Measurement and optimization of cutting forces during M200 TS milling process using the response surface methodology and dynamometer

Daniyan, Ilesanmi; Tlhabadira, Isaac; Daramola, Oluyemi Ojo; Phokobye, Solomon Ntshiniki; Siviwe, M.; Mpofu, Khumbulani

doi:10.1016/j.procir.2020.05.050

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…Initially, with a change in feed rate value from 0.1 to 0.125 mm/rev, an abrupt increase in the interface temperature is visualized, followed by interface pressure due to the higher load acting on the tool, which obviously increases the pressure and friction between the tool and the workpiece. Similarly, milling forces tend to increase with the increase in the feed rate [36][37][38].…”

Section: Resultsmentioning

confidence: 99%

Numerical Modelling, Simulation, and Analysis of the End-Milling Process Using DEFORM-3D with Experimental Validation

Senthilkumar

Hariharan

Tamizharasan

et al. 2022

Advances in Materials Science and Engineering

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In this present work, finite element analysis (FEA)-based simulation of end-milling of AISI1045 steel using tungsten carbide tool was performed using DEFORM-3D simulation software. Usui tool wear model, Johnson-cook material model and adaptive remeshing are considered during machining simulation. The impact of machining variables rate of feed, tool speed, and depth of cut was investigated, and the best integration of variables was distinguished for lower cutting temperature, principal stress, cutting forces, effective stresses, tool wear, and effective strain. The obtained results were correlated using experimentation in a vertical machining center attached with a Kistler tool dynamometer with data acquisition setup for capturing the cutting forces, and an infrared (IR) thermometer was used to measure the cutting temperature, and a comparison was done. Results showed a good correlation. There is a relationship between experimental and numerical results, and simulation findings can be utilised for interpreting the influence of machining parameters.

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Section: Resultsmentioning

confidence: 99%

Numerical Modelling, Simulation, and Analysis of the End-Milling Process Using DEFORM-3D with Experimental Validation

Senthilkumar

Hariharan

Tamizharasan

et al. 2022

Advances in Materials Science and Engineering

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“…Other authors discussed about the importance of recycling the rolling stock equipment to avoid landfill and environmental decay. In addition, in order to enhance the cost, material and energy sustainability during the machining operations, the process design, modelling and optimization of the machining operations and parameters as well as the optimization of the energy consumed during the machining operations have been recommended ( Daniyan et al., 2019c , 2019e , 2020d , 2021 ; Tlhabadira et al., 2021 ). Table 2 presents the summary of the findings in this study.…”

Section: Resultsmentioning

confidence: 99%

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

Daniyan

Mpofu

Ramatsetse

et al. 2021

Heliyon

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The life cycle models are critical in the assessment of the performance of a product from the design phase to its end of life (EoL). With the quest for manufacturing sustainability with respect to energy, process, material, and environment friendliness as well as the clamour for circular economy which emphasizes zero tolerance for waste, there is a need for a critical review of the life cycle of machine tool employed for machining operations and product development. The objective of this study is to evaluate the efficient way of managing the machine tools throughout its lifecycle. Several studies have been conducted in analysing the life cycle of the machine tools and different strategies were employed for its design, manufacture, use, maintenance and recovery at the end of life. The common approach to ensure environmental sustainability was established when comparing the literature studied. From the articles reviewed 60% applied life cycle assessment (LCA) methodology to reduce energy consumption and enhance environmental sustainability, while 40% employed other assessment tools. In this study an integrated life cycle and cyber physical machine tool model is proposed.

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“…The feasible combination of the process parameters was done using the Response Surface Methodology (RSM). The choice of the RSM was based on its ability to iteratively study the cross-effect of process parameters [33][34][35]. The numerical experimentation comprises a four factor experimental design, which varied at different levels in the following ranges; feed rate (250 − 350 mm/rev), spindle speed (1000 − 3000 rpm), cutting speed (100 − 300 m/min) and depth of cut (0.3 − 0.9 mm).…”

Section: The Response Surface Methodologymentioning

confidence: 99%

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

et al. 2020

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Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

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Measurement and optimization of cutting forces during M200 TS milling process using the response surface methodology and dynamometer

Cited by 21 publications

References 14 publications

Numerical Modelling, Simulation, and Analysis of the End-Milling Process Using DEFORM-3D with Experimental Validation

Numerical Modelling, Simulation, and Analysis of the End-Milling Process Using DEFORM-3D with Experimental Validation

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

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