scholarly journals Application of EDAS, MARCOS, TOPSIS, MOORA and PIV Methods for Multi-Criteria Decision Making in Milling Process

2021 ◽  
Vol 71 (2) ◽  
pp. 69-84
Author(s):  
Do Duc Trung
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Milling Process ◽  
Multi Criteria Decision Making ◽  
Milling Method

Abstract Low surface roughness and high Material Removal Rate (MRR) are expected in most machining methods in general and milling method in particular. However, they sometimes do not occur, for example, the MRR is often small as the surface roughness is low. In this case, the decisions made should ensure that desires are simultaneously satisfied. This situation leads to a problem known as multi-criteria decision making (MCDM). In this study, five methods including EDAS, MARCOS, PIV, MOORA and TOPSIS are used together for the decision-making in the milling process. The purpose of the research is to determine the value of cutting parameters for both the low surface roughness and large MRR. The comparison of these methods for finding the best is carefully discussed as well.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Effects of Cutting Parameters on the Surface Roughness of Ti6Al4V Titanium Alloys in Side Milling

2011 ◽  
Vol 175 ◽  
pp. 289-293 ◽  
Author(s):  
Hao Liu ◽  
Chong Hu Wu ◽  
Rong De Chen
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Side Milling ◽  
Milling Parameters ◽  
Fine Grain

Side milling Ti6Al4V titanium alloys with fine grain carbide cutters is carried out. The influences of milling parameters on surface roughness are investigated and also discussed with average cutting thickness, material removal rate and vibration. The results reveal that the surface roughness increases with the increase of average cutting thickness and is primarily governed by the radial cutting depth.

scholarly journals EXPERIMENTAL ANALYSIS AND OPTIMIZATION OF THE CONTROLLABLE PARAMETERS IN TURNING OF EN AW-2011 ALLOY; DRY MACHINING AND ALTERNATIVE COOLING TECHNIQUES

2020 ◽  
Vol 18 (1) ◽  
pp. 013
Author(s):  
Sonja Jozić ◽  
Ivana Dumanić ◽  
Dražen Bajić
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Minimum Quantity Lubrication ◽  
Cutting Parameters ◽  
Dry Machining ◽  
Cold Air ◽  
Minimum Quantity

The latest trends in machining research show that great efforts are being made to understand the impact of different cooling and lubrication techniques as well as cutting parameters on machining performances. This paper presents the investigation results of different cutting parameters and different cutting environments such as dry machining, minimum quantity lubrication (MQL) and minimum quantity lubrication with compressed cold air (MQL+CCA) on average surface roughness, cutting force and material removal rate. The experiments were designed based on three input parameters and three different cutting environments when turning of EN AW-2011 alloy. Taguchi-based grey relational analysis was used to identify the optimal process parameters by which minimum values of surface roughness, minimum value of cutting force and maximum value of material removal rate will be achieved. The results showed that minimum quantity lubrication in the stream of compressed cold air, in comparison to dry and minimum quantity lubrication machining, gives the best machining performances. Therefore, the use of MQL + CCA method, which reduces the amount of lubricant may represent in the described extent of turning operations an alternative to turning processes most often carried out by wet method that causes considerable costs for purchasing, maintaining and using cutting fluids.

Study of the Cutting Parameters on Surface Roughness and Material Removal Rate in Hard Turning of UHMWPE

2020 ◽  
Author(s):  
César Oswaldo Aguilera-Ojeda ◽  
Alberto Saldaña-Robles ◽  
Agustín Vidal-Lesso ◽  
Israel Martínez-Ramírez ◽  
Eduardo Aguilera-Gómez
Keyword(s):  
Surface Roughness ◽  
Predictive Model ◽  
Material Removal Rate ◽  
Surface Finish ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Turning Process

Abstract The surface finish of industrial components has an important role in their performance and lifetime. Therefore, it is crucial to find the cutting parameters that provide the best surface finish. In this work, an experimental study of the effect of cutting parameters on ultra-high molecular weight polyethylene (UHMWPE) by a turning process was carried out. Today, the UHMWPE polymer continues to find applications mainly in the automotive industry and biomechanics because it is resistant to impact and corrosive materials to use. A face-centered Central Composite Design (CCD) and Response Surface Methodology (RSM) were applied to evaluate the influence of the cutting speed (Vc), feed rate (f) and depth of cut (ap) of the turning operation on the Average Surface Roughness (Ra) and Material Removal Rate (MRR). Results allowed obtaining an adjusted multivariable regression model that describes the behavior of the Ra that depends on the cutting parameters in the turning process. The predictive model of Ra showed that it fits well with a correlation coefficient (R2) around 0.9683 to the experimental data for Ra. The ANOVA results for Ra showed that the feed is the most significant factor with a contribution of 42.3 % for the term f 2, while the speed and depth of cut do not affect Ra with contributions of 0.19% and 0.18%, respectively. A reduction of feed from 0.30 to 0.18 mm·rev−1 produces a decrease in surface roughness from 6.68 to 3.81 μm. However, if the feed continued to reduce an increase in surface roughness, a feed of 0.05 mm·rev−1 induces a surface roughness of 14.93 μm. Feeds less than 0.18 mm·rev−1 cause a heat generation during turning that increases the temperature in the process zone, producing surface roughness damage of the UHMWPE polymer. Also, the results for MRR demonstrated that all of the cutting parameters are significant with contributions of 31.4%, 27.4% and 15.4% to feed, speed, and depth of cut, respectively. The desirability function allowed optimizing the cutting parameters (Vc = 250 m·min−1, ap = 1.5 mm y f = 0.27 mm·rev−1) to obtain a minimum surface roughness (Ra = 4.3 μm) with a maximum material removal rate (MMR = 97.1 cm3·min−1). Finally, the predictive model of Ra can be used in the industry to obtain predictions on the experimental range analyzed, reducing the surface roughness and the manufacturing time of UHMWPE cylindrical components.

scholarly journals Optimization of Machining Parameters for Product Quality and Productivity in Turning Process of Aluminum

2021 ◽  
Vol 26 ◽  
Author(s):  
Sepideh Abolghasem ◽  
Nicolás Mancilla-Cubides
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Material Removal Rate ◽  
Manufacturing Process ◽  
Material Removal ◽  
Removal Rate ◽  
Decision Making Process ◽  
Machining Parameters ◽  
Turning Process ◽  
Productivity Rate

Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products.

Optimization of Process Parameters Using Taguchi Coupled Genetic Algorithm

2017 ◽  
pp. 67-93 ◽  
Author(s):  
Supriyo Roy ◽  
J. Paulo Davim ◽  
Kaushik Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Nsga Ii ◽  
Optimization Of Process Parameters ◽  
Cnc Lathe

In the era of mass manufacturing, Material removal rate and Surface Roughness are of primary concern even in manufacturing using contemporary CNC machines. In this work, L27 Orthogonal Array of Taguchi method is selected for three parameters (Depth of cut, Feed and Speed) with three different levels to optimize the turning parameters for Material Removal Rate and Surface Roughness on an EMCO Concept Turn 105 CNC lathe for machining SAE 1020 material with carbide tool. The MRR and SR are observed as the objective to develop the combination of optimum cutting parameters. The objectives were optimized using Taguchi, Grey Taguchi and NSGA-II. The result from these techniques was compared to identify the optimal values of cutting parameters for maximum MRR, minimum SR and best combination of both. This study also produced a predictive equation for determining MRR and SR for a given set of parameters outside the considered values. Thus, with the proposed optimal parameters it is possible to increase the efficiency of machining process and decrease production cost in CNC Lathe.

scholarly journals Optimization of Machining Parameters for MRR and Surface Roughness for 7024 AL-alloy in Pocket Milling Process

2019 ◽  
Vol 26 (1) ◽  
pp. 10-16
Author(s):  
Mustafa Mohammed Abdulrazaq ◽  
Adil Shabeeb Jaber ◽  
Ahmed Salman Hammood ◽  
Ahmed Ghazi Abdulameer
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Milling Process ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Machining Parameters ◽  
Good Surface ◽  
Good Surface Roughness

The objective of this work is the investigation of milling process variables which resulting in optimal values of the surface roughness and material removal rate during machining of 7024 Al-alloy. The machining operation implemented on C-TEK CNC milling machine. The effects of the selected parameters on the chosen characteristics have been accomplished using Taguchi’s parameter design approach; also ANOVA had been used to evaluate the contribution of each parameter on the process outputs. Different feed rates are used ranging from (60, 80 and 100) mm/min, found that high feed rates gives a high material removal rates and good surface roughness. On the other hand, using three levels of spindle speeds found that a higher spindle speeds gives better surface roughness with a little effect on MRR. The process results showed that maximum MRR achieved (2.40) mm3/min when machining feed rate (100) mm/min, spindle speed (1000) r.p.m, and depth of cut (0.6) mm while good surface roughness (0.41 µm) when machining feed rate (100) mm/min, spindle speed (1000) r.p.m, and depth of cut (0.2) mm. The level of importance of the machining parameters for material removal rate and surface roughness and is determined by using Taguchi designing experiments and the variance analysis (ANOVA).

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