Castor is an Israel-based technology company that has developed software that automatically identifies parts in manufacturing that are suitable for 3D printing, by automatically analysing each part within an existing product design-based 3D CAD files and a set of geometric and economic analyses.
Now, however, the company has announced that it has added a new capability to its parts identification software: the ability to automatically analyse 2D drawings to determine the technical and economic feasibility of opting for 3D printing. This new feature opens the door for companies working primarily with on 2D PDF drawings to consider deploying additive manufacturing. Relying on 2D drawings that can be up to decades old makes it difficult and time-consuming to determine which parts might be a good fit for 3D printing. In these cases, this new capability can offer true benefits.
The technology behind Castor’s 2D analysis is based on computer vision that interprets the geometry and product manufacturing information (PMI) of each part and on machine learning models that continue to gain deeper insights and improve over time due to the vast number of parts that are uploaded to Castor on a daily basis. The software automates the complex process of analysing large numbers of parts that must otherwise be conducted manually.
The new release expands Castor’s value proposition for companies who were, up until now, unable to assess whether 3D printing would bring a benefit to their organisation, said Omer Blaier, co-founder & CEO of Castor.
“We give these organisations a tool that helps them find new business cases and discover opportunities to reach their initiatives and 3D printing goals, using their existing 2D design files,” he said. “This analysis can expose different opportunities, which is ideal when tackling legacy products or building an AM spare parts program.”
The software enables thousands of parts from 2D drawings to be uploaded at once. Once the parts are uploaded, it automatically extracts PMI (product manufacturing information) out of the PDF files of 2D drawings and calculates the parts’ size, volume, complexity etc., based on dimensions from projected views. It then advises on the 3D printability of the parts, recommends the optimal technology and materials, and performs a financial analysis of how additive manufacturing compares to traditional manufacturing. It exports useful information and insights, both as a formal PDF report and a raw data excel sheet.
Once the files are in a 3D format the software can also offer guidance for re-designing parts for additive manufacturing (DfAM) - looking at such opportunities as part consolidation and weight reduction - and enable a quick assessment of a part’s likelihood for failure, using Finite Elements Analysis tailored to additive manufacturing.