Experimental investigation on microstructure, mechanical properties and dust emission when milling Al-20Mg2Si-2Cu metal matrix composite with modifier elements

Marani, Mohsen; Songmené, Victor; Kouam, Jules; Zedan, Yasser

doi:10.1007/s00170-018-2491-y

Cited by 12 publications

(6 citation statements)

References 44 publications

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“…Cutting speed, feed rate, and depth of cut were preferred as 550 m/min, 0.05 mm/rev, and 1.5 mm in the turning tests, respectively. These values were determined in line with the literature 20,27,28 and cutting insert manufacturer's recommendation. Vibration sig-nals in the course of turning were received by the Kistler 9257B dynamometer (Switzerland made) dynamometer and transferred to an amplifier.…”

Section: Experimental Devices and Turning Process Conditionsmentioning

confidence: 99%

Experimental research on machinability characteristics of Al-9Si alloy: Effect of Sr and Mg additives

Hekìmoğlu

Bayraktar

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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In this study, the effects of strontium (Sr) and magnesium (Mg) elements in certain rates added to Al-9Si alloy manufactured using permanent mold casting technique on the structural, mechanical, and machinability properties of these alloys were investigated. While microstructural images of the alloys were obtained with an optical microscope, hardness, tensile strength, and elongation to fracture were determined by conventional methods. The resultant cutting force ( F) and average surface roughness ( Ra) were measured as experimental outputs in the turning process. It was observed that Al-9Si alloy comprised of α-Al, eutectic Al-Si, β, and primary Si phases. The addition of Sr to the Al-9Si alloy led to the formation of the Al4Sr phase and the transformation of the β phase into the δ phase. The addition of Mg to Al-9Si-0.1Sr alloy caused the δ phase to transform into the π phase. The hardness of the tested alloy increased significantly with the Mg addition. The yield strength and tensile strength of them increased with both Sr and Mg additions. On the other hand, elongation to fracture value of the Al-9Si alloy increased with Sr addition while it decreased with the Mg addition. The F and Ra values obtained from the Al-9Si alloy increased with Sr addition in the turning process while decreased with Mg addition. The results obtained from turning tests were discussed based on the mechanical properties of alloys.

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Section: Experimental Devices and Turning Process Conditionsmentioning

confidence: 99%

Experimental research on machinability characteristics of Al-9Si alloy: Effect of Sr and Mg additives

Hekìmoğlu

Bayraktar

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…With the innovation in technology, there are advanced optimization techniques such as recurrent neural network (RNN) to solve the time series problems [12], support vector machine (SVM) to forecast the values from different sectors [13], artificial neural network (ANN) [14], convolutional neural network (CNN) [15], fuzzy logic (FL) [16],…”

Section: Introductionmentioning

confidence: 99%

Machinability investigation of Chromoly steel using TiN/Al₂O₃/TiCN/TiOCN multilayer coated tungsten carbide tool inserts with artificial neural network methodology

Singh¹,

Bharatish²,

Rajkumar³

et al. 2022

Applied Research

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Enhancing the machining efficiency of hardened steel using four‐layer coated tungsten carbide tools has gained a lot of interest among manufacturing engineers. This paper reports the machining performance of AISI 4140 steel using TiN/Al2O3/TiCN/TiOCN multilayer coated tungsten carbide tool inserts. The impact of cutting speed, depth of cut, and the nose radius on machining force, tool chip interface temperature, surface roughness, and chip forms was ascertained using full factorial machining experiments and artificial neural network (ANN) methodology. The machining force evaluated using Logistic Sigmoid activation function resulted in the highest root mean square error (RMSE) of 4.076 and regression value of 0.998. The maximum error between the ANN predicted results and experimental results for machining force, cutting temperature and surface roughness was about 2.4%, 5.3%, and 2.07%, respectively. The best architecture for ANN was “3‐6‐4” which had a coefficient of regression value of 0.97. However, in case of chip form, some of the ANN predicted values could not be mapped to ISO standard chip form and hence prediction accuracy of 80% was achieved.

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“…They found that Bi-containing ally improved machinability with decreasing cutting force value during the machining process. Barzani et al [10] studied the effects of modifiers on machinability of Al-Mg 2 Si composite. It was found that additives improved mechanical properties such as impact and tensile tests and consequently improved machinability of the composite with decreasing cutting force values.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

et al. 2021

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This study investigated the predicted cutting force model of a turning operation for Al–Si–Cu cast alloy modified with modifiers based on adaptive neuro-fuzzy inference system (ANFIS) approach. Feed rate, cutting speed and Silicon spacing were considered as the input parameters. A series of turning experiments were conducted at various feed rates and cutting speeds. The prediction result showed that the ANFIS model successfully predicted the cutting force value in terms of cutting speed, feed rate and Si spacing. A mathematical model was proposed to describe the cutting force changes during the machining of Al–Si–Cu cast alloy. Moreover, the addition of Bismuth into the base alloy decreased the cutting force compared to other refinement elements.

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Experimental investigation on microstructure, mechanical properties and dust emission when milling Al-20Mg2Si-2Cu metal matrix composite with modifier elements

Cited by 12 publications

References 44 publications

Experimental research on machinability characteristics of Al-9Si alloy: Effect of Sr and Mg additives

Experimental research on machinability characteristics of Al-9Si alloy: Effect of Sr and Mg additives

Machinability investigation of Chromoly steel using TiN/Al₂O₃/TiCN/TiOCN multilayer coated tungsten carbide tool inserts with artificial neural network methodology

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

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Experimental investigation on microstructure, mechanical properties and dust emission when milling Al-20Mg2Si-2Cu metal matrix composite with modifier elements

Cited by 12 publications

References 44 publications

Experimental research on machinability characteristics of Al-9Si alloy: Effect of Sr and Mg additives

Experimental research on machinability characteristics of Al-9Si alloy: Effect of Sr and Mg additives

Machinability investigation of Chromoly steel using TiN/Al2O3/TiCN/TiOCN multilayer coated tungsten carbide tool inserts with artificial neural network methodology

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

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Machinability investigation of Chromoly steel using TiN/Al₂O₃/TiCN/TiOCN multilayer coated tungsten carbide tool inserts with artificial neural network methodology