Optimization of Surface Roughness in Drilling Medium‐Density Fiberboard with a Parallel Robot

Ayyıldız, Elmas Aşkar; Ayyıldız, Mustafa; Kara, Fuat

doi:10.1155/2021/6658968

Cited by 32 publications

(15 citation statements)

References 26 publications

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“…Optimization has gained great importance in the manufacturing industry. By using ANOVA and RSM, analyses can be made in all areas of production [35][36][37]. For the hard turning process, various optimization methods (RSM, Taguchi, artificial neural networks, genetic algorithm, and particle swarm optimization) were used by researchers [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods

Şahin

Esen

2021

Advances in Materials Science and Engineering

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In recent years, hard turning, an alternative to grinding, which provides low cost and good surface quality, has become an attractive method to the manufacturers. In this experimental study, AISI H11 hot work tool steel that has been hardened up to 50 HRC was subjected to hard turning tests with coated carbide tooling. The analyses were carried out by applying response surface methodology with the analysis of variance method. A total of 27 experiments were modeled utilizing 3 3 full factorial design and were carried out using a CNC lathe. The effects of the cutting parameters on surface roughness, energy consumption, electric current value, and sound intensity level were investigated. Optimum cutting parameters and levels were determined according to these optimum values. The relationship between cutting parameters and output variables was analyzed with two-dimensional and three-dimensional graphics. The results show that while the most effective parameter on the surface roughness was the feed rate (88.62%), the most effective parameter on the sound intensity level was the cutting speed (44.92%). In addition, the cutting depth was the most effective parameter on both electric current (52.20%) and energy consumption (46.15%). Finally, regression coefficients were determined as a mathematical model, and it was observed that this estimated model gave results that were very similar to those achieved with real experiment (correlation values: 97.64% for surface roughness, 98.72% for energy consumption, 97.22% for electric current value, and 91.38% for sound intensity level).

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Section: Introductionmentioning

confidence: 99%

Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods

Şahin

Esen

2021

Advances in Materials Science and Engineering

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“…As a result, second degree regression model proved to be successful in predicting surface roughness. The result of the study confirms the literature (Akkuş ve Asilturk, 2011;Meral et.al., 2011;Akkuş, 2021;Ayyıldız et. al., 2021).…”

Section: Discussionmentioning

confidence: 99%

Modeling of Surface Roughness in Milling of Ti-6al-4v Alloy Using Regression Analysis

Toprak

Çolak

Bayhan

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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In this study, Ti-6Al-4V was machined under high pressure cooling conditions. Cutting parameters which were assumed as independent variables are consist of 4 different levels of cutting speed (Vc: 50-70-90-110 m/min), feed rate (f: 0.05-0.1-0.15-0.2 mm/rev) and cutting fluid pressure (P: 6-100-200-300 bar). By using SPSS 20 software, regression equations of surface roughness relative to cutting parameters was obtained as linear, second degree and linear logarithmic. Second degree multiple regression model showed best results of estimation. In the model, 95 percent of the surface roughness alterations can be explained by independent variables. Correlation between experimental data and the model was calculated as 0.975. As a result, second degree regression model proved to be successful in predicting surface roughness. The result of the study confirms the literature. When models are compared the most important parameter that affects surface roughness was observed as the feed rate. The results of the study confirms the literature.

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“…From Tables 4 and 5, the percentage contribution ratio con rms pulse ON time as the most in uencing parameter towards MRR and Ra [32]. From the graph, it is understood that the error percentage is less than 1 which is too minimum to prove the accuracy of the experiment.…”

Section: Analysis Of Variancementioning

confidence: 92%

Parametric Optimization of Wire Electrical Discharge Machining in AA7075 Metal Matrix Composite

Raju¹,

Balakrishnan²,

Priya

et al. 2022

Advances in Materials Science and Engineering

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Aluminium 7075 metal matrix composite reinforced with silicon nitride was fabricated using the stir casting technique. Composite fabricated was machined and subjected to wire electrical discharge machining to study the significant process parameter. Taguchi design of experiment using L9 orthogonal array was selected with three factors, current, pulse ON time, and wire feed at three levels. Influencing process parameters were identified using analysis of variance. Pulse ON time of 130 μs, current of 20 A, and wire feed of 1 mm/min were identified as optimized parameters for higher material removal rate. For a good surface finish, the optimized parameter was identified as 130 μs pulse ON time, 20 A current, and wire feed rate of 2 mm/min. Pulse ON time was identified as an influencing parameter followed by the current in achieving better material removal rate with good surface finish, whereas wire feed has no influence on the output parameters. Results of experimentation showed improved machining characteristics for higher pulse ON time and current for machining the synthesized composite through the Taguchi design of the experiment.

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Optimization of Surface Roughness in Drilling Medium‐Density Fiberboard with a Parallel Robot

Cited by 32 publications

References 26 publications

Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods

Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods

Modeling of Surface Roughness in Milling of Ti-6al-4v Alloy Using Regression Analysis

Parametric Optimization of Wire Electrical Discharge Machining in AA7075 Metal Matrix Composite

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