Dynamic Modeling and Monitoring of Contour Crafting—An Extrusion-Based Layered Manufacturing Process

[+] Author and Article Information
Satish Bukkapatnam

School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OKsatish.t.bukkapatnam@okstate.edu

Ben Clark

 Pratt and Whitney Rockeydyne, Inc, Canoga Park, CA

There is a match if, for example, the frequency with nth largest magnitude (n=1,2,,100) in the signal as well as the model output is the same.

The model outputs contain only x vibrations (for X-accel) and y vibrations (for Y-accel).

J. Manuf. Sci. Eng 129(1), 135-142 (Aug 16, 2006) (8 pages) doi:10.1115/1.2375137 History: Received June 06, 2003; Revised August 16, 2006

Layered manufacturing (LM) processes have emerged as legitimate processes for manufacturing various precision microelectronic components and bio-implants. These processes are also being considered for fabricating large customized free forms like buildings, statues, reactor beds, and car bodies. Many of these applications demand high levels of quality (e.g., Ra<0.1μm) and functional performance. Among the LM processes, extrusion-based processes can potentially offer high production rates together with lower setup and operating costs. Yet process failures resulting from anomalies, such as nozzle clogging, overflow, dynamic instabilities, bambooing, and machine degradation impede a widespread applicability of these processes. Scientific principles that relate the sources of these anomalies to process dynamics seem necessary for effective quality monitoring. In this paper we present a nonlinear lumped-mass model to capture dynamics underlying contour crafting, which is an extrusion-based LM process. The two degrees-of-freedom model, developed based on experimental characterizations, captures salient features of the process dynamics including the prominent manifestations of process nonlinearity. Experimental investigations show that the model can lead to effective monitoring of process conditions including overflow and underflow of material from extrusion nozzle, as well as suboptimal (fast and slow) feed rates of the extrusion head.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 1

Schematic of material flow in CC

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Figure 2

Representative “phase portrait” capturing vibrations along the X and Y directions

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Figure 3

Schematic of the lumped mass process model

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Figure 5

Dynamics of filament feeding

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Figure 9

Comparison of frequency (magnitude) plots of the model solutions with those of the measured sensor signals under near-optimal operating conditions

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Figure 10

Convergence patterns for neural networks trained using X-accel and Y-accel data

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Figure 4

Kinematics of material flow and deposition in CC

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Figure 6

CC machine used for fabricating components from polymer/polymer matrix materials

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Figure 7

Schematic of the experimentation setup

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Figure 8

Comparison of time portraits of the model solutions with those of the measured sensor signals under near-optimal operating conditions



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