Beyond the hype, one should be able to know if AM is the right production candidate for every application. In the market, there are other several software solutions available to support AM users in this regard. One more does not harm.
Another service that aims to address this question for future users is the online analysis tool, “Can I 3D Print This?” launched by industrial 3D printer manufacturer EOS and its Additive Minds applied engineering group. EOS partnered with CASTOR, to bring the Can I 3D Print This tool to fruition which is not surprising when we know that the software company provides a software solution for part identification of 3D printed parts.
Designed for both metal and polymer 3D printing, the no-cost, web-based analysis tool provides freedom and independent inquiry for those investigating the viability of Laser Powder Bed Fusion (LPBF) industrial 3D printing for production.
Once the user has filled in their information about their current manufacturing method(s) and the part design file, the tool generates a comprehensive analysis including cost estimation, predicted production time, a recommended (EOS) AM system, and recommended material. Once the analysis is complete, a report is generated and provided for download. The analysis also includes multiple comparisons of cost-per-part (CPP), production timeline, and material usage when manufacturing with 3D printing rather than current traditional processes.
Each custom application analysis report includes:
- Application geometry analysis
- Recommended AM system (3D printer) and material
- Matched material qualities in comparison to current application material
- Financial break-even and production time analysis in comparison to current manufacturing method
- CPP comparison to current manufacturing process and product life cycle
- “Smart” CPP analysis that adjusts based on part orientation
The web-based tool is currently only available for North American users, but EOS is exploring a global offering. Users are limited to the analysis of five (5) parts, but larger batch analysis of applications or assemblies can be completed through a collaboration with EOS’ Additive Minds engineering team.
While I welcome such type of solutions, I also look forward to seeing solutions that will enable users to know if they should print everything.
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