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Manufacturing Automation Research
Laboratory
© 2003-2008
Director:
Tugrul Özel, Ph.D.
Associate Professor
Industrial & Systems Engg.
Rutgers
University
96
Frelinghuysen Road, Piscataway, NJ 08854, USA
(732) 445-1099
Fax: (732) 445-5467
ozel@rutgers.edu
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Project:
International
Research Exchange For Biomedical Devices Design, Prototyping and Manufacturing
Duration:
2009-2012
Sponsor:
European Commission
Research Directorate-General, FP7-PEOPLE-2009-IRSES
Lead:
J.
Ciurana- Univ. Girona, Spain (PI)
Partners:
E. Ceretti- Univ. Bresca, Italy (co-PI), P. Bartolo- Inst. Polytech. Leiria,
Portugal
(co-PI),
C. Rodriguez- Tech Monterrey, Mexico (co-PI), J.V. Lopes De Silva CTIR,
Brazil (co-PI),
T. Özel
(co-PI)
Supporters: TBA
Summary: During the last five to ten years, the healthcare sector is
increasing its research activity, not only because final terminal cases are
looking for solutions, but also diagnosis, treatment and life-long care are
becoming ways to reduce costs of health care. Also, doctors and patients are
constantly looking for better technologies in products such as orthopedic
prosthesis, diagnostics, surgery instrumentation, drug dosing and delivery
systems. In the meantime other maturing manufacturing systems with high level of
knowledge (e.g. automotive manufacturing field) have been decreasing its
research activity and well-developed technological advancements and solutions
are not exploited as much as they can be in other fields.
The main objective is to create and reinforce synergies between applied
investigation fields of engineering and medicine in order to develop new
solutions for the healthcare sector. The project will help in creating a high
level expertise and understanding on Design, Prototyping and Manufacture for
Medical Devices using computing tools, biomaterials and other biocompatible
engineering materials.
The expected result of the project is to develop solutions for the sector of
biomedicine (new medical devices) and bioengineering such as, development of
medical devices which can slowly dose the medicine, improve existing
developments, develop artificial joint replacements and orthopedic prosthesis,
new optimized geometries and coatings for some invasive medical tools such as
needles, scalpels and surgical tools and fabrication of functional graded
scaffolds for tissue engineering.
Project:
Improving
Machinability of Titanium Alloys using Physics-Based Simulation
Modeling
Duration:
2008-2011
Sponsor:
National
Science Foundation
Lead:
Dr. Tugrul
Özel (PI)
Partners:
Dr. A.
Srivastava (TechSolve)
Supporters: Conicity,
Pennsylvania, Penn State ARL, Pennsylvania, GE Aviation, Ohio
Summary: The
objectives of this collaborative research award are to understand the
physical and thermal aspects of the material removal process in machining
titanium alloys and to improve machinability using novel cutting tools
with variable micro-geometry design and nano-layered self lubricating
coatings. The research approach would be to establish a methodology for
physics-based prediction of cutting forces, temperatures, stresses and
cyclical serrated chip formation in machining titanium alloys. Friction
and heat flow at tool-chip-workpiece interfaces for various tool edge
micro-geometry and nano-layered coatings will be identified by conducting
cutting tests. Wear rate models that relate predicted process variables
and contact conditions to tool wear under realistic machining conditions
will be developed. These models will be utilized in determining optimum
form and thickness of coating layers that will be applied on the variable
micro-geometry cutting tools. These advanced coatings will be deposited
using the electron beam physical vapor deposition process. Experimental
testing will be performed in industrial scale test-beds to validate
improved machinability and tool life at high speed machining regimes. It is expected that this advanced cutting
tool design and simulation capability would reduce the cost in product
design and development and improve productivity in aerospace, automotive,
military, chemical and medical device industry where titanium alloys are
utilized.
Project:
Investigations
on Influence of Machining-Induced Strain, Stress and Temperature Fields on
White-Etch Layer Formation in IN-100 Super Alloy
Duration:
2007
Team:
Dr.Tugrul Özel (PI)
Students: Mike Pandalfo, Adam
Miller
Sponsor: United
Technologies Research Center
Summary: Nickel-base super alloys are typically available
wrought, forged, cast and in sintered (powder metallur gy) forms and often
used in mission critical components such as in aircraft/industrial gas
turbine engines. Machinability of the nickel-alloys is generally rated as
extremely difficult. Main difficulty in machining of nickel-alloy arises
due to the high toughness and work hardening behavior of these alloys.
In
this project, Finite Element (FE) simulation of machining of powder-based
IN100 super alloy in the presence of tool flank wear is studied by using
Arbitrary Lagrangian Eulerian explicit and updated Lagrangian implicit
methods. Both simulation methods are capable of simulating plastic flow
around a round cutting tool edge without using chip separation criteria.
Johnson-Cook work material model is used for elastic-plastic work
deformations.
The simulation results include predictions of chip
formation, plastic strain, temperatures and tool stress distributions in
cases of cutting with fresh and worn cutting tool. These results are
highly essential in predicting machining induced surface alterations such
as white etched layer formation that are detrimental to fatigue life of
super alloy components.
Project:
Micro-Milling of Dies and Molds
Duration:
2006-2008
Team:
Atul Dhanorker and Dr.
Tugrul Ozel
Collaborators:
Dr. Xinyu Liu, Lamar University
Supporters:
Microlution Inc.
Summary: In this
project, a model-based micro-end milling process planning guideline for
machining micro mold cavities is proposed. The goal is to facilitate
proper selections of the process parameters. Specifically, the axial depth
of cut, the feed per tooth are critical in achieving performance
objectives in terms of cutting forces, surface accuracy, tool life, etc.
Analytical model to provide an understanding of the micro-machining with
minimum chip thickness and edge radius effects, mechanistic time-domain
simulation modeling to provide predictive capability in practical
machining performance, such as cutting forces, tool vibrations, surface
accuracy, and surface roughness are utilized.
The generalized process
planning strategy consists of two steps: roughing and finishing. In
roughing, the objective is to control the cutting force within a
predefined threshold to prevent premature tool breakage and to maximize
the material removal rate.
In finishing, the primary objective is to
control the form error within the tolerance and to obtain satisfactory
surface roughness. The proposed process planning strategy was applied for
micro-milling of array of micro-channels for micro-embossing and mold
cavities for micro-molding.
Project:
Laser
Assisted Materials Processing and Micro-Milling Processes
Team:
Atul Dhanorker and Dr.
Tugrul Ozel
Duration:
2005-2006
Collaborators: Dr.
Zhixiong Guo and Dr. Yogesh Jaluria, Mechanical and Aerospace Engineering,
Rutgers,
Dr. Frank Pfefferkorn, University of Wisconsin-Madison
Sponsor:
Rutgers University
Research Council Grants
Summary: The demand for manufacturing of very small products in metals,
polymers and ceramics for the applications various industries has been
increasing. Unlike well-developed manufacturing technologies to produce
micro/nano scale silicon products, technologies for mass micro-manufacturing of
products in non-silicon materials are still under development and presents a new
paradigm of production and manufacturing challenges. We mainly aim at
researching the integration of two different processes; laser thermal processing
and meso/micro
scale end milling, in the same length
scale so that high accuracy and high precision devices can be fabricated. The
goals of this study include: (1) establishing the scientific foundation of laser
assisted thermal processing and micro-end milling as a rapid manufacturing
process at micro and nano length
scales, (2) design and implementation of laser assisted micro manufacturing system for applications requiring high precision/accuracy, and (3) modeling and
optimization of these processes in order to achieve cost effective - high
precision manufacturing.
Project:
Investigations on the Effects of Advanced Edge
Design in PCBN cutting tools for Hard Turning
Duration:
2006-2007
Team:
Yigit Karpat and Dr. Tugrul Ozel
Sponsor: Conicity
Technologies - Weiler Corporation
Summary: In this
project, we study micro-geometry edge design effects on the Polycrystalline
Cubic Boron Nitrite (PCBN) cutting tool performance in hard turning. We
conducted orthogonal cutting tests using chamfered, honed, and waterfall honed
various PCBN inserts in machining of AISI 4340 alloy steel. We develop slip-line
field analysis based analytical models to predict friction factor at tool-chip
interface using measured forces and chip dimensions. We utilized Finite Element
simulations to predict field variables such as forces, temperatures, and
stresses in the chip, the tool and the workpiece. We also develop tool wear rate
models to predict the influence of micro-geometry on tool wear behavior of
the PCBN inserts.
Project:
Precision
Micromachining using Pulsed Lasers
Duration:
2005-2006
Team:
Wen-Hui
Lee and Dr.
Tugrul Ozel
Sponsor: Rutgers University
Research Council Grants
Summary: Laser micromachining has the capability to fabricate very small and basic 2.5
D geometric features on a range of materials in the form of laser ablation or
irradiation. Short pulsed lasers that can achieve wide range of wavelengths in
the form of harmonics of infrared laser beam at 1064 nm wavelength have been a
very effective micro-machining tool used for hole drilling, cutting, scribing,
trimming and marking. Direct laser ablation can be performed by controlling
laser beam properties such as laser energy, intensity, pulse duration and
wavelength in micro-machining 3-D geometric features.
Project: Predictive Modeling
and Optimization of Machining Parameters in Hard
Turning
Duration:
2005-2007
Team:
Yigit Karpat
and Dr. Tugrul Ozel
Sponsor: Conicity Technologies, Weiler Corporation
Summary: In this study, we study the hard turning process that is a
beneficial practice to increase quality and reduce cost and lead-time for
machining steel components. We develop workpiece material model based
analytical models to predict field variables such as forces, temperatures, and
stresses in the chip, the tool and the workpiece. We also develop tool wear rate
models based on predicted field variables. The quality of the finish-machined
surfaces is highly dependent upon the tool wear. We aim to validate the tool
wear models with the experiments and conduct multi-objective optimization
studies using particle swarm intelligence to attain most desirable surface
finishes with prolonged tool life and improved productivity in the hard turning
processes.
Project:
Monitoring and Modeling of
Tool Wear and Surface Finish in Hard Part Machining Using Neural Networks
Duration:
2003-2005
Team:
Yigit Karpat and Dr.
Tugrul Ozel
Sponsor: Rutgers
University, Shape-Master Tools, Timken Company
Summary: In this project, we utilize neural network modeling to
monitor, model and predict surface roughness and tool flank wear over the
machining time for variety of cutting conditions in hard turning. Regressions
models are also developed in order to capture process specific parameters. The
data sets from measured surface roughness and tool flank wear were employed to
train the neural network models. Trained neural network models are used in
predicting surface roughness and tool flank wear for other cutting conditions. A
comparison of neural network models with regression models is also carried out.
Predictive neural network models are found to be capable of better predictions
for surface roughness and tool flank wear within the range that they had been
trained. We use evolutionary computational algorithms such as particle swarm
intelligence for optimization of machining parameters by utilizing predictive
neural network models developed. Currently, predictive neural network modeling
is being extended to dynamically monitor and predict tool wear modes and
patterns seen in finish hard turning processes.
Project:
Computational
Modeling of Machining Induced Stresses and Surface Properties in High Speed Machining
Duration:
2004-2006
Team:
Erol
Zeren and Dr. Tugrul Ozel
Sponsor: Rutgers University
Research Council Grants
Supporters:
McWilliams Forge Co
Summary: The main objective of this research is to establish a
computational modeling
framework for high speed machining in order to be able to investigate
effects of field variables on the integrity and quality of the machined
workpiece. We aim to investigate the formation of residual stresses,
micro-hardness and microstructure in the high-speed machined workpiece surfaces.
We use commercially available Finite Element Analysis software (ABAQUS/
Explicit) to simulate
machining process and apply Arbitrary
Eulerian Lagrangian with dynamic explicit solutions in conjunction
with adaptive meshing to simulate continuous, serrated or
shear localized chip formation. The cutting tool models always have round and/or
chamfered edges to represent realistic conditions. The ultimate goal is to
accurately compute temperature, strain, strain rate and residual stress fields
in high speed machining using computational models and to predict
the resultant surface properties. We
apply this modeling framework to high speed machining of alloy steels, aluminum and
titanium alloys.
Project:
Development of Workpiece Material and
Friction Models for Simulation of Machining of Aerospace Alloys
Duration:
2002-2003
Team:
Erol Zeren and Dr. Tugrul Ozel
Sponsor: New Jersey Space
Grant Consortium
Supporters:
Third Wave Systems Inc.
Summary: This study is an initiative to characterize workpiece material
and friction properties in dry machining of aerospace alloys. Machining of
difficult-to-cut aerospace alloys and materials is a challenging process and
desired part quality (surface finish and tolerance) requirements are tough to
achieve with conventional machinery due to process stability
problems associated
with rigidity, vibrations and wear behavior of cutting tools. Finite Element
simulations of machining processes have the potential to reduce experimentation,
optimize cutting parameters, and improve overall machinability and part quality
for aerospace machining applications. One of the key elements in success of such
simulation models is reliable workpiece material and friction data. This
research study aims to develop novel, robust and reliable workpiece material and
friction models and data for enhancing performance and reliability of Finite
Element simulations.
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