Response surface methodology and artificial neural network-based models for predicting performance of wire electrical discharge machining of inconel 718 alloy

This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives

Performance investigation of integrated model of quarter car semi-active seat suspension with human model

In this paper, an integrated model of a semi-active seat suspension with a human model over a quarter is presented. The proposed eight-degrees of freedom (8-DOF) integrated model consists of 2-DOF for the quarter car model, 2-DOF for the semi-active seat suspension and 4-DOF for the human model. A magneto-rheological (MR) damper is implemented for the seat suspension. The fuzzy logic-based self-tuning (FLST) proportional–integral–derivative (PID) controller allows to regulate the controlled force on the basis of sprung mass velocity error and its derivative as input. The controlled force is tracked by the Heaviside step function which determines the supply voltage for the MR damper. The performance of the proposed integrated model is analysed, in-terms of human head accelerations, for several road profiles and at different speeds. The performance of the semi-active seat suspension is compared with the traditional passive seat suspension to validate the effectiveness of the proposed integrated model with a semi-active seat suspension. The simulation results show that the semi-active seat suspension improves the ride comfort significantly by reducing the head acceleration effectively compared to the passive seat suspension

Evaluation of copper-based alloy (C93200) composites reinforced with marble dust developed by stir casting under vacuum environment

Copper-based alloy (C93200) composites reinforced with a different weight percentage of marble dust particles (1.5, 3, 4.5, and 6 wt.%) were developed by stir casting method under vacuum environment. By using this type of reinforcement, it was possible to detect a suitable material for bearing applications. The manufactured material was characterized for its mechanical properties using a micro-hardness tester. A universal INSTRON-5967 machine was used to detect the yield and tensile strength. Further the hardness features were measured using a Walter Uhl model machine, whereby the wear characteristics were simulated under the pin-on-disc tribometer under different working conditions in ambient temperature (23 °C). Next, the preference selection index (PSI) technique that considers multi-criteria decision-making was proposed to validate which material was the best candidate. For the selection of material criteria, some specific material intrinsic properties—such as, density, void fraction, hardness resistance along with tensile, compressive, and flexural strength—were proposed and the surface characteristics linked to friction coefficients along wear properties. It was found that the novel composite material containing 4.5 wt.% of marble dust provided the best combination of properties and is a suitable candidate material for bearing applications

Thermal based surface modification techniques for enhancing the corrosion and wear resistance of metallic implants: a review

For successful implantation, biomaterials need excellent corrosion and wear resistance in the body environment, a combination of high strength and low modulus, appropriate ductility and non-cytotoxic. Due to their unique mechanical properties and durability, metallic biomaterials have been widely utilised in clinical applications, such as joint replacements, dental root implants, orthopaedic fixation devices, and cardiovascular stents. However, the wear and corrosion of metallic implants determine the service period of implantation owing to the release of incompatible metal ions into the body that may induce inflammation and allergic reactions. This review article focuses on the effect of corrosion and wear on the implant and the human body and mechanisms to enhance corrosion and wear resistance. Initially, metallic biomaterials and their properties are presented. Then, the reasons for implant failure are highlighted with a focus on details of wear and corrosion mechanisms. Finally, various thermal-based surface modification techniques and their applications in enhancing corrosion and wear resistance of Titanium-based biomaterials are presented. Surface modification techniques are currently discussed as the “best solution” to improve corrosion and wear resistance performance, providing superior tissue compatibility and encouraging osseointegration

Investigating a novel Ag/ZnO based hybrid nanofluid for sustainable machining of inconel 718 under nanofluid based minimum quantity lubrication

Sustainable machining, with implementation of eco-friendly dry and minimum quantity lubrication (MQL) methods, has gained attraction of researchers and engineers to solve the environmental and health allied issues caused by bulk usage of the conventional cutting fluids. Nanofluids have recently became more obvious choice as a cutting fluid in MQL applications owing to their superior thermo-physical properties. In this work, a novel hybrid nanofluid, consisting of Ag coated ZnO (Ag/ZnO) nanoparticles in ethylene glycol base, has been proposed as cutting fluid for MQL application. Initially, the Ag/ZnO hybrid nanoparticles were synthesized by a chemical precursor method. Then, synthesized nanoparticles were blended in ethylene glycol at several volume concentrations (Φ = 0.05 % to 0.2 %). The nanofluids were characterized for their thermo-physical properties and stability criterion, and then a nanofluid with the best performance was selected for nanofluid based MQL (NFMQL) experiments. The performance of NFMQL was compared with dry, MQL environments during milling of difficult-to-cut Inconel 718 superalloy with PVD coated carbide inserts. Taguchi L9 orthogonal array was incorporated for experimental design to investigate the effect of cutting speed, feed rate and machining environment on the machining performance in terms of the average surface roughness and cutting temperature. Analysis of variance shown that the cutting environment contributed to the average surface roughness and cutting temperature by 24.52 % and 44.74 %, respectively. As compared to dry and MQL condition, the NFMQL improved the surface finish by 23.5 % and 13.07 %, respectively, and reduced the cutting temperature by 15.38 % and 8.56 %, respectively, owing to proposed hybrid nanofluids enhanced lubrication and heat dissipation properties. Furthermore, the field emission scanning electron microscopic (FESEM) images of used cutting tool inserts reveal that the NFMQL condition induces the minimum tool wear as compared with MQL and dry environments. Finally, the multi-response optimization was achieved through the implementation of Taguchi grey relational analysis (TGRA) with the optimum combination of cutting speed of 30 mm/min, feed rate of 0.036 mm/tooth, and NFMQL cutting environment