India Insights

Data-Driven Machine Learning Software AMOptoMet for Additive Manufacturing

The Additive Manufacturing industry has reached a stage where it has started delivering on the hype of the last five years. The Industry has seen 18% growth in 2018, standing at $9.3 billions in revenue. Optimized processes, increased confidence from multinational large corporations  and advanced tools which enhances the machine & supply chain has a major contribution to this growth.

The additive manufacturing technology itself is rapidly evolving and now, INTECH, the Bangalore headquartered company has developed a cutting-edge data-driven additive manufacturing (AM) software called AMOptoMet. The latter analyses the correlation between the various Additive Manufacturing process criteria and the actual material performance in reality. This helps in accurately predicting the printing parameters to be implemented in 3D printers.

It is a dynamic tool that can be customized to accommodate specific powder traceability and thermal management standards. In addition, it is capable of adjusting the changing Particle Size Distribution and thermal differences during the build process.

In November 2018, DMG MORI teamed up with Intech with an aim to benefit all those involved in the additive manufacturing industry through the integration of AMOptoMet software into their LASERTEC machines. The combination greatly enhances data availability and by using large-scale machine learning, it creates value for the customers.

AMOptoMet software collates empirical data collected from the machines onto networks, thus augmenting the theoretically predicted data. These multiple inputs are then merged into creating a multi-dimensional data structure that empowers better decision making. The real power of the industrial usage of IoT technology is not merely in linking the devices to each other, but also in compiling information that facilitates qualitative decisions. This is precisely the power that AMOptoMet harnesses. This cutting-edge software is truly remarkable, and can do the following:

Forerunner prediction

Predicting mechanical performance and properties based on an available set of process parameters. This allows the user to overcome the time and expense required by a conventional trial-and error process to a greater extent.

Transposition –

Upon receiving an input such as desired hardness, the machine algorithm decides on the process parameters to be deployed to achieve the desired results that have the potential to accelerate the application development.

Machine Learning

AMOptoMet continuously improves its learning from earlier data sets and applying the knowledge thus gathered to new datasets. As more data is generated across the organisation, the software will continually improve and the predictive reliability will continue to become more accurate.

It constantly improves its output accuracy and reduces the requirement for data needed for new builds. Intech is also aligning with an increasing number of powder manufacturers and adding newer alloys to the basic alloy module. The customer is given freedom to add his own alloys and keep the information confidential. AMOptoMet software is going to reduce the repeated design of experiments by creating printing parameters based on the composition of the alloy, PSD and machine specifications.

The path-breaking software has now entered the second phase of its development and now AMOptoMet will receive value enhancements courtesy of the integrated hardware products. The new program will have an algorithm that would facilitate real-time analysis and in-situ monitoring of data behind the creation of OptoTherm and Powder Management Modules.

AMOptoMet’s algorithms are capable of processing the thermal data and marking areas of the build surface that have higher or lower temperature. The AMOptoMet Powder Management Module is a non-stop monitoring of particle size distribution process. It dynamically adjusts to changing parameters. In both modules, the tools evaluate data collected with each reading of PSD and then co-relate between the irregularities of the build and their impact on the mechanical performance. This allows it to autonomously alter the printing parameters to ensure sturdy build and quality of the end-product.

The AMOptoMet software is now available in the market and for any company keen to build AM parts.