A study on machinability evaluation of Al-Gr-B&lt;SUB align="right"&gt;4C MMC using response surface methodology-based desirability analysis and artificial neural network technique

Ponnuvel, S.; Senthilkumar, N.

doi:10.1504/ijrapidm.2019.097030

Cited by 20 publications

(4 citation statements)

References 33 publications

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“…A cumulative approach that comprises of both statistical and mathematic approaches for model formulation and problem analysis; an output of attention is affected by various factors with an aim of optimizing the outputs is RSM [20,21]. In utmost problems in RSM, the arrangement of connection amid the dependent and independent factors is unfamiliar [22].…”

Section: Response Surface Methodologymentioning

confidence: 99%

Temperature evaluation and performance comparison of various cooling techniques in drilling of grade 5 titanium alloy

et al. 2021

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Heat dissipation is a foremost issue in machining of materials and to overcome this different cooling techniques are adopted to improve the surface integrity and diminish microstructural damage and to effectively flush chips produced. In this research, machinability of titanium grade 5 (Ti-6Al-4V) was carried out through drilling process considering three different cooling procedures like cryogenic cooling (CO2), palm oil based minimum quantity lubrication (MQL) and wet/flood cooling with general cutting fluid (emulsifiers). Experiments were prearranged in par with Taguchi?s technique and a suitable L18 orthogonal array was formulated and the outputs obtained viz., interface temperature and cutting forces (torque/thrust force) were analyzed by adopting response surface methodology (RSM). For interface temperature, type of coolant is the most influencing factor, whereas for cutting forces, the impact of feed rate, drill speed and type of coolant are significant. Response surfaces were fitted for the outputs and subsequently a higher polynominal regression equation was developed for prediction. The optimum condition obtained for constraint multiobjective optimization using desirability approach was: drill speed of 42.49 m/min, feed rate of 0.05 mm/rev and cooling type as cryogenic cooling. The main objecive is to minimize the interface temperature and torque subjected to the constraint thrust force (?1500 N). Confirmation experiment performed with optimum condition displays a significant enhancement in output results.

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Section: Response Surface Methodologymentioning

confidence: 99%

Temperature evaluation and performance comparison of various cooling techniques in drilling of grade 5 titanium alloy

et al. 2021

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“…Figure 5 presents the functional elements of FIS for converting the input to crisp output using rule-base. Number of membership functions (MF) and set values depends upon the required response [26]. Sugeno and Mamdani implication methods are popular methods available in fuzzy systems.…”

Section: Fuzzy Interference Systemmentioning

confidence: 99%

Drilling Parameters Analysis on In-Situ Al/B4C/Mica Hybrid Composite and an Integrated Optimization Approach Using Fuzzy Model and Non-Dominated Sorting Genetic Algorithm

Palanikumar¹,

Krishnan

Natarajan

et al. 2021

Metals

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In-situ hybrid metal matrix composites were prepared by reinforcing AA6061 aluminium alloy with 10 wt.% of boron carbide (B4C) and 0 wt.% to 6 wt.% of mica. Machinability of the hybrid aluminium metal matrix composite was assessed by conducting drilling with varying input parameters. Surface texture of the hybrid composites and morphology of drill holes were examined through scanning electron microscope images. The influence of rotational speed, feed rate and % of mica reinforcement on thrust force and torque were studied and analysed. Statistical analysis and regression analysis were conducted to understand the significance of each input parameter. Reinforcement of mica is the key performance indicator in reducing the thrust force and torque in drilling of the selected material, irrespective of other parameter settings. Thrust force is minimum at mid-speed (2000 rpm) with the lowest feed rate (25 mm/min), but torque is minimum at highest speed (3000 rpm) with lowest feed rate (25 mm/min). Multi-objective optimization through a non-dominated sorting genetic algorithm has indicated that 1840 rpm of rotational speed, 25.3 mm/min of feed rate and 5.83% of mica reinforcement are the best parameters for obtaining the lowest thrust force of 339.68 N and torque of 68.98 N.m. Validation through experimental results confirms the predicted results with a negligible error (less than 0.1%). From the analysis and investigations, it is concluded that use of Al/10 wt.% B4C/5.83 wt.% mica composite is a good choice of material that comply with European Environmental Protection Directives: 2000/53/CE-ELV for the automotive sector. The energy and production cost of the components can be very much reduced if the found optimum drill parameters are adopted in the production.

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“…There is a wide variety of decision support systems focused on materials engineering [ 18 ]; however, very few really take advantage of technologies based on artificial intelligence [ 9 , 10 , 19 , 20 , 21 , 22 ]. Although several studies that use machine learning to address metallotechnics and the properties of metals have been published [ 23 , 24 ], aluminum alloys have hardly been investigated considering their tempers from an industrial perspective using these tools [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

2020

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In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.

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A study on machinability evaluation of Al-Gr-B<SUB align="right">4C MMC using response surface methodology-based desirability analysis and artificial neural network technique

Cited by 20 publications

References 33 publications

Temperature evaluation and performance comparison of various cooling techniques in drilling of grade 5 titanium alloy

Temperature evaluation and performance comparison of various cooling techniques in drilling of grade 5 titanium alloy

Drilling Parameters Analysis on In-Situ Al/B4C/Mica Hybrid Composite and an Integrated Optimization Approach Using Fuzzy Model and Non-Dominated Sorting Genetic Algorithm

Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

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