation technology combined with design exploration and ML enables engineers to meet time-to-market challenges effectively, and helps teams deliver higher performing products that consider more design dimensions throughout the development process. By applying the same physics-based tools used for verification from concept to design, and through to the sign-off stage guided by ML, enables faster design convergence by confidently rejecting low-potential designs earlier in development cycles.

How can predictive analytics and machine learning help manufacturers save cost and time in preventive maintenance?
Unexpected downtime can significantly impact tangible and intangible operating costs. The implementation of smart manufacturing combined with the power of Industrial IoT have enabled organisations to collect real-time data about how their equipment is operating and avoid unnecessary maintenance.

Altair’s data analytics platform helps manufacturers perform preventative or corrective actions using insights found by analysing data generated directly from their equipment. ML can immediately show benefits, whether with existing assets equipped with sensors or new wireless sensors without historical data. The system can trigger insights based on anomaly detection and it can classify different types of faults.

With the insights from our predictive analytics, data science teams can deliver optimised maintenance routines that will minimise unexpected downtime and add efficiencies to regular operations—all completed without manually creating sophisticated algorithms from scratch or a need for experience in advanced analytics programming.

How can product developers access the HPC power they need, migrate to the cloud, and eliminate I/O bottlenecks?
Many organisations struggle to manage and mine data that comes from modern technology platforms. The data arriving into the organisation may be in the form of a small amount of very large files, or in the form of millions of very small files arriving every day, or even every minute. Altair’s workload management tools enable organisations to work efficiently with big data in high-performance computing, modern processing and storage platforms, and cloud environments.

In the data centre and in the cloud, Altair’s HPC tools let you orchestrate, visualise, optimise, and analyse your most demanding workloads, easily migrating to the cloud and eliminating I/O bottlenecks. Top500 systems and small to mid-sized computing environments alike rely on Altair to keep infrastructure running smoothly. With longstanding hardware and cloud provider partnerships, we handle the integrations for customers so that they can focus on moving business forward.

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