Sustainable Manufacturing and Parametric Analysis of Mild Steel Grade 60 by Deploying CNC Milling Machine and Taguchi Method

Azim, Shakir; Noor, Sahar; Khalid, Qazi Salman; Khan, Aqib Mashood; Pimenov, Danil Yurievich; Ahmad, Imran; Babar, Abdur Rehman; Pruncu, Catalin I.

doi:10.3390/met10101303

Cited by 15 publications

(7 citation statements)

References 41 publications

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“…To identify the significant effects of the input parameters and their relationship on the output parameters, one of the statistical techniques [ 23 ]—the one-way Analysis of Variance (ANOVA) presented by Tank et al [ 24 ], Köklü et al [ 25 ], Kumar et al [ 26 ], as well as Palaniappan et al [ 27 ]—was used. Statistical calculations were carried out at a confidence level of α = 0.05.…”

Section: Resultsmentioning

confidence: 99%

Dimensional Analysis of Workpieces Machined Using Prototype Machine Tool Integrating 3D Scanning, Milling and Shaped Grinding

et al. 2020

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In the literature, there are a small number of publications regarding the construction and application of machine tools that integrate several machining operations. Additionally, solutions that allow for such integration for complex operations, such as the machining of shape surfaces with complex contours, are relatively rare. The authors of this article carried out dimensional analysis of workpieces machined using a prototype Computerized Numerical Control (CNC) machine tool that integrates the possibilities of 3D scanning, milling operations in three axes, and grinding operations using abrasive discs. The general description of this machine tool with developed methodology and the most interesting results obtained during the experimental studies are given. For a comparative analysis of the influence of the machining method on the geometric accuracy of the test pieces, an Analysis of Variance (ANOVA) was carried out. The obtained results show that for four considered features (deviations of flatness, vertical parallelism, opening dimensions, and opening cylindricality), no statistically significant differences were detected. For the evaluation criteria, the probability level p exceeded the assumed confidence level α = 0.05 and ranged from p = 0.737167 to p = 0.076764. However, such differences were found for two others—a dimensional deviation between flat surfaces (p = 0.010467) and horizontal parallelism deviation (p = 0.0)—as well as for the quality of the machined surface defined by four surface texture parameters: Ra (p = 0.831797), Rt (p = 0.759636), Rq (p = 0.867222), and Rz (p = 0.651896). The information obtained by the ANOVA will be useful for the elimination the weaknesses of the prototype machine tool, further analysis of technological strategies, and to find possible benefits of integrating machining operations.

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Section: Resultsmentioning

confidence: 99%

Dimensional Analysis of Workpieces Machined Using Prototype Machine Tool Integrating 3D Scanning, Milling and Shaped Grinding

et al. 2020

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“…In the turning process, the surface rough-ness of the work piece that is not suitable is a problem that is often encountered [7]. Inappropriate parameters result in the roughness of the work piece not being as de-sired [8]. With the background of these problems, ideas arise to analyze the value or level of roughness of turning results using the Taguchi method.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Analysis and Optimization for ST 37 Steel in Lathe Operation using Design of Experiments Method

Siti Nurrohkayati,

Syach,

Takdir

2024

J. Phys.: Conf. Ser.

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The machining process is one of the processes used to process steel materials. The value or level of material roughness is one of the values that is considered in the machining process, according to the desired roughness value. One example is in the turning process, spindle speed, depth of feed, and feed motion are factors that can affect the level of roughness of a material. The level of roughness that is not according to demand is one of the problems in the manufacturing industry, especially in the results of the turning process. One of the causes of the discrepancy in the roughness value is because the parameters used are not suitable. Therefore, this study aims to determine the optimal parameters in the turning process to obtain the optimal roughness value, and to determine the parameters that most influence the roughness of the turning process. In this study, ST 37 steel was used. This study used an experimental design method with Orthogonal Array L8. The experiment was carried out with 5 control factors and 3 noise factors. Each factor has 2 levels and is repeated 8 times. In this study using a conventional lathe and for roughness testing using a Surface Roughness Tester. Based on the results of data analysis, the movement factor has the highest influence on the surface roughness value. The ranking factors that affect the roughness value are as follows: spindle speed of 345 rpm, feed motion of 0.051 mm/rad, depth of cut 1 mm, dry cooling, and automatic sledding. The Design of Experiments (Taguchi) method use quality characteristics to determine the results of the analysis. In this study, the quality characteristic is smaller the better. Smaller the better characteristics is where the smaller value, then better the quality. In variations of the spindle rotation speed and feed motion, we get where if the spindle speed is high and the feed motion is low it will produce a low level of roughness, whereas if the spindle speed is low and the feed movement is high it will produce a high level of roughness.

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“…Therefore, there is always a need for an effective optimization method to determine the optimum combination of parameters [6][7][8]. This is important in terms of the sustainability assessment, which covers the consumed energy, machining costs, and eco-friendly manufacturing [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140

et al. 2021

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The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.

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Sustainable Manufacturing and Parametric Analysis of Mild Steel Grade 60 by Deploying CNC Milling Machine and Taguchi Method

Cited by 15 publications

References 41 publications

Dimensional Analysis of Workpieces Machined Using Prototype Machine Tool Integrating 3D Scanning, Milling and Shaped Grinding

Dimensional Analysis of Workpieces Machined Using Prototype Machine Tool Integrating 3D Scanning, Milling and Shaped Grinding

Surface Roughness Analysis and Optimization for ST 37 Steel in Lathe Operation using Design of Experiments Method

Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140

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