scholarly journals Influence of cutting parameters on cutting forces and surface roughness in dry turning of Al using PCD and different coated tools

Sadhana ◽  
2019 ◽  
Vol 44 (8) ◽  
Author(s):  
R Kumar ◽  
S K Pattnaik ◽  
J K Minz ◽  
S Padhi ◽  
S K Sarangi
Keyword(s):  
Surface Roughness ◽  
Cutting Forces ◽  
Cutting Parameters ◽  
Dry Turning ◽  
Coated Tools

Machinability Investigation in Dry Turning of Ti–6Al–4V with a Novel TiB2–ZrC Cermet Tool Using RSM

2020 ◽  
Author(s):  
Yikun Yuan ◽  
Wenbin Ji ◽  
Shijie Dai
Keyword(s):  
Surface Roughness ◽  
Tool Life ◽  
Wear Mechanism ◽  
Cutting Forces ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Processing Efficiency ◽  
Response Factors ◽  
Dry Turning ◽  
Tool Wear Mechanism

Abstract To ensure accuracy and improve the processing efficiency of Ti–6Al–4V alloys, dry turning experiment of Ti–6Al–4V was carried out using a novel TiB2–ZrC cermet tool. The tool was reinforced by nanoscale VC additive and exhibited excellent hardness and fracture toughness. Response surface methodology (RSM) was used in the experiment to verify and evaluate the cutting performance of TiB2–ZrC cermet tool. The cutting forces and surface roughness (Ra) were selected as the optimization objective. Then the analysis of variance (ANOVA) was employed to ascertain the effective cutting parameters on response factors and demonstrate accuracy of the models. It was found that the effective cutting parameters on surface roughness was feed rate, while cutting depth significantly affected cutting forces. And the confirmation experiments showed that the predicted values coincide with experimental values nearly. Based on the optimized cutting parameters, the tool life and tool wear mechanism were investigated. When the vc, ap and f were 100 mm/min, 0.16 mm, 0.1 mm/rev, respectively, the cutting length and tool life could reach to 3233 m and 29.4 min, respectively, due to the excellent wear resistance and stability of TiB2–ZrC cermet tool at high cutting temperature. In this case, the main wear mechanism was adhesive wear and diffusion wear.

Surface Roughness Predictive Model of UNS A97075 Aluminum Pieces Obtained by Dry Turning Tests Based on the Cutting Forces

2012 ◽  
Vol 217-219 ◽  
pp. 1628-1635 ◽  
Author(s):  
Beatriz De Agustina ◽  
Eva María Rubio ◽  
Miguel Ángel Sebastián
Keyword(s):  
Experimental Study ◽  
Surface Roughness ◽  
Predictive Model ◽  
Cutting Forces ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Dry Turning ◽  
Frequency Domains ◽  
Range Of Values ◽  
Roughness Measurements

The present work shows an experimental study for a first approach of a surface roughness predictive model of UNS A97075 aluminum pieces obtained by dry turning tests based on the cutting forces. In a first step, a design of experiments (DOE) 25 was employed to analyse the influence of the cutting parameters and type of tool on the surface roughness with the objective to find out a combination of cutting conditions that allow obtaining a range of values of surfaces roughness according to the aeronautical specifications requierements. The factors considered for this design were the feed rate, spindle speed, depth of cut, type of tool (nose radious) and machined length (zone of the workpiece where the surface roughness measurements are taken). The obtained data was analysed by means of the analysis of variance (ANOVA) method. And secondly, with the previous selected conditions selected it was developed by multiple regression a model to predict the surface roughness by measuring the cutting forces generated during the dry turning tests of aluminum alloy UNS A97075 pieces. The predictive model of surface roughness obtained includes statistical values calculated from the forces sygnal in time and frequency domains.

scholarly journals Evaluation of Cutting-Tool Coating on the Surface Roughness and Hole Dimensional Tolerances during Drilling of Al6061-T651 Alloy

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1783
Author(s):  
Hamza A. Al-Tameemi ◽  
Thamir Al-Dulaimi ◽  
Michael Oluwatobiloba Awe ◽  
Shubham Sharma ◽  
Danil Yurievich Pimenov ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Cutting Tool ◽  
Good Practice ◽  
Cutting Tools ◽  
Statistical Design ◽  
Cutting Parameters ◽  
Statistical Design Of Experiments ◽  
Hole Quality ◽  
Coated Tools ◽  
Tool Coating

Aluminum alloys are soft and have low melting temperatures; therefore, machining them often results in cut material fusing to the cutting tool due to heat and friction, and thus lowering the hole quality. A good practice is to use coated cutting tools to overcome such issues and maintain good hole quality. Therefore, the current study investigates the effect of cutting parameters (spindle speed and feed rate) and three types of cutting-tool coating (TiN/TiAlN, TiAlN, and TiN) on the surface finish, form, and dimensional tolerances of holes drilled in Al6061-T651 alloy. The study employed statistical design of experiments and ANOVA (analysis of variance) to evaluate the contribution of each of the input parameters on the measured hole-quality outputs (surface-roughness metrics Ra and Rz, hole size, circularity, perpendicularity, and cylindricity). The highest surface roughness occurred when using TiN-coated tools. All holes in this study were oversized regardless of the tool coating or cutting parameters used. TiN tools, which have a lower coating hardness, gave lower hole circularity at the entry and higher cylindricity, while TiN/TiAlN and TiAlN seemed to be more effective in reducing hole particularity when drilling at higher spindle speeds. Finally, optical microscopes revealed that a built-up edge and adhesions were most likely to form on TiN-coated tools due to TiN’s chemical affinity and low oxidation temperature compared to the TiN/TiAlN and TiAlN coatings.

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.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

2020 ◽  
Vol 36 ◽  
pp. 28-46
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Salah Eddine Mechraoui ◽  
Mohamed Athmane Yallese ◽  
Mustapha Temmar
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 316L ◽  
Cutting Power ◽  
Dry Turning ◽  
Optimization Of Cutting Parameters

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

Optimization of Machining Parameters During Turning of Tungsten Heavy Alloys Using Taguchi Analysis

2019 ◽  
Author(s):  
Chithajalu Kiran Sagar ◽  
Amrita Priyadarshini ◽  
Amit Kumar Gupta
Keyword(s):  
Surface Roughness ◽  
Cutting Forces ◽  
Metal Removal ◽  
Cutting Parameters ◽  
Machining Parameters ◽  
Taguchi Analysis ◽  
Heavy Alloys ◽  
Wide Range ◽  
Tungsten Heavy Alloys ◽  
Machining Parameter

Abstract Tungsten heavy alloys (WHAs) are ideally suited to a wide range of density applications such as counterweights, inertial masses, radiation shielding, sporting goods and ordnance products. Manufacturing of these components essentially require machining to achieve desired finish, dimensions and tolerances However, machining of WHAs are extremely challenging because of higher values of elastic stiffness and hardness. Hence, there is a need to find the right combination of cutting parameters to carry out the machining operations efficiently. In the present work, turning tests are conducted on three different grades of WHAs, namely, 90WHA, 95WHA and 97WHA. Taguchi analysis is carried out to find out the most contributing factor as well as optimum cutting parameters that can give higher metal removal rate (MRR), lower surface roughness and lower cutting forces. It is observed that feed rate is the most prominent factor with percentage contribution varying in the range of 46–61%; whereas cutting speed has least effect on cutting forces, especially for 95WHA and 97WHA. Optimum values of forces, surface roughness and MRR and the corresponding machining parameters to be taken are presented. It is observed that 95W WHA has slightly better machinability as compared to other two grades since it gives highest MRR with lowest cutting forces and surface roughness values. The optimum machining parameter settings, so predicted, can be utilized to machine WHAs efficiently for manufacture of counter weights and inertial masses used in aerospace applications.

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