Optimizing Power Consumption for Sustainable Dry Turning of Treated Aluminum Alloy

Nur, Rusdi; Kurniawan, Denni; Noordin, M.Y.; Izman, S.

doi:10.1016/j.promfg.2015.07.096

Cited by 21 publications

(10 citation statements)

References 10 publications

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“…The degree of predictable performance of a product or process in the presence of noise factors can be defined from S/N ratio values. For each type of machining output, the higher the S/N ratio, the better the result [19]. In this study, lower F c and k s values are important from the point of view of energy consumption.…”

Section: Signal To Noise (S/n) Response Analysis For Cutting Effectsmentioning

confidence: 90%

“…This is defined as the ratio of the mean squared deviations to the mean squared error. This analysis was performed with a confidence level of 95% [19]. The importance of the input parameters in ANOVA analysis was identified by comparing the F-values of each input parameters.…”

Section: Analysis Of Variance (Anova) For Tool Lifementioning

confidence: 99%

“…Therefore, appropriate selection of machining parameters reduces machining costs, increases tool life, and reduces power consumption [18]. The optimization of cutting parameters is usually difficult and requires knowledge of machining and empirical equations related to tool life, force, and power [19]. Selvaraj et al [20] utilized the Taguchi method to define the optimal cutting parameters in the turning of two different grades of duplex stainless steel.…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Youssef

El-Hofy

Ahmed

2018

JMMP

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Due to their high ductility, high durability, and excellent corrosion resistance, stainless steels are attractive materials for a variety of applications. However, high work hardening, low thermal conductivity, and high built-up edge (BUE) formation make these materials difficult to machine. Rapid tool wear and high cutting forces are the common problems encountered while machining these materials. In the present work, the application of Taguchi optimization methodology has been used to optimize the cutting parameters of the drilling process for machining two stainless steels: austenitic AISI 304 and duplex AISI 2205 under dry conditions. The machining parameters which were chosen to be evaluated in this study are the tool material, cutting speed, and feed rate, while, the response factors to be measured are the tool life (T), cutting force (F c ), and specific cutting energy (k s ). Additionally, empirical models were created for predicting the T, F c and k s using linear regression analysis. The results of this study show that AISI 2205 stainless steel has a shorter tool life, a higher cutting force, and a higher specific cutting energy than AISI 304 stainless steel. In addition, the Taguchi method determined that A 3 B 1 C 1 and A 3 B 3 C 1 (A 3 = TiN-coated twist drill, B 1 = 13 m/min, B 3 = 34 m/min, C 1 = 0.12 mm/rev) are the optimized combination of levels for the best tool life and the lowest cutting force, respectively. Meanwhile, the optimized combination of levels for all three control factors from the analysis, which provides the lowest specific cutting energy, was found to be A 3 B 1 C 3 (A 3 = TiN-coated twist drill, B 1 = 13 m/min, C 3 = 0.32 mm/rev) for both stainless steels.

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Section: Signal To Noise (S/n) Response Analysis For Cutting Effectsmentioning

confidence: 90%

Section: Analysis Of Variance (Anova) For Tool Lifementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Youssef

El-Hofy

Ahmed

2018

JMMP

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“…The equipment used to make tools and components of the turbine runner were lathes machine, CNC milling machine, drilling machine, bending machine, cutting plates, grinding machine, electric welding machine, dial indicators, and balancing tool. The machining process was also performed by [9][10][11] in experiments. Additionally, lathe machine was employed for turning of aluminum alloy.…”

Section: Materials and Equipmentmentioning

confidence: 99%

Welding fixture design for the production of cross-flow turbine runner

Muchtar

Rasyid

Nur

2018

AIP Conference Proceedings

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The industrial sectors in Indonesia is currently growing very rapidly because it is supported by the development of technology in the field of manufacturing. One of the most frequently used machining operations in the manufacturing industry is welding operations. Welding operation is the most efficient and efficient method to combine two or more metal components. The product quality of welding can be obtained by using the production tool (fixture). The purpose of this research is to improve the quality of turbine runner product by using the welding fixture. The method was to design and make welding fixture of turbine runner welding, test the concentricity, straightness of the dish, the stability of the dish to the shaft, and the alignment of the blades to the shaft, and to analyze the deviation of measurement results. Based on the result of welding fixture design of cross-flow turbine runner component, a prototype welding fixture which can facilitate the assembling process of cross-flow turbine runner component was obtained. The conclusions of this research were: 1) The quality of the cross-flow turbine runner product can be increased along with the small geometrical deviation on cross-flow turbine runner using accelerator tool, 2). The maximum deviation of the runner disc placement to the axis of the axis on the 300mm diameter runner is 1.2mm and the runner of 200mm with diameter of 0.68mm, and 3). The assembling time of the turbine runner components is shorter using weld assembly tools with better accuracy.

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“…However, the specific cutting energy is very low because large volumes of material are removed due to the high feed rates and cutting speeds employed in order to achieve higher productivity [22,23]. The specific cutting energy tends to increase as the hardness and mechanical strength of machined materials increases and decreases as the feed rate and cutting speed increase, since the former increases the material removal rate while the latter decreases the cutting force.…”

Section: B Power Generated When Machining Aluminum Alloysmentioning

confidence: 99%

Machining of Aluminum alloys: a review

Praneeth¹

2017

IJRASET

154

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The use of aluminum alloys in manufacturing industry has increased significantly in recent years. This is because primarily to their ability to combine lightness and strength in a single material. Concomitant to this growth, the machining of aluminum alloys has enormously increased in volumetric proportionsso that the chip volume represents up to 80 % of the original volume of the machined material in certain segments of the industry, like aerospace. In this context, knowledge of the characteristics of machinability of aluminum alloys is essential to provide industry and researchers with information that allows them to make the right decisions when they come to machining this fantastic material. The purpose of this review is to compile relevant information about the characteristics of machinability of aluminum alloys into a single document.

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Optimizing Power Consumption for Sustainable Dry Turning of Treated Aluminum Alloy

Cited by 21 publications

References 10 publications

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Welding fixture design for the production of cross-flow turbine runner

Machining of Aluminum alloys: a review

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