scholarly journals Precision Allocation Method of Large-Scale CNC Hobbing Machine Based On Precision-Cost Comprehensive Optimization

2021 ◽  
Author(s):  
Zongyan Hu ◽  
Shilong Wang ◽  
Chi Ma
Keyword(s):  
Machine Tool ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Cost Minimization ◽  
Optimization Method ◽  
Manufacturing Cost ◽  
Multi Objective ◽  
Allocation Method ◽  
Tool Performance ◽  
The Cost

Abstract In modern machine tool design, precision is an important index to characterize machine tool performance. and precision allocation has become a key task. Since middle 20th century, the precision allocation method using optimization technology to balance manufacturing cost and quality has gradually developed. But most methods mainly take the cost minimization as the goal to optimize the precision allocation. As the precision and manufacturing cost are a pair of factors to be comprehensively considered, balance between them is needed to meet different design requirements. This paper proposes a comprehensive optimization method to trade-off between precision and cost. A multi-object precision allocation optimization model aiming at minimizing fuzzy manufacturing cost and comprehensive precision of machine tool is constructed. A multi-object optimization algorithm to solve the model is designed, combining the multi-objective grey wolf optimization algorithm with multi-objective decision analysis method TOPSIS. A case study based on a large-scale hobbing machine shows that the comprehensive optimization of manufacturing cost and machining precision is realized by using the proposed multi-object precision allocation optimization method.

An Optimization Method for Geometric Error of Machine Tool Based on NSGA-II

2013 ◽  
Vol 418 ◽  
pp. 180-186
Author(s):  
Li Gang Cai ◽  
Cui Zhang ◽  
Qiang Cheng ◽  
Pei Hua Gu ◽  
Hong Ying Wang
Keyword(s):  
Machine Tool ◽  
Optimization Method ◽  
Geometric Error ◽  
Machining Accuracy ◽  
Body System ◽  
Critical Problem ◽  
Nsga Ii ◽  
Allocation Method ◽  
The Cost ◽  
Multi Body

Balancing the cost and processing precision of machine tool by the method of error allocation without affecting the machining performances is a critical problem in the Machine tool industry. In this paper, a new accuracy allocation method for multi-axis machine tool based on Multi-body system theory, manufacturing and quality loss costs and relationship between tolerances and accuracy parameters of components is proposed. This optimization method is performed with Non-Dominated Sorting Genetic Algorithm II algorithm using Isight and Matlab software. A three-axis vertical machine tool is taken as an example to demonstrate the method, and the optimization results show that the accuracy allocation method proposed is feasible in the optimization of geometric errors on the premise of satisfying machining accuracy requirements.

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

2019 ◽  
Vol 72 (2) ◽  
pp. 243-259 ◽  
Author(s):  
Mohammed M. Ahmed ◽  
Essam H. Houssein ◽  
Aboul Ella Hassanien ◽  
Ayman Taha ◽  
Ehab Hassanien
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Multi Objective ◽  
Whale Optimization

scholarly journals Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4362
Author(s):  
Subramaniam Saravana Sankar ◽  
Yiqun Xia ◽  
Julaluk Carmai ◽  
Saiprasit Koetniyom
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Fuel Consumption ◽  
Optimization Algorithm ◽  
Internal Combustion Engines ◽  
Optimization Method ◽  
Computational Time ◽  
Weight Factor ◽  
Driving Cycles ◽  
The Cost

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

Multi-objective process optimization for construction

1992 ◽  
Vol 19 (1) ◽  
pp. 129-136 ◽  
Author(s):  
Peter Chang ◽  
Leonhard Bernold
Keyword(s):  
Goal Programming ◽  
Optimization Method ◽  
Construction Cost ◽  
Construction Process ◽  
Trade Off ◽  
Process Duration ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
The Cost ◽  
Construction Optimization

Much of the existing work in construction analysis focuses on determining the construction cost based on an allowable project duration. In this type of construction analysis, two important questions are not considered. First, is the construction cost minimized for the allowable process duration? Second, would a small change in the process duration result in a significant change in the cost of the project? An optimization method is proposed to answer these questions. The approach consists of an integration of computer simulation with goal programming. The optimization method proposed allows one to assign priorities to the various design objectives such as cost and duration, which avoids the need to use subjective weights. Furthermore, since the approach simulates the construction process by computer, it can be applied to any repetitive construction process. In addition to the capability of the model to provide a single optimal solution to a construction optimization problem, it can be used to determine the trade-off between conflicting objectives. Examples are presented to illustrate the formulation process and the capabilities as a decision-making tool for construction. It is shown that the trade-off curves produced by the proposed model can provide useful information on the cost implications of various design variables, as well as on the trade-offs that exist among them. Key words: construction optimization, multi-objective optimization, goal programming, trade-off analysis, simulation.

scholarly journals Determination of Aircraft Cruise Altitude with Minimum Fuel Consumption and Time-to-Climb: An Approach with Terminal Residual Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 147
Author(s):  
Taehak Kang ◽  
Jaiyoung Ryu
Keyword(s):  
Cost Minimization ◽  
Complex Problem ◽  
Residual Analysis ◽  
Collocation Methods ◽  
Cost Index ◽  
Multi Objective ◽  
Direct Collocation ◽  
The One ◽  
The Cost

A pandemic situation of COVID-19 has made a cost-minimization strategy one of the utmost priorities for commercial airliners. A relevant scheme may involve the minimization of both the fuel- and time-related costs, and the climb trajectories of both objectives were optimized to determine the optimum aircraft cruise altitude. The Hermite-Simpson method among the direct collocation methods was employed to discretize the problem domain. Novel approaches of terminal residual analysis (TRA), and a modified version, m-σ TRA, were proposed to determine the goals. The multi-objective cruise altitude (MOCA) was different by2.5%, compared to the one statistically calculated from the commercial airliner data. The present methods, TRA and m-σ TRA were powerful tools in finding a solution to this complex problem. The value σ also worked as a transition criterion between a single- and multi-objective climb path to the cruise altitude. The exemplary MOCA was determined to be 10.91 and 11.97 km at σ = 1.1 and 2.0, respectively. The cost index (CI) varied during a flight, a more realistic approach than the one with constant CI. With validated results in this study, TRA and m-σ TRA may also be effective solutions to determine the multi-objective solutions in other complex fields.

scholarly journals A Multi-Objective Optimization for Supply Chain Network Using the Bees Algorithm

10.5772/56754 ◽  
2013 ◽  
Vol 5 ◽  
pp. 38 ◽  
Author(s):  
Ernesto Mastrocinque ◽  
Baris Yuce ◽  
Alfredo Lambiase ◽  
Michael S. Packianather
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Cost Minimization ◽  
Optimization Method ◽  
Bees Algorithm ◽  
Chain Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimum Configuration ◽  
Chain Problem

A supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels, hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as cost minimization and lead-time minimization. There are several multi-objective optimization methods which have been applied to find the optimum solutions set based on the Pareto front line. In this study, a swarm-based optimization method, namely, the bees algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature and several experiments have been conducted in order to show the performance of the proposed model; in addition, the results have been compared to those achieved by the ant colony optimization method. The results show that the proposed bees algorithm is able to achieve better Pareto solutions for the supply chain problem.

Design Optimization of Machine-Tool Structures Considering Manufacturing Cost, Accuracy, and Productivity

1984 ◽  
Vol 106 (4) ◽  
pp. 531-537 ◽  
Author(s):  
M. Yoshimura ◽  
Y. Takeuchi ◽  
K. Hitomi
Keyword(s):  
Machine Tool ◽  
Design Optimization ◽  
Structural Model ◽  
Structural Models ◽  
Optimization Method ◽  
Machining Accuracy ◽  
Manufacturing Cost ◽  
Structural Members ◽  
Machine Tool Structures ◽  
Machining Productivity

This paper proposes a multiphase design optimization method using simplified structural models in order to minimize manufacturing cost of machine-tool structures under constraints of machining accuracy, machining productivity, and local deformations of structural members. The manufacturing cost is divided into three components—material cost, welding cost, and machining cost, each of which is minimized in the multiphase optimization process. The method is demonstrated on a structural model of a double-column machine tool.

scholarly journals Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3337
Author(s):  
Ruiye Li ◽  
Peng Cheng ◽  
Yingyi Hong ◽  
Hai Lan ◽  
He Yin
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Element Model ◽  
Optimization Method ◽  
Geometric Parameters ◽  
Maximum Temperature ◽  
Design Decision ◽  
Multi Objective

The extensive use of finite element models accurately simulates the temperature distribution of electrical machines. The simulation model can be quickly modified to reflect changes in design. However, the long runtime of the simulation prevents any direct application of the optimization algorithm. In this paper, research focused on improving efficiency with which expensive analysis (finite element method) is used in generator temperature distribution. A novel surrogate model based optimization method is presented. First, the Taguchi orthogonal array relates a series of stator geometric parameters as input and the temperatures of a generator as output by sampling the design decision space. A number of stator temperature designs were generated and analyzed using 3-D multi-physical field collaborative finite element model. A suitable shallow neural network was then selected and fitted to the available data to obtain a continuous optimization objective function. The accuracy of the function was verified using randomly generated geometric parameters to the extent that they were feasible. Finally, a multi-objective genetic optimization algorithm was applied in the function to reduce the average and maximum temperature of the machine simultaneously. As a result, when the Pareto front was compared with the initial data, these temperatures showed a significant decrease.

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