Rapid prototyping (RP) or layered manufacturing (LM) technologies have been extensively used to manufacture prototypes composed mainly of plastics, polymers, paper, and wax due to the short product ...development time and low costs of these technologies. However, such technologies, with the exception of selective laser melting and sintering, are not used to fabricate metallic products because of the resulting poor life, short cycle, poor surface finish, and low structural integrity of the fabricated parts. The properties endowed by these parts do not match those of functional parts. Therefore, extensive research has been conducted to develop new additive manufacturing (AM) technologies by extending existing RP technologies. Several AM technologies have been developed for the fabrication of metallic objects. These technologies utilize materials, such as Ni-, Al-, and Ti-based alloys and stainless steel powders, to fabricate high-quality functional components. The present work reviews the type of materials used in laser-based AM processes for the manufacture of metallic products. The advantages and disadvantages of processes and different materials are summarized, and future research directions are discussed in the final section. This review can help experts select the ideal type of process or technology for the manufacturing of elements composed of a given alloy or material (Ni, Ti, Al, Pb, and stainless steel).
In this study, a novel data-driven direct automatic tuning scheme is proposed for the general form of fixed-structure digital controllers of hybrid systems. The controller is directly auto-tuned ...while it interacts with the real plant; no modelling or plant identification procedure is involved. The auto-tuning problem is formulated as a controller parameter optimisation problem, which is solved using a quasi-Newton method. A new method for calculating the gradient of the objective function from closed-loop experiment data is proposed to reduce the sensitivity to the initial values of the parameters. Domain knowledge of controller design based on pole placement and internal model principle is introduced as a priori information to further improve the performance of the auto-tuning. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme and performances of different objective functions are evaluated. The performance of the proposed scheme is compared with the standard relay feedback and iterative feedback tuning methods. Reliable controllers can be obtained automatically by using the proposed scheme; the only remaining task of the control engineer is to estimate the order of the plant by counting the number of independent energy storage elements.
Electric vehicles that run on batteries have a major disadvantage of temperature abnormalities when operated at extreme working conditions. Therefore, thermal management of battery pack is essential ...to ensure its safety and performance. There are three operation strategies of thermal management air-based, liquid-based, and phase change material-based battery thermal management systems (BTMSs). Optimization studies on BTMSs have been focused mainly on the structural parameters as compared to the operating parameters. In liquid cooled multichannel flow-type BTMSs, equal flow rates are employed in all the channels. However, only a few studies have focused on variable coolant flow rates in channel-type liquid BTMS and its optimization. In this paper, a multichannel cold plate-based liquid BTMS is proposed for Lithium-ion battery pack comprising of two prismatic cells operating at 1 C discharge rate. Multi-objective optimization (MOO) technique coupled with computational fluid dynamics (CFD) simulations is used for obtaining optimal mass flow rate combination of coolant in the channels for reducing the power consumption of the BTMS without compromising on its thermal performance. Response surface methodology is adopted for the sensitivity analysis of the operating parameters and Multi Objective Genetic Algorithm (MOGA) approach is used to obtain the optimal solution set. The results showed a maximum reduction of 66.33%, 38.10%, and 43.56% for mass flow rate, maximum pressure and power consumption respectively in comparison to equal mass flow rate case whereas the temperature rise and temperature distribution of the battery system remain within the nominal range.
Energy production systems such as proton-exchange membrane fuel cell (PEMFC) has a promising future in the cleaner energy market due to zero emissions. Rubber pad forming (RPF) process of metallic ...bipolar plates of PEMFCs is gaining attention among the researchers. Studies based on design of experiments have been conducted to find the crucial parameters of the forming process. These methods are based on the assumptions of the model structure, correlated residuals, etc., which can cause uncertainty in estimation ability of the model on unseen data. Therefore, the present study focuses on the design of robust models of these parameters for PEMFCs using an optimization approach of genetic programming (GP). The inputs from the experiments considered in GP are radius, the friction coefficient, the filling factor and the minimum thickness. Experiments on PEMFCs validates the performance of the GP models. Further, the relationships between the two inputs and the three outputs for PEMFCs are generated as well as the contributions of each input to each of the output. Optimization of the models generated by GP can further determine the forming quality of metallic bipolar plates of PEMFCs by an appropriate setting of the two inputs.
•Problem on optimization of forming process of bipolar plates of PEMFC is taken.•Experiments on determining the minimum thickness of bipolar plates were conducted.•Genetic Programming (GP) methodology is then proposed to build models.•Additional experiments validate the models and investigates its robustness.•2-D and 3-D surface analysis of model provides insights into working of process.
The recycling process of materials from used and wasted printed circuit boards plays an important role in electronic waste management. These waste printed circuit boards (PCBs) hold metals such as ...copper, aluminium, nickel, and magnesium. The efficient recovery process of such metals from waste PCBs is needed for recycle and possible reuse for manufacturing of products. The metal recovery process is complex and, multidimensional and costly to perform. In addition, the efficient (maximum) recovery of metals exhibit higher dependence on determination of optimum combination of inputs in the recovery process from waste PCBs. Therefore, this work illustrated the ability of four predictive modelling methods (Analysis of Variance, Genetic Programming, Artificial Neural Network and Generalized Neural Network) to model complex suspension electrolysis process (recovery process) and their comparative analysis on recovery of copper metal from waste PCBs. Experiments were designed based on variations of three design/input parameters such as concentration of sulfuric acid, concentration of copper sulphate and current density. The comparative analysis of the four methods mentioned above reveals that Generalized Neural Network performed the best with coefficient of determination value at 0.92. 3-D surface contour analysis showed that a lower concentration of sulfuric acid (0.3–0.6) coupled with higher concentrations of copper sulfate (∼0.8–1) yielded a higher percentage of copper. However, current density brought an increase in copper recovery only when there was a corresponding increase in the concentration of sulfuric acid or copper sulphate. Thus, the optimal conditions to gain the maximum yield of copper obtained through this method were concluded as follows: concentration of copper sulphate value at 31.4 g/L, concentration of sulfuric acid at 112 g/L and current density at 3 A/dm2.
•Problem on maximum recovery of Cu from electrolysis process from WPCBs is undertaken.•Procedure comprising Experiment coupled Predictive modelling is proposed.•3-D surface analysis reveals that a lower H.2SO4 (0.3-0.6) and higher CuSO4 (∼0.8-1) yields higher percentage of Cu•Optimization is performed to obtain the maximum yield value of Cu.
•Problem of Battery capacity estimation in uncertain environment is undertaken.•Two inputs, temperature and discharge rate, is considered for modeling capacity.•Genetic programming (GP) with variant ...of its objective functions is proposed.•SRM based GP model is able to estimate capacity accurately.•Global and 3D analysis suggests temperature is vital input factor for estimation.
Past studies on battery models is focussed on formulation of physics-based models, empirical models and fusion models derived from the battery pack data of electric vehicle. It is desirable to have an explicit, robust and accurate models for battery states estimation in-order to ensure its proper reliability and safety. The present work conducts a brief survey on battery models and will propose the evolutionary approach of Genetic programming (GP) for the battery capacity estimation. The experimental design for GP simulation comprises of the inputs such as the battery temperature and the rate of discharge. Further, the seven objective functions in GP approach is designed by introducing the complexity based on the order of polynomial. This step will ensure the precise functions evaluation in GP and drives the evolutionary search towards its optimum solutions. The design and analysis of the GP based battery capacity models involves the statistical validation of the seven objective functions based on error metrics with 2-D and 3-D surface plots. The results conclude that the GP models using Structural risk minimization (SRM) objective function accurately estimate the battery capacity based on the variations of the inputs. 2-D and 3-D surface analysis of the GP model reveals the increasing–decreasing nature of temperature-battery capacity curve with temperature the dominant input. The battery capacity model obtained using SRM as an objective function in GP is robust and thus can be integrated in the electric vehicle system for monitoring its performance and ensure its safety.
•PEMFC was designed based on six operating conditions (conductivity of electrode, etc).•Problem of determining the optimum operating conditions of PEMFC is undertaken.•A New Support Vector Machine ...based Model is used to determine relations of conditions.•Optimization of model results in maximum power density of 870mW/cm2 of PEMFC.•Experimental data validates the model and its performance is found satisfactory.
Polymer electrolyte membrane fuel cells (PEMFCs) are considered as an alternative source of power generation due to its high efficiency and environmental harmlessness. Studies were conducted to find out the relation between power density and operating parameters, such as operating temperature, fuel cell pressure, the relative humidity of anode, the relative humidity of cathode, porosity of gas diffusion electrode (GDE), conductivity of GDE. However, less focus was paid on determining the operating conditions which will maximize the power density of PEMFCs. The present work proposes the optimal design of a power density model for PEMFCs based on the artificial intelligence approach of support vector machine (SVM). The formulated power density model is in good agreement with the experimental data. Simulation profiler is then generated to verify its insensitivity to the variations in the operating conditions and the inferences on relationships between power density and operating parameters are investigated. The results show that the fuel cell pressure and conductivity of GDE are the two most dominant inputs in effective functioning of PEMFC. Optimization of the formulated model reveals that the operating temperature of 86.2K, the fuel cell pressure of 3.44atm, the relative humidity of anode of 50%, the relative humidity of cathode of 64.4%, the porosity of GED of 0.5 and the conductivity of GDE of 997.7s/m gives the maximum power density value of 870mW/cm2 of PEMFC.
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