ISME Graduate Student Conference Papers

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    Integrated projects: Applying Lean principles to reverse engineer a common consumer product to develop a more sustainable design
    (Wichita State University, 2023-04-14) Williams, Amber; Lynch, Adam Carlton
    We will be evaluating the effects of integrating a graduate engineering course with five undergraduate courses in a customer-supplier relationship, linking paired teams into squads with a unifying "Drill." The teams will be using the Six Sigma DMAIC methodology and KEEN (Kern Entrepreneurial Engineering Network) Entrepreneurial Mindset team building activities to provide consistency of deliverables and strength team dynamics. The objective is to reverse engineer a common consumer product and discover ways to improve its design to manufacture it in a more sustainable manner, while still meeting the operational and financial goals of a company. Simultaneously, we will be meeting the Wichita State goal which bridges the gap between classroom learning and real-life experiences through our integrated project. Our study sample consists of over 150 students enrolled in 6 engineering classes. The central theme in all the classes is a common, global product, a drill. Each course will be examining different components of the drill, performing design analysis within the specific course bodies of knowledge. The drill sub-assemblies included the hard carrying case for Statics, batteries & charging stations for Circuits, the main drill body for Machine Elements, statistical process control of the components in an SPC course, project management in an Engineering Leadership course, culminating in the graduate course, Lean, which will develop a Business Plan to launch a startup business to manufacture and assemble all the components of the drill. The integrated groups have weekly deliverables. This pilot program is presently in the last 16-weeks of the semester. We will measure, analyze, and improve this program through our data pool of 150 students by conducting interviews, surveys, and other case studies. This integrated project places students in customer-supplier relationships as actual companies would interact. The goal of these customer-supplier interactions will help to cultivate interpersonal skills for future careers in a variety of industries. Our secondary goal is to help students rediscover their curiosity in engineering while improving as a student through value creation resulting from the connections with students from the other classes. Our third goal is to prepare these fellow students for their future industrial colleagues.
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    Energy-aware optimization for solving distributed flexible job shop scheduling problems
    (Wichita State University, 2023-04-14) Joshi, Rahul; Gupta, Deepak P.
    INTRODUCTION: The significance of distributed manufacturing and energy costs in today's continuously increasing globalizing world has been recognized by scholars and researchers and efforts have been taken to improve process scheduling efficiency/reduce manufacturing costs over the years. A Distributed Flexible Job-Shop Scheduling Problem (DFJSP) is a distributed scheduling problem where each factory represents a single Flexible Job-Shop Scheduling Problem where a particular job cannot be assigned to more than one factory. PURPOSE: This research aims to address the distributed flexible job shop scheduling problem (DFJSP) by minimizing the maximum completion time (makespan time) where setup times of available machines for current operation depends on the previous operation processed by it (sequence dependent setup times). METHODS: This research proposed and formulated a mixed integer linear programming (MILP) model with an early optimization termination criterion to solve the DFJSP. Furthermore, this model is extended to consider electricity consumption by introducing the objective function of energy usage in the form of processing energy, setup energy & idle energy consumption and a strategy to shutdown machines when the machines are in idle state. A constraint programming model and a meta-heuristic algorithm in the form of a combination of Genetic and NEH algorithm (NEH-GA) is proposed and compared with the MILP model. RESULTS: The results shows that the MILP model achieves optimality within reasonable time and performs better than constraint programming model when problem complexity is small whereas constraint programming tends to do better when the problem complexity is large. The proposed meta-heuristic algorithm performs the best in terms of computation time when the problem complexity is large albeit not guaranteeing an optimal solution. CONCLUSION: All three models were successful in finding solutions which reduce the total energy consumption in five out of the total fourteen problem instances. For future research studies, the current DFJSP base model can be modified to other objectives such as earliness/tardiness. Similarly, optimization models can be formulated for DFJSP considering machine breakdowns with sequence dependent setup times.
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    Estimating and optimizing HVAC energy costs in industrial building
    (Wichita State University, 2022-04-29) Joshi, Rahul; Gupta, Deepak P.
    In the US, industrial buildings accounts for over 33% of the total energy consumption. Heating, ventilation & air-conditioning (HVAC) system consumes second most energy after production system. In industrial buildings, heat waste from heavy machineries and large building envelope makes the HVAC system work harder when compared to commercial and residential buildings. The objective of this research is to find factors responsible for high HVAC system energy costs and optimize them to maximize energy savings. Most industrial facilities do not have sub-metering of individual energy consuming component. To establish baseline energy consumption model and disaggregate the HVAC component of energy consumption relative to changes in production and weather data, we propose the use of simple inverse linear regression models using monthly utility billing and weather data. In addition, multi-layer Long short-term memory (LSTM) neural network model is used to forecast short term weather to predict temperature. Using the forecasted weather and baseline HVAC energy consumption relation, a mixed integer programming model is used to maximize energy savings by scheduling activities within the building. The optimization model was tested using experimental data to find trends and relation between energy savings and factors affecting it by considering constraints such as resource availability, deadlines etc. The LSTM model achieves a high validation accuracy of up to 80%. This optimization model can potentially achieve up to 30% reduction in HVAC energy consumption.
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    A hybrid learning model for the vehicle routing problem with drones in the era of industry 4.0
    (Wichita State University, 2022-04-29) Arishi, Ali; Krishnan, Krishna K.
    Under the vision of industry 4.0, the application of drones or unmanned aerial vehicles (UAV) in last-mile transportation has been capturing the attention of many logistics giants. Leaders in logistics and transportation such as Amazon, UPS, and Uber are investigating the use of drones to solve the last-mile problem. However, finding an optimal routing strategy for a truck with multiple drones is an NP-hard problem. Traditional methods suffer from a long computation time and scalability issues. In this study, we consider a different scheme of the last-mile delivery problem in which a modern truck equipped with a fleet of drones is used to deliver light parcels to a set of customer locations. In this formulation, the truck starts from the depot and visits a set of launching sites where the truck parks and drones can be deployed to make all last-mile deliveries. All drones have a limited flying range and load capacity that cannot exceed in every trip. After serving the customer, drones must return to the truck for a battery swap. Once all drones are collected, the truck moves to the next parking location and repeats the process until all customers are served. To tackle this problem, a hybrid machine learning approach for location clustering and routing decisions for last-mile delivery is proposed. The proposed hybrid model comprises two stages. We propose a constrained k-means algorithm to cluster customer locations based on the user-specified constraints in the first stage. The centroid of each constrained cluster will serve as a parking spot for the truck. In the second stage, a deep reinforcement model is trained to cover all parking spots. Using the proposed hybrid approach, the objective is to reduce the total operational cost of the truck-drones delivery problem by finding a good partition that optimizes the trade-off between the number of parking stops and the distance covered by each drone while adhering to the userspecified constraints. Extensive experiments are carried out to evaluate the effectiveness of the proposed approach. Solutions obtained are compared with other classical methods. Results show that our approach can produce quality solutions in real-time. Moreover, a sensitivity analysis of key parameters is conducted to highlight critical trade-offs in using the multiple drones and their dependence on operating costs and problem sizes.
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    3D printing of Portland cement using binder jetting systems
    (Wichita State University, 2022-04-29) Al Turk, Abdelhakim; Weheba, Gamal
    Additive Manufacturing (AM) in recent years has become a common method for creating detailed parts or models from 3D CAD data. The main applications of AM in the construction industry are dominated by Contour Crafting (CC) and Binder Jetting (BJ). Start writing your introduction here. This Project presents proof of the concept that Portland cement can be used as a material on a commercial binder jetting system 3d printer. The Zprinter 450 binder jetting 3D printer was used to print sixteen cement cylindrical specimens. The standard powder used on this 3d printer was replaced by Quick-Setting Cement material (Quikrete 1240). Cylindrical cement specimens were constructed using different layer thicknesses 0.0035 and 0.005 inches. Once the printing was completed, the specimens were cured in one of two solutions (water and alkaline solution) over two periods (14 and 28 days). Both printing and curing followed a replicated factorial experiment covering all possible combinations of the factors. Average values of the compressive strength and deviation from nominal height (DNH) were used as response variables. An examination of the first interaction plot indicates that maximum strength is obtained when the specimens were printed using the low level of layer thickness and cured in the alkaline solution. An examination of the second interaction plot reveals that changes in the layer thickness have a significant effect on the average deviation when the alkaline solution was used. While all measurements indicated an increase in the average height, the maximum deviation is observed when specimens were constructed using the low level of layer thickness and cured using the alkaline solution. This project proved that Portland cement can be 3d printed using a binder jetting system with and compressive strength around 14 MPa (2030.53 PSI). This Project has several potential applications in the construction industry. It may eliminate the need for traditional methods in restoration projects, where small tiles and sculptures need to be replaced. The method may be used to 3D print customized tiles with letter scripts like the house number plaque or the decorative calligraphy tiles found in Churches and Mosques.
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