advances in numerical modeling of manufacturing processes

TRANS. INDIAN INST. MET., VOL. 57, NO. 4, AUGUST 2004
It was found that both single and double reduction
dies follow almost similar paths for circumferential
stress, since the nature of the deformation is similar.
Also the axial stress paths are almost similar for the
first reduction and emergence from the first land.
Since the percentage reduction is lesser at the first
stage of the double reduction die as compared to the
single reduction die, there are some tensile axial
stresses at the center in the case of the former.
Industrial trials with the new double reduction die
design eliminated the cracks in the typical trial of
10,000 parts. This validated the design predictions.
7. CONCLUSIONS
Advances in numerical techniques along with accurate
modeling of material behavior have made it possible
to analyze and optimize deformation and solidification
processes for significant improvement in process
quality and productivity. Recently, these techniques
have been augmented by AI tools such as fuzzy
reasoning and ANNs that enable incorporation of
domain knowledge. In addition, SPC and robust
design techniques have been integrated to reduce
variability and to improve product properties. The
collaborative industrial-governmental-academic
research being conducted at the Manufacturing
Research Group, The Ohio State University has
resulted in significant cost reduction, and quality and
productivity improvements for the participating
industry. The cases presented in this paper are only
a few examples of the application of these advanced
theoretical techniques to the steel mills and the
aerospace and automotive manufacturers (die casting
and forging). The goal of this paper was to
demonstrate the few successes of this collaborative
effort.
ACKNOWLEDGMENTS
The support provided by the member companies
(Timken, Inland steel and Chaparral steel) of the
Consortium for the Advancement of Rolling
Technology, by the Volvo Car Company, by the
Sikorsky Aircraft Corporation, the Metaldyne
Corporation and by the Dynamic Systems Incorporated
(DSI) is gratefully acknowledged. The author also
wishes to acknowledge the support from the
Department of Energy and the UES, Inc; from SFTC,
Columbus OH, for providing FEA software DE-
FORM_2D TM ; and from TechSolve, Cincinnati OH
for providing help with experiments for the machining
research. The author finally expresses his appreciation
to Praveen Pauskar, Jiang Hua, Shivakumar Kannan,
Venkat Sankararaman, Ashish Pabalkar, Peeyush
Mittal and Satish Kini, and the former and current
research associates at The Ohio State University, for
their hard work and efforts.
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