Optimization of Process Parameters in Laser Engineered Net Shaping (LENS) Deposition of Multi-Materials

2015 ◽  
Author(s):  
Jingyuan Yan ◽  
Nafiseh Masoudi ◽  
Ilenia Battiato ◽  
Georges Fadel
Keyword(s):  
Fabrication Process ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Feeding Rates ◽  
Multi Objective ◽  
Heterogeneous Objects ◽  
Multiple Materials ◽  
Optimization Methodology ◽  
Manufacturing Technologies ◽  
Net Shaping

During the past few years, metal based additive manufacturing technologies have evolved and may enable the direct fabrication of heterogeneous objects with full spatial material variations. A heterogeneous object has potentially many advantages and in many cases can realize appearance and/or functionality that homogeneous objects cannot achieve. In this work we employ a preprocess computing combined with a multi-objective optimization algorithm based on the modeling of the LENS deposition of multiple materials to optimize the fabrication process. The optimization methodology is applied to the fabrication of cermet composite (using Inconel 718 and ceramic powders) with prescribed material feeding rates. The multi-objective optimization considers that the energy consumption and the material waste during the fabrication process should be minimized, while the probability of the melting of the powders should be maximized. The optimization software modeFRONTIER® is used to drive the computation procedure with a MATLAB code. The results show the design and objective spaces of the Pareto optimal solutions, and enable the users to select preferred setting configurations from the set of optimal solutions.

A Mathematical Model-Based Optimization Method for Direct Metal Deposition of Multimaterials

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Jingyuan Yan ◽  
Ilenia Battiato ◽  
Georges M. Fadel
Keyword(s):  
Inconel 718 ◽  
Laser Power ◽  
Laser Energy ◽  
Scanning Speed ◽  
Metal Deposition ◽  
Direct Metal Deposition ◽  
Fabrication Process ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective

During the past few years, metal-based additive manufacturing technologies have evolved and may enable the direct fabrication of heterogeneous objects with full spatial material variations. A heterogeneous object has potentially many advantages, and in many cases can realize the appearance and/or functionality that homogeneous objects cannot achieve. In this work, we employ a preprocess computing combined with a multi-objective optimization algorithm based on the modeling of the direct metal deposition (DMD) of dissimilar materials to optimize the fabrication process. The optimization methodology is applied to the deposition of Inconel 718 and Ti–6Al–4V powders with prescribed powder feed rates. Eight design variables are accounted in the example, including the injection angles, injection velocities, and injection nozzle diameters for the two materials, as well as the laser power and scanning speed. The multi-objective optimization considers that the laser energy consumption and the powder waste during the fabrication process should be minimized. The optimization software modeFRONTIER® is used to drive the computation procedure with a matlab code. The results show the design and objective spaces of the Pareto optimal solutions and enable the users to select preferred setting configurations from the set of optimal solutions. A feasible design is selected which corresponds to a relatively low material cost, with laser power 370 W, scanning speed 55 mm/s, injection angles 15 deg, injection velocities 45 m/s for Inconel 718, 30 m/s for Ti–6Al–4V, and nozzle widths 0.5 mm under the given condition.

scholarly journals Single- and Multi-Objective Optimization of a Dual-Chamber Microbial Fuel Cell Operating in Continuous-Flow Mode at Steady State

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 839
Author(s):  
Ibrahim M. Abu-Reesh
Keyword(s):  
Flow Rate ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Dual Chamber ◽  
Worst Case ◽  
Multi Objective ◽  
Model Based ◽  
Conflicting Objectives

Microbial fuel cells (MFCs) are a promising technology for bioenergy generation and wastewater treatment. Various parameters affect the performance of dual-chamber MFCs, such as substrate flow rate and concentration. Performance can be assessed by power density ( PD ), current density ( CD ) production, or substrate removal efficiency ( SRE ). In this study, a mathematical model-based optimization was used to optimize the performance of an MFC using single- and multi-objective optimization (MOO) methods. Matlab’s fmincon and fminimax functions were used to solve the nonlinear constrained equations for the single- and multi-objective optimization, respectively. The fminimax method minimizes the worst-case of the two conflicting objective functions. The single-objective optimization revealed that the maximum PD ,   CD , and SRE were 2.04 W/m2, 11.08 A/m2, and 73.6%, respectively. The substrate concentration and flow rate significantly impacted the performance of the MFC. Pareto-optimal solutions were generated using the weighted sum method for maximizing the two conflicting objectives of PD and CD in addition to PD and SRE   simultaneously. The fminimax method for maximizing PD and CD showed that the compromise solution was to operate the MFC at maximum PD conditions. The model-based optimization proved to be a fast and low-cost optimization method for MFCs and it provided a better understanding of the factors affecting an MFC’s performance. The MOO provided Pareto-optimal solutions with multiple choices for practical applications depending on the purpose of using the MFCs.

scholarly journals A New Method for Dynamic Multi-Objective Optimization Based on Segment and Cloud Prediction

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 465 ◽  
Author(s):  
Peng Ni ◽  
Jiale Gao ◽  
Yafei Song ◽  
Wen Quan ◽  
Qinghua Xing
Keyword(s):  
Optimization Problems ◽  
Pareto Set ◽  
Benchmark Problems ◽  
Small Range ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Good Convergence ◽  
Multi Objective ◽  
The Law ◽  
Cloud Theory

In the real world, multi-objective optimization problems always change over time in most projects. Once the environment changes, the distribution of the optimal solutions would also be changed in decision space. Sometimes, such change may obey the law of symmetry, i.e., the minimum of the objective function in such environment is its maximum in another environment. In such cases, the optimal solutions keep unchanged or vibrate in a small range. However, in most cases, they do not obey the law of symmetry. In order to continue the search that maintains previous search advantages in the changed environment, some prediction strategy would be used to predict the operation position of the Pareto set. Because of this, the segment and multi-directional prediction is proposed in this paper, which consists of three mechanisms. First, by segmenting the optimal solutions set, the prediction about the changes in the distribution of the Pareto front can be ensured. Second, by introducing the cloud theory, the distance error of direction prediction can be offset effectively. Third, by using extra angle search, the angle error of prediction caused by the Pareto set nonlinear variation can also be offset effectively. Finally, eight benchmark problems were used to verify the performance of the proposed algorithm and compared algorithms. The results indicate that the algorithm proposed in this paper has good convergence and distribution, as well as a quick response ability to the changed environment.

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

2019 ◽  
Vol 33 (08) ◽  
pp. 1959025
Author(s):  
Lan Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Superior Performance ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Evolution Algebra ◽  
Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

Sirocco Fan Design for Residential Ventilation Through Multi-Objective Optimization to Enhance Aerodynamic Performance

2012 ◽  
Author(s):  
Jin-Hyuk Kim ◽  
Kyung-Hun Cha ◽  
Kwang-Yong Kim
Keyword(s):  
Total Pressure ◽  
Stokes Equations ◽  
Pressure Rise ◽  
Total Efficiency ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Sirocco Fan

A multi-objective optimization of a sirocco fan for residential ventilation has been carried out in the present work. A hybrid multi-objective evolutionary algorithm combined with response surface approximation is applied to optimize the total-to-total efficiency and total pressure rise of the sirocco fan for residential ventilation. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by finite volume method and solved on hexahedral grids for the flow analysis. Numerical results are validated with the experimental data for the total-to-total efficiency and total pressure. The total-to-total efficiency and total pressure rise of the sirocco fan are used as objective functions for the optimization. In order to improve the total-to-total efficiency and total pressure rise of the sirocco fan, four variables defining the scroll cut-off angle, scroll diffuser expansion angle, hub ratio and the blade exit angle, respectively, are selected as the design variables in this study. Latin-hypercube sampling as design-of-experiments is used to generate the design points within the design space. A fast non-dominated sorting genetic algorithm with an ε–constraint strategy for the local search is applied to determine the global Pareto-optimal solutions. The trade-off between two objectives is determined and discussed with respect to the representative clustered optimal solutions in the Pareto-optimal solutions compared to the reference shape.

scholarly journals A NEW PROBABILISTIC ALGORITHM FOR SOLVING A CLASS OF SINGLE OR MULTI-OBJECTIVE OPTIMAL PROBLEMS

2009 ◽  
Vol 12 (11) ◽  
pp. 11-26
Author(s):  
Hao Van Tran ◽  
Thong Huu Nguyen
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Fixed Number ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Probabilistic Algorithm ◽  
Multi Objective ◽  
Single Objective

We consider a class of single-objective optimization problems which haves the character: there is a fixed number k (1≤k<n) that is independent of the size n of the problem such that if we only need to change values of k variables then it has the ability to find a better solution than the current one, let us call it Ok. In this paper, we propose a new numerical optimization technique, Search Via Probability (SVP) algorithm, for solving single objective optimization problems of the class Ok. The SVP algorithm uses probabilities to control the process of searching for optimal solutions. We calculate probabilities of the appearance of a better solution than the current one on each of iterations, and on the performance of SVP algorithm we create good conditions for its appearance. We tested this approach by implementing the SVP algorithm on some test single-objective and multi objective optimization problems, and we found good and very stable results.

scholarly journals Multi-objective optimization of single-lane roundabout geometric design: safety, mobility, and environmental sustainability

2021 ◽  
Author(s):  
Hend Ahmed ◽  
Said M. Easa
Keyword(s):  
Environmental Sustainability ◽  
Vehicle Emissions ◽  
Geometric Design ◽  
Geometric Parameters ◽  
Specific Power ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Highway Safety Manual ◽  
Optimization Methodology ◽  
The Uk

Mobility, safety performance and environmental sustainability are priorities in the geometric design of roundabouts. This thesis presents a multi-objective optimization methodology for the geometric design of single-lane roundabouts. Mobility is defined in terms of roundabout delay and modeled using the (UK) empirical model. The collision frequency represents the safety objective, and modeled using the methodology outlined in the Highway Safety Manual. Environmental sustainability is represented by NOX, HC, CO2, and CO vehicle emissions and is modeled using the vehicle specific power (VSP) methodology. The presented model directly identifies the optimal geometric parameters of roundabouts. Traffic data, site conditions, and guidelines limitations were used as input data while the output decision values that minimize delay, collisions, and vehicle emissions are the optimal geometric parameters. The practical application of the proposed model is illustrated using an application example. The model was validated using an actual location, and a sensitivity analysis was conducted.

scholarly journals A Multi-Objective Solution of Green Vehicle Routing Problem

2019 ◽  
Vol 10 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Özgür Kabadurmuş ◽  
Mehmet Serdar Erdoğan ◽  
Yiğitcan Özkan ◽  
Mertcan Köseoğlu
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Alternative Fuel ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Routing Problem ◽  
Multi Objective

Abstract Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used ɛ-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.

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