Scientia Iranica (23453605)31(20)pp. 1880-1888
Modeling and determining the optimal conditions for the Jet Electrochemical Machining (Jet-ECM) process is critical. In this study, a hybrid approach combining numerical and Design of Experiments (DOE) methods have been applied to model and determine the optimal conditions for Jet-ECM. The voltage (V), inner tool diameter (I), initial machining gap (G), and electrolyte conductivity (C) are considered input variables. Additionally, dimensional accuracy (E) and machining depth (D) are response variables. Twenty-seven numerical simulations have been performed using the Box–Behnken design to implement the Response Surface Methodology (RSM). Consequently, two mathematical models have been obtained for these response variables. The effects of the input variables on the response variables are investigated using statistical techniques such as variance analysis. Furthermore, the desirability function approach has been applied to determine the optimal conditions for dimensional accuracy and depth of machining. The results show that the optimal values for achieving maximum depth of machining while maintaining a dimensional accuracy of 0.05 mm are as follows: electrolyte conductivity of 8 S/m, voltage of 36.9 V, initial machining gap of 200 μm, and inner tool diameter of 0.4 mm. © 2024 Sharif University of Technology.
Computational Particle Mechanics (21964386)10(1)pp. 143-153
Nowadays, various methods are being formed on new composites and nanocomposite compounds. Investigating the properties of nanocomposites and finding their optimal properties pave the way for a better use of them. In this study, first, mechanical molecular dynamics method is used to investigate mechanical properties of aluminum/carbon nanotubes (Al-CNT) nanocomposite, then, the effect of temperature change, strain rate, and chirality of nanotubes on the elastic modulus and ultimate stress of nanocomposite have been investigated. However, in order to simultaneously investigate these three parameters on the properties of nanocomposite and to find an optimal point for the elastic modulus and ultimate stress, the experimental design method for optimization was used. Derringer method was used to determine optimal parameters for simultaneous optimization of two response variables, namely elastic modulus and ultimate stress. It can be concluded that the optimal conditions occur simultaneously at 50 K, strain rate 0.01, and chirality (5,5), in which the value of the elastic modulus is 156 GPa and the ultimate strain value is 13.7 GPa and simultaneous minimum value of elastic modulus and ultimate stress occur at 650 K, strain rate 0.0205, chirality (3,3), in which the value of elastic module is 94 GPa and the ultimate strain value is 6.44 GPa. © 2022, The Author(s) under exclusive licence to OWZ.
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (20412983)236(23)pp. 11322-11329
Molecular dynamics simulation is among the most significant methods in nanoscale studies. This paper studied the effect of strain rate, temperature, and nanotube chirality on the stress-strain behavior of aluminum/silicon nanotubes (SiNTs) using molecular dynamics simulation. Ultimate tensile stress and Young’s modulus of the nanocomposite were evaluated using molecular dynamics simulation. According to the results, Young’s modulus of the nanocomposite decreased with increasing temperature. Also, Young’s modulus decreased by increasing the strain rate. Next, an experimental approach was used based on the Box–Behnken design. According to the input parameters and the experimental approach, the number of simulations in the software was 39 runs. Overall, it is concluded that the optimal conditions were created at a temperature of 50 K, a strain rate of 0.01/ps, and chirality of (5,5), leading to the elasticity modulus of 137 GPa and the ultimate tensile stress of 11.8 GPa. © IMechE 2022.
Ali, M.,
Basti a., A.,
Jamali a., A. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (20412983)234(17)pp. 3436-3446
Selection of optimal and suitable process parameters is a crucial issue in manufacturing processes especially in electrochemical machining (ECM). Since the utmost target is to find suitable machining parameters for gaining desired machining performances, a new hybrid approach has been applied for inverse modelling of ECM process. Four machining inputs, i.e. voltage, tool feed rate, electrolyte flowrate and concentration; and two machining responses, i.e. surface roughness (Ra) and material removal rate (MRR) are presented as input variables and responses, respectively. In the proposed approach, firstly, comprehensive mathematical equations have been established based on response surface methodology (RSM). The two machining performances are modeled in this step with machining parameters. Then, the differential evolution (DE) algorithm has been used for Pareto-based multi-objective optimization. Finally, group method of data handling (GMDH)-type neural networks is used through the Pareto table for inverse modelling. As a result, four models have been developed for each of the four machining parameters; therefore, each machining parameters is determined according to the machining performance as two new design variables. The results demonstrated that the suggested method is a helpful and promising tool for inverse modelling and determining such important relationships between optimized responses and input variables. © IMechE 2020.
Ali, M.,
Basti a., A.,
Jamali a., A. Latin American Applied Research (03270793)47(4)pp. 157-162
Selection of appropriate machining parameters which result in desired outcomes plays a key role in effective utilization of the electrochemical machining (ECM) process. In this paper, in order to correlate between ECM process parameters and cost functions, comprehensive mathematical models were first determined based on response surface methodology (RSM). Voltage, tool feed rate, electrolyte flow rate and concentration of NaNO3 solution were considered as the machining parameters while material removal rate (MRR) and surface roughness (Ra) were considered as cost functions. To do this, three scenarios of machining performances, Ra ≤ 0.9μm, 0.9μm ≤ Ra ≤ 1.8μm, and 1.8μm ≤ Ra ≤ 2.7μm, were considered for optimization search based on desirability functions. The goal is to find the optimum set of machining parameters in order to maximize the MRR while keeping Ra in specified ranges simultaneously. The results show that the errors between experimental and anticipated optimal values are less than 8.16% and hence confirm the effectiveness of the proposed approach. © 2003-2012 Latin American Applied Research Journal.
Ali, M.,
Basti a., A.,
Jamali a., A. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering (20413009)231(6)pp. 1114-1126
Electrochemical machining is a unique prevalent nonconventional manufacturing process used in different industries involving various process parameters, which greatly influence machining performance. Therefore, selection of proper and optimal parameters setting is a challenging issue. In this paper, differential evolution algorithm is applied to look for the optimum solution to this problem. Four parameters, i.e. voltage, tool feed rate, electrolyte flow rate, and electrolyte concentration; and two machining criteria, i.e. material removal rate and surface roughness (Ra) are considered as input variables and responses, respectively. The main purpose is to maximize material removal rate and minimize Ra to achieve better machining performance. In this way, comprehensive mathematical models have first been developed using response surface methodology through experimentation based on central composite design plan. Then, differential evolution algorithm has been utilized for optimizing the process parameters; both single- and multiobjective optimizations are considered, and optimal Pareto front is determined. Finally, optimization result of a trade-off design point in the Pareto front of Ra and material removal rate was also verified experimentally. This machined surface was examined with field-emission scanning electron microscope images. The results showed that the proposed approach is an effective and suitable strategy for optimization of the electrochemical machining process. © 2016, © IMechE 2016.
