Optimization of Multistage Machining Systems, Part 1: Mathematical Solution

1992 ◽  
Vol 114 (4) ◽  
pp. 524-531 ◽  
Author(s):  
J. S. Agapiou
Keyword(s):  
Optimization Problem ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Idle Time ◽  
Optimization Techniques ◽  
Machining Parameters ◽  
Machining Time ◽  
Physical Constraints ◽  
Time Requirements ◽  
The Cost

The optimization problem for multistage machining systems has been investigated. Due to uneven time requirements at different stages in manufacturing, there could be idle times at various stations. It may be advantageous to reduce the values of machining parameters in order to reduce the cost at stations that require less machining time. However, optimization techniques available through the literature do not effectively utilize the idle time for the different stations generated during the balancing of the system. Proposed in this paper is an optimization method which utilizes the idle time to the full extent at all machining stations, with the intention of improving tool life and thus achieving cost reduction. The mathematical analysis considers the optimization of the production cost with an equality constraint of zero idle time for the stations with idle time. Physical constraints regarding the cutting parameters, force, power, surface finish, etc., as they arise in different operations, are also considered. The aforementioned problem has been theoretically analyzed and a computational algorithm developed. The advantages and effectiveness of the proposed approach are finally established through an example.

Different Methods of Artificial Intelligence Used for Optimization the Turning Process

2015 ◽  
Vol 808 ◽  
pp. 60-65
Author(s):  
Nicolae Cofaru ◽  
Adrian Florea
Keyword(s):  
Random Variable ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Software Application ◽  
Random Weights ◽  
The Cost ◽  
Optimal Cutting Parameters ◽  
Cost Components

In this paper, we realize a comparative study between some heuristics methods applied in turning operation in order to find optimal cutting parameters. We consider five different constraints aimed to achieve minimum total cost of machining. We have chosen the Simulated Annealing (SA) – a local search method, and Weighted-Sum Genetic Algorithm (WSGA) – a non-Pareto approach of a multi-objective optimization algorithm, based on a weighted aggregation of objectives. The aggregation may be with fixed weights or with random (variable) weights. The simulations showed that, even if it produces better results than the SA, WSGA with fixed weights, does not lead to optimum results, highlighting in this way that in the formula of the cost function, some cost components may be more important than others. In addition, we extend our previous work by integrating in the software application a new optimization method: WSGA with random weights. Also, we increase the application’s flexibility by reconfiguring the graphical user interface in order to allow tuning the optimization techniques parameters.

Design Optimization of Cutting Parameters in Turning 45Cr Steel Using Cermets Tool

2010 ◽  
Vol 443 ◽  
pp. 238-243 ◽  
Author(s):  
Zhi Lin Han ◽  
Bin Lin ◽  
Bao Xing Zhang ◽  
Lei Zhang
Keyword(s):  
Principal Component Analysis ◽  
Optimal Parameter ◽  
Principal Component ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Component Analysis ◽  
Process Efficiency ◽  
Machining Parameters ◽  
Optimization Of Cutting Parameters

In this paper, the optimization of cutting parameters in turning thin-walled 45Cr steel workpieces with cermets tool is researched. A new integrated optimization method is proposed, in which the parameter design of the Taguchi method, principal component analysis method and response surface method (RSM) are applied to obtain the optimal parameter for a hard turning process using mixed cermets tools. The orthogonal array experiment is conducted to economically obtain the response measurements. The Principal Component Analysis (PCA) is applied to transform the original evaluation variables into new, uncorrelated comprehensive variables, which includes most information of original variables, then the output response can be calculated by the principal components. At last, the RSM is used to build the relationship between the input parameters and output responses, and obtain the desired machining parameters. The quality of workpieces and the process efficiency are improved obviously.

Taguchi Design of Experiment in the Optimization of Tool Life in Turning Process of Duplex Stainless Steel DSS

2015 ◽  
Vol 809-810 ◽  
pp. 189-194
Author(s):  
Grzegorz Krolczyk ◽  
Andrzej Metelski ◽  
Radoslaw Maruda ◽  
Stanislaw Legutko
Keyword(s):  
Tool Life ◽  
Design Of Experiment ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Cutting Condition ◽  
Machining Parameters ◽  
Deep Well ◽  
Optimal Values ◽  
Coated Carbide

The paper presents the contribution in methodology of production processes of difficulty to cut materials particularly in optimization method of Duplex Stainless Steels (DSS). In this work, Design of Experiment (DOE) is used to examine turning experimental data. The DOE, based on the Taguchi method with orthogonal array L9 and signal-to-noise ratio are used. The optimal values of the technological cutting parameters with coated carbide tool point are searched. ANOVA analysis was performed to determine the signification of machining parameters. The significance of various cutting parameters on tool life have been proven. The results at optimum cutting condition are predicted using estimated values. The study was performed within a production facility during the machining of electric motor parts and deep-well pumps.

scholarly journals An adjoint for likelihood maximization

2009 ◽  
Vol 465 (2111) ◽  
pp. 3267-3287 ◽  
Author(s):  
David J. J. Toal ◽  
Alexander I. J. Forrester ◽  
Neil W. Bressloff ◽  
Andy J. Keane ◽  
Carren Holden
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Optimization Problem ◽  
Computational Modelling ◽  
Kriging Model ◽  
Optimization Techniques ◽  
Algorithmic Differentiation ◽  
Accelerate Convergence ◽  
Standard Analytical Method ◽  
The Cost

The process of likelihood maximization can be found in many different areas of computational modelling. However, the construction of such models via likelihood maximization requires the solution of a difficult multi-modal optimization problem involving an expensive O ( n 3 ) factorization. The optimization techniques used to solve this problem may require many such factorizations and can result in a significant bottleneck. This article derives an adjoint formulation of the likelihood employed in the construction of a kriging model via reverse algorithmic differentiation. This adjoint is found to calculate the likelihood and all of its derivatives more efficiently than the standard analytical method and can therefore be used within a simple local search or within a hybrid global optimization to accelerate convergence and therefore reduce the cost of the likelihood optimization.

Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm

2015 ◽  
Vol 815 ◽  
pp. 268-272 ◽  
Author(s):  
Nur Farahlina Johari ◽  
Azlan Mohd Zain ◽  
Noorfa Haszlinna Mustaffa ◽  
Amirmudin Udin
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Firefly Algorithm ◽  
Volume Fraction ◽  
Optimization Problems ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Time

Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

scholarly journals An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process

2019 ◽  
Vol 9 (7) ◽  
pp. 1495 ◽  
Author(s):  
Shih-Ming Wang ◽  
Chun-Yi Lee ◽  
Hariyanto Gunawan ◽  
Chin-Cheng Yeh
Keyword(s):  
Power Consumption ◽  
Optimization Method ◽  
Milling Process ◽  
Empirical Models ◽  
Hybrid Optimization ◽  
Machining Parameters ◽  
Machining Time ◽  
Hybrid Optimization Method ◽  
Guiding Function ◽  
Optimal Cutting Parameters

This study proposes a hybrid optimization method which can help users to find optimal cutting parameters which will provide better efficiency and lower power consumption for a milling process. Empirical models including performance-power consumption characteristic curves of servo motors were built, and an optimization algorithm adopting the empirical models with procedure guiding function was developed. The empirical models were built based on the measurements from planned machining experiments with different combination of machining parameters including spindle speed, feedrate, and chip load, etc. After integrating the models and algorithm, an optimization system with human machine interface, which has procedure guiding function, was developed. The system can recommend optimal machining parameters for a milling process for shorter machining time and lower electricity costs based on the original machining parameters. Finally, cutting experiments were conducted to verify the proposed system, and the results showed that the proposed method can effectively enhance efficiency by 42.06% and save 34.74% in machining costs through reducing machining time and electrical power consumption.

Application of Taguchi Method in the Optimization of Cutting Parameters for Turning Metal Matrix Composite

2011 ◽  
Vol 189-193 ◽  
pp. 3056-3060 ◽  
Author(s):  
Keartisak Sriprateep ◽  
Puttipong Patumchat ◽  
Wasan Theansuwan
Keyword(s):  
Taguchi Method ◽  
Metal Matrix ◽  
Design Method ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Taguchi Methods ◽  
Machining Parameters ◽  
Taguchi Optimization ◽  
Optimization Of Cutting Parameters

The objective of this study was to utilize Taguchi methods to optimize surface roughness, tool wear and power required to perform the machining operation in turning metal matrix composites (MMC). The cutting parameters are analyzed under varying cutting speed, feed rates and cutting time. The settings of turning parameters were determined by using Taguchi’s experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the turning parameters. Confirmation tests with the optimal levels of machining parameters are carried out in order to illustrate the effectiveness of Taguchi optimization method. The results show that the Taguchi method is suitable to solve the stated problem with minimum number of trials.

scholarly journals Firefighting Equipment Arrangement Optimization for an Offshore Platform Considering Travel Distances

2021 ◽  
Vol 9 (5) ◽  
pp. 503
Author(s):  
Byeong Cheol Lee ◽  
Youngsu Choi ◽  
Hyun Chung
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Offshore Platform ◽  
Set Cover ◽  
Coverage Area ◽  
Set Cover Problem ◽  
Service Areas ◽  
Cover Problem ◽  
The Cost ◽  
Initial Arrangement

The offshore plant, due to its characteristics, is subject to many restrictions on the material and design of the pipes. Because the design of the firefighting piping depends on the pre-set fire protection design, it is possible to reduce the cost of the piping design by optimizing the arrangement of the firefighting equipment. Existing studies have low accuracy in obtaining service areas under these conditions. In addition, the arrangement optimization problem is generally modeled as a set cover problem (SCP). However, except for the traditional greedy approximation, this problem is not well researched for general solutions. In this paper, first, a modified iterative-deepening search (MIDS), which accurately obtains a service area according to the travel distance in the grid space, is proposed before optimization. Additionally, this paper seeks to define a set cover problem by combining the subsets obtained by MIDS. Second, by using the traditional greedy algorithm, we obtained the initial arrangement of the firefighting equipment. Then, we proposed a method to obtain an approximate optimization solution using a modified greedy method including rearrangement. The validity of the proposed coverage area acquisition and arrangement optimization method is verified by comparing the performance with other algorithms. Finally, this study was applied to the drawings of an actual offshore platform.

A Methodology of Off-Line Optimization for NC Milling Process

2006 ◽  
Vol 315-316 ◽  
pp. 1-5 ◽  
Author(s):  
Ying Xue Yao ◽  
Chang Qing Liu ◽  
Jian Guang Li ◽  
H.J. Jing ◽  
S.D. Chen
Keyword(s):  
End Milling ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Milling Process ◽  
Machining Process ◽  
Machining Time ◽  
Cutting Power ◽  
Nc Program ◽  
Nc Milling ◽  
Control Technologies

Traditional adaptive control technologies in machining process optimization are limited in applications because they depend much on sensors, controllers and other hardware. An off-line optimization method for end milling process with constant cutting power is presented. On taking advantage of virtual machining which simulates milling process, acquires cutting parameters and predicts cutting forces, method taking constant cutting power as an objective is discussed to optimize feed rates and cutting speeds. Based on optimal result, the feed rates and spindle revolutions in NC program are re-scheduled. Controlled milling experiments show that machining time is reduced and machining stability is improved by using the optimized NC program.

scholarly journals Optimization Method of Customized Shuttle Bus Lines under Random Condition

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 52
Author(s):  
Zhichao Sun ◽  
Kang Zhou ◽  
Xinzheng Yang ◽  
Xiao Peng ◽  
Rui Song
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Operating Cost ◽  
Optimization Method ◽  
Routing Problem ◽  
Hybrid Intelligent Algorithm ◽  
Passenger Travel ◽  
Time Requirements ◽  
The Cost ◽  
Stochastic Constraints

Transit network optimization can effectively improve transit efficiency, improve traffic conditions, and reduce the pollution of the environment. In order to better meet the travel demands of passengers, the factors influencing passengers’ satisfaction with a customized bus are fully analyzed. Taking the minimum operating cost of the enterprise as the objective and considering the random travel time constraints of passengers, the customized bus routes are optimized. The K-means clustering analysis is used to classify the passengers’ needs based on the analysis of the passenger travel demand of the customized shuttle bus, and the time stochastic uncertainty under the operating environment of the customized shuttle bus line is fully considered. On the basis of meeting the passenger travel time requirements and minimizing the cost of service operation, an optimization model that maximizes the overall satisfaction of passengers and public transit enterprises is structured. The smaller the value of the objective function is, the lower the operating cost. When the value is negative, it means there is profit. The model is processed by the deterministic processing method of random constraints, and then the hybrid intelligent algorithm is used to solve the model. A stochastic simulation technique is used to train stochastic constraints to approximate uncertain functions. Then, the improved immune clonal algorithm is used to solve the vehicle routing problem. Finally, it is proved by a case that the method can reasonably and efficiently realize the optimization of the customized shuttle bus lines in the region.

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