52 ISE Magazine | www.iise.org/ISEmagazine
Inside IISE Journals
Enhancing the grain storage
management capability by
harnessing the sensor data
Grain storage is a critical issue for the
national economies of a majority of de-
veloping countries. The quality of grains
will decrease if grains are not properly
preserved in granaries. Thermal man-
agement plays an essential role in grain
storage because temperature is among
the key factors that may directly influ-
ence the quality of stored grains.
The thermal field of a whole granary
is of great importance in grain thermal
management. The accurate acquisition of
the whole thermal field information gen-
erates a meaningful index for grain qual-
ity surveillance and storage maintenance.
Therefore, a timely and effective thermal
field estimation approach is necessary for
grain storage.
Doctoral student Di Wang and profes-
sor Xi Zhang from Peking University
and professor Kaibo Liu from the Uni-
versity of Wisconsin-Madison developed
an online thermal field estimation ap-
proach in grain storage in their article,
“Modeling of a Three-Dimensional Dy-
namic Thermal Field Under Grid-Based
Sensor Networks in Grain Storage.” This
article combines a 3D nonlinear physical
dynamics model with a stochastic spatio-
temporal model to accurately obtain a
dynamic thermal field during grain stor-
age by using the sensor data collected in
the granary.
The technique in this article has been
well applied in several national grain de-
pots in China and has achieved satisfac-
tory thermal field estimation results. This
technique provides guidance for the de-
sign of engineering structures to conserve
energy and reduce preservation costs. For
example, an accurate estimation of the
grain thermal field can help optimize the
layout and enact on-and-off strategies of
sensor networks in granaries. This tech-
nique has been honored in “Grain Sci-
ence and Technology Achievements of
China” in 2017 and 2018.
CONTACT: Xi Zhang; xi.zhang@pku.edu.cn; +
86-010-82524911; Department of Industrial Engi-
neering & Management, Peking University, Beijing
100871, China
Enabling ‘lab-to-fab’
transition of metal-based
additive manufacturing
Laser-based additive manufacturing
(LBAM) is a disruptive technology that
can greatly enhance future capabilities
for the commercial and defense indus-
tries. One major challenge of imple-
menting LBAM is that parts must often
meet very stringent requirements for
qualification and certication, requiring
This month we highlight two articles from IISE Transactions. The first article studies the sensing system design and
estimation to provide a thermal field of a whole granary for grain quality surveillance and storage maintenance. The
developed technique has been implemented in several national grain depots with great successes. The second article
develops machine learning methods for facilitating the laboratory to fabrication (lab-to-fab) transition of additive
manufacturing technology. The developed method provides the capability of using in-situ sensor data to predict defects in
the AM part, which speed up the transitioning “laboratory” research to component-level “fabrication.” These articles will
appear in the May 2019 issue of IISE Transactions (Volume 51, No. 5).
Doctoral student Di Wang from Peking University in Beijing stands in an empty grain
warehouse before sensor layout initiation.
April 2019 | ISE Magazine 53
long lead times for introducing AM parts
into mission-critical applications in de-
fense. The unique benefits of rapid build
time and unique microstructural control
offered by AM processes cannot be fully
realized without real-time process tech-
nologies that accelerate performance-
based qualication and certification.
Mississippi State University research-
ers collaborated with the U.S. Army
Research Laboratory (ARL) to develop
machine learning methods for facilitat-
ing the laboratory to fabrication (lab-to-
fab) transition of AM technology. This
ongoing collaboration utilizes LBAM
to customize lightweight, complex parts
while maintaining mechanical proper-
ties and quality. The LBAM process
provides the capability to tailor the ap-
plication and reduce weight to ensure
the resilient platforms and systems that
meet the Army’s modernization needs.
This interdisciplinary team included
Ph.D. student Mojtaba Khanzadeh,
former Mississippi State student Su-
dipta Chowdhury, professors Haley R.
Doude, Mohammad Marufuzzaman
and Linkan Bian, all from the Bagley
College of Engineering at Mississippi
State University, and ARL scientist
Mark Tschopp, now regional lead of
ARL Central. In their research paper,
“In-Situ Monitoring of Melt Pool Imag-
es for Porosity Prediction in Laser-based
Additive Manufacturing Processes,” the
team developed a new machine-learn-
ing method that used sensor data (melt
pool thermal image streams from ex-
tensive experimental studies) to predict
defects in the resultant part (locations
of porosity measured via CT scan). The
study demonstrates the efficacy of real-
time process monitoring for defects in
The success of this research signi-
cantly contributes to real-time process
monitoring and control technologies,
which will improve the quality, pro-
ductivity and cost reduction required
for wide adoption of metal AM pro-
cesses. This critical study has wide-
reaching implications for accelerating
the selection and optimization of build
parameters and developing a better fun-
damental understanding of the interac-
tion of material and process on LBAM
component properties. Both are neces-
sary components for reaching the goal
of “lab-to-fab,” efficiently transitioning
laboratory” research to component-
level “fabrication” in industry.
CONTACT: Linkan Bian; bian@ise.msstate.edu;
(662) 325-0570; Mississippi State University, 260
McCain Building, Mississippi State University,
MS 39762.
A dynamic target volatility
strategy for asset allocation
Since the collapse of Lehman Brothers
in 2008, it has become increasingly im-
portant for investors to manage financial
risk through tail risk hedging. This is
especially true of institutional inves-
tors with long-term investments such
as funds and insurance companies who
have established a risk-control strategy
known as “target volatility” to maintain
a predetermined level of volatility for a
The target volatility strategy system-
atically adjusts the investment propor-
tion of the portfolio between a risky and
a risk-free asset to maintain a desired
level of target volatility. Accurately fore-
casting the volatility of the risky asset
return is a critical task when implement-
ing a target volatility strategy.
In “A Dynamic Target Volatil-
ity Strategy for Asset Allocation using
Artificial Neural Networks,” authors
Youngmin Kim from Soonchunhy-
ang University and David Enke from
the Missouri University of Science and
Technology developed an intelligent
target volatility strategy for asset alloca-
tion. The strategy provides a volatility
forecast using artificial neural networks,
based in part on a stability-oriented ap-
proach (SOA), while also incorporating
target volatility.
The model is unique in that it ap-
plies an SOA based on statistical tests to
compare data from the current period to
a past set of data from a stable period.
This research summary highlights
an article from Volume 63, No.
4, of The Engineering Economist.
Many investors manage risk by
attempting to maintain a constant
target level of volatility in their
portfolios. In this article, the
authors use an artificial neural
network to forecast the volatility of a
risky asset based on historical data
and equivalent futures contracts.
Mississippi State University researchers involved in the “lab to fab” project for additive
manufacturing included (from left) Ph.D. student Mojtaba Khanzadeh and professors
Mohammad Marufuzzaman, Haley R. Doude and Linkan Bian.
Mark Tschopp Sudipta Chowdhury
54 ISE Magazine | www.iise.org/ISEmagazine
This provides a higher level of reliability
due to a more abundant source of stable
volatility data compared to traditional
approaches that often model on a smaller
amount of unstable drawdown data.
In addition to applying intelligent
classification and forecasting techniques,
according to the market situation as ob-
tained from a SOA and a volatility index,
equivalent futures contracts are used
in the model instead of the underlying
portfolio assets. This approach is more
practical and cost-effective for managers
and investors compared to conventional
models for asset allocation.
Working together, the model acts as a
risk-management technique for achiev-
ing long-term investment and capital
allocation objectives. It provides bet-
ter portfolio performance compared to
benchmark strategies in terms of both
the downside risk of the portfolio (i.e.,
fat-tailed distributions) and a common
risk-adjusted measure of return (i.e.,
Sharpe ratio).
In the end, the hope is that intelligent
and dynamic risk models can help inves-
tors receive expected long-term returns,
even as the volatility of individual assets
and the markets change over time.
CONTACT: David Enke; enke@mst.edu; 221 Engi-
neering Management, Missouri University of Sci-
ence and Technology, Rolla, MO 65409.
Jianjun (Jan) Shi is the Carolyn J. Stewart
Chair and professor in the H. Milton Stewart
School of Industrial and Systems Engineer-
ing at the Georgia Institute of Technology. He
is editor-in-chief of IISE Transactions, an
academician of the International Academy for
Quality and a fellow of IISE, ASME and
Sarah M. Ryan is the Joseph Walkup Profes-
sor of Industrial and Manufacturing Systems
Engineering at Iowa State University. She is
editor-in-chief of The Engineering Economist
and a fellow of IISE.
My fascination
with industrial
engineering comes from
that never-ending search
for optimization and
efficacy. We don’t only
search for solutions to benefit manufacturing,
we also facilitate and improve people’s lives by
making products more affordable. We can make
basic daily tools readily available to improve the
quality of products and services. Reducing waste
and inefficiency also saves people time, which
they can dedicate to their close ones, family
and themselves.
Give Back
to the Future
of the Profession
Donate to the
IISE Scholarship Fund
The IISE Scholarship Fund recognizes industrial
and systems engineering students’ academic
excellence and campus leadership. Last year,
IISE awarded more than $90,000 in scholar-
ships. Eenne Bausta is pursuing his B.S. in
Industrial Engineering with a minor in Systems
Engineering at ITESM – Queretaro in Mexico.
He won IISE’s Dwight D. Gardner Scholarship.
Visit www.iise.org/PlannedGiving to make a
donaon today.
IISE Transactions is IISE’s flagship research
journal and is published monthly. It aims
to foster exchange among researchers and
practitioners in the industrial engineering
community by publishing papers that are
grounded in science and mathematics and
motivated by engineering applications.
The Engineering Economist is a quarterly
refereed journal published jointly by IISE
and the American Society of Engineering
Education. Devoted to issues of capital
investment, its topics include economic
decision analysis, capital investment analysis,
research and development decisions, cost
estimating and accounting, and public policy
To subscribe, call (800) 494-0460 or (770)
About the journals
Youngmin Kim David Enke