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Virtual Manufacturing and Process Optimization

by Neeraj Panhalkar last modified 2015-03-04 18:59

Advanced Manufacturing research in CGDM is focused on the development of computational algorithms/methods for modeling machine and process errors and determining optimal process parameters by simulating virtual models of manufacturing processes including traditional subtractive process (turning, milling, etc.), additive processes (Additive Manufacturing (AM)/3D Printing), casting and sheet metal operations. The main objectives associated with this research are improving the efficiency of manufacturing processes in three core areas: a) improved part accuracy and quality, b) optimal material utilization and c) reduced energy expenditure (sustainable manufacturing)

Additive Manufacturing (AM)

Current AM research in CGDM focuses on the development of computational algorithms for improving the efficiency of AM processes in three core areas: a) part accuracy, b) material utilization and c) energy expenditure. Some of the pertinent research in these areas is listed below.

Geometric Dimensioning and Tolerancing (GD&T) Errors in AM Parts:

The correlation between the AM process parameters and GD&T errors in AM parts have been estimated using virtual modeling and simulation methods based on first principles and mathematical approaches. The correlation between process parameters and part errors are also used for calculating the optimal process parameters which would minimize the part errors and also satisfy the GD&T callouts.

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STL File Modification and Development of Alternate File Formats:

Prof. Anand's team is also looking into ways of developing methods to modify the existing Stereolithography (STL) format for minimizing part errors while reducing the number of STL facets. They are also investigating methods for creating alternate file formats using curved Bezier patches.

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Adaptive Slicing and Slice Modification Algorithms:

Another area of research currently being performed in AM is the application of 2D and 3D data structure based algorithms for adaptive slicing and for intelligent modification of 2D slice contours for reducing part errors.

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Material Utilization in AM Processes:

A voxel-based approach has been developed to calculate the volume of supports in AM processes and to correlate this volume to process parameters. Efforts to visualize the correlation between supports and part orientations are also being investigated.

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Calculation of Process Energy in AM Processes:

If AM technology has to be accepted as a mainstream manufacturing process, the energy footprint associated with AM processes has to be minimized. With this goal, Dr. Anand and his team is currently developing a first principles based discrete methodology for calculating the process energy in AM processes and will use this methodology to minimize the energy expenditure in AM processes.

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Analysis of manufacturing processes and process capabilities using deterministic (Computational Geometry, 3D Data Structures, Visibility Graphs, etc) and statistical tools (ANOVA, regression, etc) is a primary focus of the research efforts undertaken by Dr. Anand. Manufacturing processes analyzed in CGDM include turning, milling, casting, CNC cutting, Rapid Prototyping (RP)/Additive Manufacturing (AM) etc.