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.

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.

Research on Microscopic Grain-Workpiece Interaction in Grinding through Micro-Cutting Simulation, Part 2: Factorial Study

2009 ◽  
Vol 76-78 ◽  
pp. 15-20 ◽  
Author(s):  
Lan Yan ◽  
Xue Kun Li ◽  
Feng Jiang ◽  
Zhi Xiong Zhou ◽  
Yi Ming Rong
Keyword(s):  
Cutting Force ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Shear Angle ◽  
Depth Of Cut ◽  
Specific Cutting Force ◽  
Single Grain

The grinding process can be considered as micro-cutting processes with irregular abrasive grains on the surface of grinding wheel. Single grain cutting simulation of AISI D2 steel with a wide range of cutting parameters is carried out with AdvantEdgeTM. The effect of cutting parameters on cutting force, chip formation, material removal rate, and derived parameters such as the specific cutting force, critical depth of cut and shear angle is analyzed. The formation of chip, side burr and side flow is observed in the cutting zone. Material removal rate increases with the increase of depth of cut and cutting speed. Specific cutting force decreases with the increase of depth of cut resulting in size effect. The shear angle increases as the depth of cut and cutting speed increase. This factorial analysis of single grain cutting is adopted to facilitate the calculation of force consumption for each single abrasive grain in the grinding zone.

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.

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.

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