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Geometric Limited, a leader in Product Lifecycle Management (PLM), Engineering Services and Offshore Product Development (OPD) solutions and technologies, today announced the release of Version 2.2 of DFMPro for Pro/ENGINEER that supports Wildfire 5.0 and Windows 7.
DFMPro is a novel, automated design for manufacturing (DFM) review tool, that facilitates upstream manufacturability validation and identification of areas in design that are difficult, expensive or impossible to manufacture. It allows quick and in-depth examination of product manufacturability through advanced design rules for manufacturing processes like milling, drilling, turning, sheet metal fabrication, and injection molding.
Version 2.2 of DFMPro for Pro/ENGINEER improves the scope and ease of automated manufacturability analysis with the following new features:
- The new Injection Molding rules include checks for adequate hole spacing, appropriate drafts and fillet radii for ribs, boss height, and OD/ID ratio, among others. These have been derived from well-known industry-accepted best practices.
- A new functionality, to export and import analysis results, allows users to save the DFMPro analysis, and view it at their convenience; and also to share the analysis results with other DFMPro users.
- The batch mode of DFMPro includes support to generate the exported results – An automated system directly emails the results for further analysis and action. Users can directly view the results without re-executing DFMPro analysis.
- Advanced report generation functionality with 3D interactivity - This report can be shared with other users for further review and additional collaboration.
- Facilitates early detection and rectification of design problems affecting manufacturing
- Automates the manufacturability review process
- Allows easier and faster collaboration on manufacturability of designs
- Reduces repetitive design iterations and ‘Engineering Change’ requests by taking into account manufacturing considerations during design
- Enables manufacturability knowledge capture and reuse with its scalable framework
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