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#3DStartup: Manufacturing Technology Project Turns Metal 3D Printing into a Predictive Process

Manufacturing Technology Project (MTP) is an American start-up dedicated to developing engineering tools. These tools combine artificial intelligence, multiphysics simulation, and digital twins to optimize manufacturing processes and predictive modeling, with the ultimate goal of

Manufacturing Technology Project
3Dnatives

Manufacturing Technology Project (MTP) is an American start-up dedicated to developing engineering tools. These tools combine artificial intelligence, multiphysics simulation, and digital twins to optimize manufacturing processes and predictive modeling, with the ultimate goal of offering autonomous production control. Its first flagship product is called AdditiveM and, as its name suggests, is dedicated to metal additive manufacturing. One of the primary features of the simulation platform is that it improves the performance of 3D printing processes. We met with Hamed Hosseinzadeh, President & Tech Strategist, to learn more about the projects of this ambitious company!

3DN: Can you introduce yourself? How did you discover 3D printing?

I am a computational mechanical and materials engineer, and the President and Tech Strategist of Manufacturing Technology Project (MTP). My background is in physics-based modeling, multiphysics simulation, and AI-assisted engineering, with a strong focus on advanced manufacturing and materials processing.

Hamed Hosseinzadeh

I discovered metal additive manufacturing during my academic research while working on coupled thermal–mechanical–microstructural simulations. What immediately fascinated me was that additive manufacturing is not just a fabrication method, but a fully coupled physical system in which heat transfer, fluid flow, phase transformation, residual stress, microstructure evolution, and structural performance are all tightly interconnected. At the same time, I observed that much of the industry still relied heavily on trial-and-error and empirical tuning, which is costly, time-consuming, and risky for qualification-critical applications.