Research into software for design, simulation and optimization in additive manufacturing.
Bits to Atoms is conducting independent and unbiased research into software for design, simulation, and optimization for additive manufacturing. Through one-on-one interviews with both developers and users, analysis of academic papers, and questionnaires, we aim to understand cutting-edge developments and market adoption.
DESIGN AND ADDITIVE MANUFACTURING SOFTWARE Landscape
Bits to Atoms is developing a comprehensive map of the design, optimization, and manufacturing software landscape currently available for additive manufacturing.
Navigating the capabilities and connectivity of the software has become overly complex and opaque.
The project aims to demystify the software terrain, aiding individuals and organizations in charting a clear path through the technological landscape to make informed decisions regarding which software to use, purchase, and/or develop.
We are preparing a series of questionnaires to collect data on specific software applications or categories related to design, simulation, and optimization for additive manufacturing.
Take some time to fill out these brief questionnaires, as your input will be valuable in expanding our understanding of the current landscape.
If you feel that you have more information to share or would like to collaborate with us in any way, please reach out.
We are always eager to engage with individuals who share our passion for advancing the field of additive manufacturing through research and development.
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Join the CDFAM Computational Design (+DfAM) Symposium in New York City, where leaders in the fields of computational design and advanced manufacturing will come together.
Register to participate in two days of insightful presentations and networking opportunities with leading engineers, academics, and software developers, focusing on topics such as AI in additive manufacturing.
Distinguished researchers from Altair, Autodesk, Carnegie Mellon, MIT, Cognitive Design Systems, and more will explore the applications of AI in the AM domain, discussing both current implementations and future advancements.