MASTA 16 Launched With AI and HPC for Faster Powertrain Development

Smart Manufacturing Technology (SMT) has launched MASTA 16, a major update to its powertrain design, analysis, and optimization software.

The new version was developed to help engineers perform faster simulation, wider design exploration, and make more confident engineering decisions.

Join Our Youtube Channel to stay updated with daily Indian car & bike news instantly! 

MASTA 16 expands on technologies such as AI-assisted optimization, High Performance Computing (HPC), automation, and higher-fidelity simulation. According to the company, its focus is to make the development process faster and more efficient, particularly for modern powertrains and increasingly complex electrified systems.

Also Read: Bolt.Earth Marks World EV Day with Free Charging Across Pan-India Blaze DC Network

What is MASTA 16?

MASTA is a CAE software platform used for the design, simulation, and optimization of transmissions, gears, bearings, shafts, electric machines, and complete powertrain systems.

SMT states that modern engineering teams need to evaluate multiple design possibilities early in development. However, high-fidelity simulations can be computationally expensive, making it impractical for engineers to test every possible design combination.

MASTA 16 attempts to address this gap. The new release helps scale simulation workflows, automate repetitive tasks, and allow engineers to explore more design options in a relatively short time.

AI and HPC: The Biggest Highlights of MASTA 16

MASTA 16’s major focus is on AI, automation, and scalable computing.

Multiple Simulations with High Performance Computing

New HPC capabilities allow simulations to be distributed across multiple machines or remote computing environments, rather than running on a single workstation.

The practical benefit is that engineers can run multiple simulations in parallel. This can reduce the turnaround time for complex studies and evaluate more design variations in the same timeframe.

According to SMT, HPC is not limited to making simulations faster. Its purpose is also to give engineers the opportunity to explore a larger design space before making development decisions.

Automated Workflows with the MASTA CLI

MASTA 16 also introduces a Command Line Interface (CLI).

Through this, MASTA analyses can be integrated into external systems, remote machines, and automated engineering pipelines. Engineers can offload heavy simulations to high-spec machines and automate repetitive simulation tasks.

The software also includes an integrated debugger for Python scripting, allowing engineers to develop, run, and debug automation scripts within MASTA.

AI-Based Optimization Receives Major Upgrade

The Machine Learning Micro Geometry Optimizer has been significantly upgraded in MASTA 16.

This includes capabilities such as AI-assisted contact patch evaluation, multi-objective Pareto optimization, and system-deflection surrogate modeling.

Simply put, it helps software engineers identify better trade-offs between multiple competing objectives, rather than optimizing for just one target. For example, parameters such as noise and durability can be considered simultaneously.

According to SMT, the system-deflection surrogate model can make optimization loops 2–3 times faster, particularly in larger MASTA models.

90% Development-Time Reduction in a Customer Evaluation

MASTA 16’s most interesting claim relates to machine-learning-based optimization.

According to SMT’s press release, machine-learning-driven optimization techniques reduced development time by approximately 90% in a customer evaluation. This allowed engineers to reach equivalent design outcomes significantly faster.

Here’s an important point: 90% shouldn’t be considered a universal MASTA 16 performance figure. It’s the result of a specific customer evaluation, so it’s more accurate to mention it in context within the actual article.

Also Read: Volkswagen U.S. Leadership Shake-Up: What’s Behind The Major Change?

Faster NVH Analysis with AI-Assisted Acoustics

MASTA 16 also introduces AI-assisted Acoustics Upscaling.

High-fidelity acoustic simulations provide accurate results, but running them can take considerable computational time. MASTA 16 uses machine learning to help identify important acoustic behavior and critical noise frequencies.

An important aspect of this approach is confidence bounds. This means that engineers receive not only a predicted result, but also visibility into the uncertainty level of the prediction.

According to SMT, this approach can help engineers conduct focused analysis on critical areas and evaluate multiple concepts relatively quickly.

Flexible Gear Blank Advanced LTCA

A major technical upgrade to MASTA 16 is Flexible Gear Blank Advanced LTCA.

This technology couples gear tooth contact, flexible gear body deflection, and wider system interactions into a single calculation. This helps model the behavior of lightweight or thin-rimmed gears in a more realistic way.

MASTA 16 models gear blank and tooth contact through a single finite-element representation, while a Hertzian representation is used for local contact stiffness.

Its objective is to improve the prediction of gear stresses, load distribution, and transmission error.

This technology could be particularly relevant for high-performance and lightweight applications, as well as for EV powertrains, where structural flexibility can impact NVH, durability, and efficiency due to lower weight and higher operating speeds.

Major Updates for Electric Powertrains

In light of the growing adoption of electrification, electric machine analysis capabilities have also been expanded in MASTA 16.

The new release includes features such as:

• Surface Permanent Magnet (SPM) machine modeling
• Demagnetization analysis
• Measured and simulated current waveform inputs
• Electric Machine Parametric Study Tool
• Enhanced parameter studies

Support for Real-World Current Waveforms

Electric machines do not operate on ideal sine-wave current under real-world conditions. Inverter behavior can cause distortion and ripple in the current.

MASTA 16 allows engineers to use measured or simulated current waveforms in simulations. This can help model electric machine behavior closer to real operating conditions.

Analyzing Demagnetization Risk

The new demagnetization analysis feature helps engineers evaluate the impact high temperatures or high-current operating conditions can have on the performance of electric machine magnets.

Performing this type of validation earlier in the development process can help identify potential performance issues before later-stage testing.

Bearing and High-Fidelity Modeling Improvements

Bearing modeling has also been upgraded in MASTA 16.

Integrating detailed bearing behavior into the workflow through meshed bearing rings has been made easier, while Continuous Flexible Interpolation has been introduced to more accurately represent bearing ring deformation and load distribution.

According to the company, these capabilities can be particularly useful in modeling large systems and flexible structures.

MASTA 16 also includes FE centrifugal effects, which help account for component deformation at high rotational speeds in the analysis. This is relevant for better representing alignment, contact conditions, and load sharing in high-speed applications.

Why is it important for automotive and aerospace?

Modern powertrains are becoming increasingly lightweight, high-speed, and electrified. This makes it crucial to understand complete system behavior in addition to traditional component-level analysis.

MASTA 16’s approach strengthens this system-level analysis. The focus is on integrating gear flexibility, tooth contact, electric machine behavior, NVH, and broader powertrain interactions into the simulation workflow.

In addition to automotive, SMT positions the MASTA platform for aerospace, robotics, off-highway, industrial equipment, motorsport, and energy applications.

MASTA 16 Also Relevant for India

SMT also has active technical engagement with India. The company covered topics such as MASTA 16 and Future Development Roadmap, Machine Learning Micro-Geometry Optimization, NVH workflows, and automation using scripting at the 2026 India Technical Forum.

The Forum also featured guest presentations from organizations such as Cummins, Eaton, Nirma University, and Valeo.

This clearly demonstrates the MASTA ecosystem’s technical interaction with the engineering and powertrain development community in India.

MASTA 16 Available Now

Smart Manufacturing Technology has officially made MASTA 16 available starting September 14, 2026. The overall focus of the new version is more than a simple software upgrade—the company wants to move simulation towards making the development process earlier, faster, and more decision-oriented.

With the combination of AI-assisted optimization, HPC, automated workflows, higher-fidelity gear analysis, and expanded electric machine capabilities, MASTA 16 becomes an interesting update, especially for electrified and high-performance powertrain development.

Auto India Daily Verdict

The biggest takeaway from MASTA 16 is not just AI, but practically integrating AI with the engineering workflow. HPC helps engineers run more simulations, AI optimization accelerates design exploration, while higher-fidelity modeling and real-world electric machine inputs try to bring simulation results closer to actual operating conditions.

A customer result of 90% development-time reduction is definitely attention-grabbing, but it’s not accurate to view it as a universal claim. Overall, the direction of MASTA 16 is clearly towards faster virtual development, broader design exploration, and system-level engineering of increasingly complex EV powertrains.

Note: This information and images were provided by Smart Manufacturing Technology (SMT) through an official press release.

Also Read: BYD 10000 Flash Charging Stations: 1,500kW Network Expands Rapidly