Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method

Tlhabadira, Isaac; Daniyan, Ilesanmi; Machaka, Ronald; Machio, Christopher N; Masu, Leonard; VanStaden, L.R.

doi:10.1007/s00170-019-03452-4

Cited by 46 publications

(21 citation statements)

References 26 publications

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“…(2010) Application on machining technologies Exploratory Daniyan et al. (2019c) CNC Milling Empirical Tlhabadira et al. (2019) CNC Milling Empirical Daniyan et al.…”

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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“…(2010) Application on machining technologies Exploratory Daniyan et al. (2019c) CNC Milling Empirical Tlhabadira et al. (2019) CNC Milling Empirical Daniyan et al.…”

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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“…On the contrary, the magnitude of the surface roughness was observed to decrease with an increase in the spindle speed and vice versa. This may be due to the fact that a decrease in the depth of cut may result in an increase in the surface area to volume ratio of the work piece, thereby promoting frictional activities at the tool-work piece interface and subsequently, increase in the surface roughness of the material [26]. Furthermore, each revolution of the machine's spindle represents a smaller circumferential distance, therefore, an increase in the spindle speed may cause the circumferential distance to decrease.…”

Section: No /mentioning

confidence: 99%

“…The surface roughness index is used to indicate the level of surface irregularities or finish during the machining operations. It is a parameter, which determines the quality of the final product and its ability to meet the design specifications and its functional requirements [26]. The correlation between temperature and surface roughness is that when the cutting temperature exceeds the optimum, the surface roughness of the work piece will increase.…”

Section: Introductionmentioning

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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“…The final result demonstrates that f z = 0.08 mm/tooth, a p = 1.62 mm, V c = 331 m/min, and a e = 15.49 mm are the optimum level for minimum roughness and maximum material removal. Tlhabadira et al [10] proposed a combined mathematical model that uses inputs from numerical and experimental results to express surface roughness as a function of machining parameters. The numerical model estimated potential contact pressure, stress, and deformation of a cutting tool while cutting operation, and experimental results, in the form of the Taguchi method were used to understand the influence of the cutting speed, feed, and the depth of cut on final surface roughness.…”

Section: Milling Operationsmentioning

confidence: 99%

Surface roughness analysis in milling machining using design of experiment

Moayyedian

Mohajer

Kazemian³

et al. 2020

SN Appl. Sci.

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The objective of this paper is to evaluate the effect of different machining parameters on the surface roughness of Hardox 600 (hard and tough steel with high wear resistance and hardness of 600 HBW) in the milling process using the Taguchi optimization method. The selected process parameters are feed rate, spindle speed, the depth of cut, and radial immersion. Based on the number of parameters and their levels L 9 , an orthogonal array of the Taguchi was chosen. Surface roughness data were collected for nine experiments. Signal to noise (S/N) ratio and analysis of variance calculation were conducted to determine the optimum level and percentage of contribution of each parameter. Furthermore, the mathematical model was created to determine the predicted value of S/N ratio, and the experiments were implemented to justify the mathematical model. It is found that the margin error for the mathematical model and the experimental result was 5.5%, resulting from any uncontrollable parameters affecting the machining process. It is also identified that the radial immersion was the most significant parameter with 45.33% of contribution in the milling procedure of steel with high hardness (Hardox 600). Hence, the surface roughness was mostly influenced by radial immersion (D), followed by the depth of cut (C), spindle speed (B), and finally, feed rate (A) respectively based on the level of their significance. The optimal level of the selected parameters for the minimum surface roughness value is the radial immersion at level 1, depth of cut at level 1, spindle speed at level 2, and feed rate at level 2.

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Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method

Cited by 46 publications

References 26 publications

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

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)

Surface roughness analysis in milling machining using design of experiment

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