Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA

Imani, Lila; Henzaki, Ali Rahmani; Hamzeloo, Seyed Reza; Davoodi, Behnam

doi:10.1177/0954405419889204

Cited by 51 publications

(28 citation statements)

References 37 publications

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“…The encountered discontinuous chips result in preventing tool wear and accelerate the life of the tool which significantly increases the machined surface quality of the work material. 21,40 With the influence of high-speed machining of hard-to-cut materials, the heat generation will increase and accordingly results in decrease of plastic deformation and significantly decreasing the friction due to increment of temperature. 41 The cutting fluid applied along the flank face takes away the high heat generation and accordingly increases lubrication effect at the rake face of the cutting tool.…”

Section: Resultsmentioning

confidence: 99%

Analysis of electrostatic solid lubricant spray process parameters during turning Ti-6Al-4V alloy

Gunda¹,

Narala

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Titanium alloys are considered as hard-to-cut material due to its relatively high resistance to corrosion, low thermal conductivity and high chemical reactivity. Effective penetration of sufficient amount of lubricant in the metalworking process is of utmost importance to (a) reduce frictional resistance at the high-temperature tool–workpiece interface and (b) avert the formation of built-up edge in machining process. Traditionally, the lubricant forms a thin fluid film to reduce the friction. In order to reduce or eliminate coolant usage and to apply lubri-coolant efficiently at tool–chip–workpiece interface, a novel near-dry machining technique called electrostatic charged solid lubricant spray system has been developed. In electrostatic charged solid lubricant spray technique, the aerosolized micro-droplet particles were sprayed from the nozzle tip which uses a phenomenon called contact-charge electrification principle. The quick penetration and wettability of charged atomized droplet particles are impinged with high velocity at the tool–chip interface. The focus of the present experimental research is to analyse the optimum lubrication process parameters of the experimental set-up in terms of effectively applying cutting fluid in the machining zone. The study shows that the desired atomization and machining performance of the electrostatic charged solid lubricant spray technique can be obtained by governing different spray parameters (lubricant flow rate, air pressure, spray nozzle position, nozzle distance, electrostatic voltage). The results revealed that when the charged lubricants are applied with high velocity and large number of droplets, it will have significant influence on surface roughness and cutting force. The fabricated electrostatic charged solid lubricant spray technique provides us with an efficient and economic machining solution by applying solid lubricants effectively at tool–material interface during turning of Ti-6Al-4V alloy material. The present approach and the obtained results will help in better understanding of the machinability of hard-to-cut materials in manufacturing industries.

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Section: Resultsmentioning

confidence: 99%

Analysis of electrostatic solid lubricant spray process parameters during turning Ti-6Al-4V alloy

Gunda¹,

Narala

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Hanafi et al 5 also employed the ANN method in predicting the cutting forces for the turning processes of carbon fiber-reinforced polymer composites. Imani et al 6 also adopted an ANN algorithm combined with experimental data to build a prediction model for investigating the cutting forces and surface roughness for machining Inconel 738, as well as optimizing the processing parameters with the genetic algorithm (GA). Moreover, Cus et al 7 used the ANN algorithms to effectively develop the cutting forces estimation models of ball-end milling processes.…”

Section: Introductionmentioning

confidence: 99%

The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050

Wei

Wang

Zhou

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The cutting forces will generally suffer massive complex factors, such as material deformation, tool eccentricity and system vibration, which will inevitably induce many great difficulties in accurately modeling the cutting force predictions that are very significant to investigate cutting processes. Therefore, the genetic algorithm optimized back-propagation and particle swarm optimization neural networks will be adopted to effectively construct cutting force prediction models. In these two back-propagation prediction models, the main milling parameters will be defined into their input vectors, and the transient milling forces along three different directions will be selected as their output vectors, then the implicit relationships between input and output vectors can be directly generated through practically training and learning these two built back-propagation models with a set of experimental milling force data. Meanwhile, the finite element analysis method will be also used to predict milling forces through programming two easy-to-operate plug-ins that can efficiently construct finite element analysis models, conveniently define processing parameters, and automatically perform mesh generation. Subsequently, the milling forces predicted by the established genetic algorithm optimized back-propagation and particle swarm optimization back-propagation models will be analytically compared with finite element analysis simulations and experiments; also the stress distribution and chip formations of finite element analysis and experiments will be comparatively investigated. Finally, the obtained results clearly indicate that these two back-propagation models built by artificial neural networks can well agree with finite element analysis simulations and experiments, but the particle swarm optimization back-propagation model is superior to the genetic algorithm optimized back-propagation model, which clearly demonstrate the particle swarm optimization back-propagation model has higher efficiencies and accuracies in predicting the average and transient cutting forces for different milling processes on aluminum alloy 7050.

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“…The interpolative models of the SCE, arithmetical mean roughness (Ra), and means roughness depth (Rz) were developed in terms of the spindle speed (S), nose radius (r), f, and a for the dry milling of SKD61 material [4]; and the findings showed that a set of feasible optimal solutions could be employed to obtain a low SCE, a smooth surface, and high MRR. The artificial neural network (ANN) was applied to develop the models of the machining force (Fc) and SR regarding the axial depth of cut (ap), v, and f for the milling of Inconel 738 [5];…”

Section: Introductionmentioning

confidence: 99%

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

Nguyen

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The burnishing process is used to enhance the machining quality via improving the surface finish, surface hardness, wear-resistance, fatigue, and corrosion resistance, and it is mostly used in aerospace, biomedical, and automotive industries to improve reliability and performance of the component. The combined turning and burnishing process is therefore considered as an effective solution to enhance both machining quality and productivity. However, the trade-off analysis between energy consumption, surface characteristics, and production costs has not been well-addressed and investigated. This study presents an optimization of the compressed air assisted-turning-burnishing (CATB) process for aluminum alloy 6061, aimed to decrease the energy consumption as well as surface roughness and to enhance the Vicker hardness of the machined surface. The machining parameters for consideration include the machining speed, feed rate, depth of cut, burnishing force, and the ball diameter. The improved Kriging models were used to construct the relations between machining parameters and the technological response characteristics of the machined surface. The optimal machining parameters were obtained utilizing the desirability approach. The energy based-cost model was developed to assess the effectiveness of the proposed CATB process. The findings showed that the selected optimal outcomes of the depth of cut, burnishing force, diameter, feed rate, and machining speed are 0.66 mm, 196.3 N, 8.0 mm, 0.112 mm/rev, and 110.0 m/min, respectively. The energy consumption and surface roughness are decreased by 20.15% and 65.38%, respectively, while the surface hardness is improved by 30.05%. The production cost is decreased by 17.19% at the optimal solution. Finally, the proposed CATB process shows a great potential to replace the traditional techniques which are used to machine non-ferrous metals.

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Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA

Cited by 51 publications

References 37 publications

Analysis of electrostatic solid lubricant spray process parameters during turning Ti-6Al-4V alloy

Analysis of electrostatic solid lubricant spray process parameters during turning Ti-6Al-4V alloy

The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

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