Featured Research

This page shows featured research performed in PSEL.

전기차 모듈러아키텍처 인터페이스 개념화 및 지수화 방법론 개발

Funded by Hyundai Motor Group, 2025.04~2025.08 (PI)

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Funded by the National Research Foundation of Korea (Young Scientist Grants_ Outstanding Track, #NRF-2022R1C1C1012140), 2022.03~2025.02 (PI)

적정 플랫폼 도출방안 및 Modular Design 애로기술 연구

Funded by LG Electronics, 2024.04~2024.11 (PI)

파킨슨병 조기진단 및 예측을 위한 뇌영상 및 생체역학 기반 AI모델 개발 연구

한국연구재단 기초연구실 (#RS-2023-00222406, 2022.03~2025.02) (참여)

Development of an intelligent generative design framework for additive manufacturing

Funded by the National Research Foundation of Korea (Young Scientist Grants_ Outstanding Track, #NRF-2022R1C1C1012140 or RS-2022-NR072150), 2022.03~2025.02 (PI)

Characterization of Power Demand and energy consumption for fused filament fabrication using cfr-peek

Purpose - This study models power demand and energy consumption of fused filament fabrication (FFF) for carbon fiber reinforced polyether-ether-ketone (CFR-PEEK) based on a material addition rate (MAR), which is affected by process parameter changes in an FFF machine. Moreover, a virtual AM plant handling multiple FFF machines and part designs is simulated to compare the energy and production dynamics of operational strategies that treat orders differently based on their inherent MAR.
Design/methodology/approach - A full-factorial design of experiments considering major FFF parameters (i.e., layer thickness and printing speed) is planned to fabricate CFR-PEEK samples for each process combination. Then, the MAR of each process parameter combination is calculated to derive regression models for average power demand and total energy consumption. Furthermore, a discrete-event simulation model for a virtual AM system of aircraft parts is built to analyze changes in power demand and energy consumption along with order lead time and production volume under three operational strategies (i.e., higher MAR first-out, first-in-first-out, and lower MAR first-out).
Findings - The MAR of FFF for CFR-PEEK plays a key role in energy dynamics in which a decrease in energy consumption dominates over an increase in power demand as the MAR increases. Furthermore, preferentially processing parts with a higher MAR in the AM system is the most beneficial strategy in both energy consumption and productivity.
Originality – The findings from this study show that the energy performance of CFR-PEEK applications in FFF should be understood with the MAR of an AM system since the impact of AM complexity on energy performance can be operationally controlled by managing the MAR of part orders for the entire AM system.

Funded by Incheon National University Research Grant in 2021, "Characterization of Power Demand and Energy Consumption for Fused Filament Fabrication Using CFR-PEEK," 2021.5~2022.4 (PI)

Development of an Intelligent Recommender System for Baby Sitter Education through Deep Learning

Funded by TIPA (the Korea Technology and Information Promotion Agency for SMEs) (#G21S297338101), 2020.12~2022.12
Principal Investigator: Mom-Sitter (주식회사 맘편한세상, www.mom-sitter.com )

Selection of infill parameters considering operational and mechanical performance in additive manufacturing for CFR-PEEK

As high-performance polymers in mechanical properties is available for Fused Filament Fabrication (FFF), decision making for infill parameter settings becomes more essential to achieve both operational and mechanical performance of printed outputs. However, existing studies have investigated the effects of infill parameters mainly on mechanical performance for common polymers without identifying appropriate infill settings under trade-offs between operational and mechanical performance. Focusing on Carbon Fiber Reinforced Poly-Ether-Ether-Ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance for the high-performance material but also to derive appropriate infill settings through a multi-criteria decision-making process considering the conflicting effects.

Funded by Incheon National University (International Cooperative Research Grant) in 2020, "Selection of infill parameters considering their effects on operational and mechanical performance in additive manufacturing using CFR-PEEK," 2020.5~2021.4 (PI)
Collaborated with Louisiana State University (Dr. Hyun Woo Jeon)

Hybrid truck-drone Delivery Routing Model for Last Mile Logistics in Urban Areas

In this project, we propose a novel mathematical model as a mixed integer linear program (MILP) for efficiently coping with a complex last-miledelivery procedure under uncertainties of the drones. We extend the traditional vehicle routing models to the hybrid last-mile delivery systems bytaking into account two important practical issues: the effect of parcel weight on drone energy consumption and restricted flying areas. The proposed models are mainly aimed at optimal routing and scheduling of both UAVs (Unmanned Aerial Vehicles) and delivery trucks in the hybrid truck-drone last-mile delivery systems. We have assumptions as follows: (1) drones are launched and retrieved from the delivery truck at only a depot or a customer location; (2) the battery of UAVs is charged while attaching to the delivery truck but the charging time is a random variable depending on several factors; (3) drones fly at a steady speed, and the fluid density of air and gravity are assumed to be fixed; and (4) the shape of a no-fly zone is a circle, and the zones do not overlap. We propose a stochastic network flow model to address an urban last-mile logistics model using the hybrid truck-drone delivery systems under uncertainty in the drone operations. In particular, we aim to incorporate the effect of uncertainties of drone operations to partially explain the unpredictable delivery time and the availability of drones for the last-mile logistics by using a joint chanceconstrained program (JCCP). To solve a proposed model efficiently and to reduce the number of scenarios to a manageable level effectively, the sample average approximation (SAA) approach and Monte Carlo sampling techniques are discussed as parts of the solution procedure.

Funded by Incheon National University (International Cooperative Research Grant) in 2020, "Hybrid Truck-Drone Delivery Routing Model for Last Mile Logistics in Urban Areas," 2020.5~2021.4 (PI)
Collaborated with South Dakota School of Mines and Technology (Dr. Hyeong suk Na)

Text mining for characterization of collectivistic leadership

This study aims to clarify underlying characteristics of collectivistic leadership in situational and behavioral contexts. Beyond prevailing top-down approaches focusing on limited literature reviews, this study employs a bottom-up approach using text mining for a comprehensive set of the existing literature in collectivistic leadership to extract inherent key features and contextual similarities. This enables to effectively characterize collectivistic leadership based on objective text information and thereby to clarify current redundant and ambiguous categories in collectivistic leadership

Funded by Incheon National University (International Cooperative Research Grant) in 2019, "Text mining-based analysis for underlying characteristics of collectivistic leadership approaches," 2019.5~2020.4 (PI)
Collaborated with Penn State Altoona (Dr. Jong Gyu Park)

Predictive Energy Consumption Model for Additive Manufacturing

Additive manufacturing relies on energy as an input resource to operate the process. For this reason, an additive manufacturing system is required to have efficient energy control during its fabrication process to minimize not only energy cost but also environmental impact (e.g., carbon footprint). Energy consumption in a 3D printing device based on fused filament fabrication is affected by selected process parameters. For example, build time and nozzle movement are affected by the combination of various process parameters, and they lead to variability in active power (Watt) during the fabrication process. This study employs statistical analysis and machine learning for a large amount of energy consumption data collected during 3D printing with different process parameter settings to better predict energy variability in additive manufacturing for efficient energy control.

Funded by the National Research Foundation of Korea (2018R1C1B5040256), "An Investigation on an Innovative Design Framework for Additive Manufacturing and Impacts of Variability Factors in Additive Manufacturing," 2018.3~2021.2 (PI)
Collaborated with Louisiana State University (Dr. Hyun Woo Jeon) and UNIST (Dr. Sunghoon Lim).

Impact of Additive Manufacturing Parameters for CFR-PEEK

Fused Filament Fabrication (FFF) has been proven to be an effective additive manufacturing technique for Carbon Fiber Reinforced Poly-Ether-Ether-Ketone (CFR-PEEK) due to its practicality in use. However, relationships between process parameters and their trade-offs in manufacturing performance have not been extensively studied yet although these factors are essential to identify the optimal parameter settings for CFR-PEEK fabrication. This study therefore investigates the impact of critical FFF parameters (i.e., layer thickness, build orientation, and printing speed) on the manufacturing performance (i.e., printing time, dimensional accuracy, and material cost) of CFR-PEEK outputs for different designs.

Funded by the National Research Foundation of Korea (2018R1C1B5040256), "An Investigation on an Innovative Design Framework for Additive Manufacturing and Impacts of Variability Factors in Additive Manufacturing," 2018.3~2021.2 (PI)
Collaborated with Louisiana State University (Dr. Hyun Woo Jeon)

Process Capability Analysis for Fused Filament Fabrication

The main objective of this study is to examine the capability of FFF for Polylactic Acid (PLA) from a dimensional accuracy point of view under optimal process parameter settings. The choice of PLA is supported by its wide range of applications in industry. A full-factorial experimental design is planned and performed to determine process parameter settings for minimum dimensional variations in length, height, and width. Since the optimal process settings for each dimension are different, the individual dimensional accuracy responses are converted into a single indicator and optimized simultaneously to minimize the overall dimensional inaccuracy. Then, process capability analysis is performed under the optimal parameter settings to examine the production tolerances of FFF.

Funded by the National Research Foundation of Korea (2018R1C1B5040256), "An Investigation on an Innovative Design Framework for Additive Manufacturing and Impacts of Variability Factors in Additive Manufacturing," 2018.3~2021.2 (PI)
Collaborated with Iowa State University (Dr. Gül E. Okudan Kremer)

Development of design for Additive Manufacturing Framework

This study develops a design framework for additive manufacturing through the integration of axiomatic design and theory of inventive problem-solving (TRIZ). This integrated approach is effective because the axiomatic design approach can be used to systematically define and analyze a design problem, while the TRIZ problem-solving approach combined with an additive manufacturing database can be used as an idea generation tool to generate innovative solutions for the design problem.

Funded by the National Research Foundation of Korea (2018R1C1B5040256), "An Investigation on an Innovative Design Framework for Additive Manufacturing and Impacts of Variability Factors in Additive Manufacturing," 2018.3~2021.2 (PI)
Collaborated with Iowa State University (Dr. Gül E. Okudan Kremer)

Characterization of Product Family evolution using Network Science

Although product family design has been widely discussed in the extant literature, inherent evolving properties in a product family design structure have not been sufficiently explored in an analytical manner. To tackle this issue, this research aims to characterize underlying properties in an evolving product family structure based on a network science approach. First, a product family structure is represented as a network to describe relationships among the components of a product family structure. Then, topological properties and patterns in a product family structure network at each time period are investigated through a case study using the smartphone models of a major company.

Funded by Incheon National University (International Cooperative Research Grant) in 2018, "An investigation on the network topology of an evolving product family structure and its robustness and complexity," 2018.5~2019.4 (PI)
Collaborated with Iowa State University (Dr. Gül E. Okudan Kremer)

<Two-phased Approach for Sustainable Supplier Section and Order Allocation>

sustainable supplier selection and order allocation

Existing supplier selection models also tend to focus on the formulation of a simplified supply chain structure rather than considering more realistic supply chain operations under multiple sourcing and product designs. To facilitate the complex decision-making process of global supplier selection problems, this study proposes an integrated approach that consists of two phases to effectively reflect the multi-perspectives of global supply chain design for sustainability. The first phase identifies sustainable supplier regions through multi-attribute utility theory, considering four regional sustainability indices for economic and social factors in global business and logistics, to reflect the decision maker's risk attitudes on global business opportunities. In the second phase, a multi-objective integer linear programming model for multiple sourcing and multiple product designs that minimizes economic and environmental objectives is applied to find optimal suppliers and their order quantities in the regions selected from the first phase. The proposed approach is illustrated through a bicycle supplier selection case study.

Collaborated with Iowa State University (Dr. Gül E. Okudan Kremer) and Mississippi State University (Dr. Junfeng Ma)

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