Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM

Dental Material Selection for the Additive Manufacturing of Removable Complete Dentures (RCD)

This research addresses the development of a formalized approach to dental material selection (DMS) in manufacturing removable complete dentures (RDC). Three types of commercially available polymethyl methacrylate (PMMA) grades, processed by an identical Digital Light Processing (DLP) 3D printer, were compared. In this way, a combination of mechanical, tribological, technological, microbiological, and economic factors was assessed. The material indices were calculated to compare dental materials for a set of functional parameters related to feedstock cost. However, this did not solve the problem of simultaneous consideration of all the material indices, including their significance. The developed DMS procedure employs the extended VIKOR method, based on the analysis of interval quantitative estimations, which allowed the carrying out of a fully fledged analysis of alternatives. The proposed approach has the potential to enhance the efficiency of prosthetic treatment by optimizing the DMS procedure, taking into consideration the prosthesis design and its production route.

Optimization of WEDM Parameters While Machining Biomedical Materials Using EDAS-PSO

In the present work, an attempt has been made to study the influence of process parameters of the wire electric discharge machining (WEDM) process on the machining characteristics. The commercially pure titanium is machined by WEDM using brass wire as an electrode. The input parameters in this work were pulse on-time (A_on), pulse off-time (A_off), servo voltage (SV) and wire tension (WT). On the other hand, dimensional accuracy (DA), average surface roughness (R_a) and maximum surface roughness (R_z) were chosen as the response parameters. The empirical relations developed for response characteristics were solved collectively using Evaluation Based on Distance from Average Solution (EDAS) and Particle Swarm Optimization (PSO). The optimized setting for minimizing the surface irregularities while machining titanium alloy on WEDM is predicted as A_on: 8 μs; A_off: 13 μs; SV: 45 V; and WT: 8 N. Moreover, the predicted solution at the optimized parametric settings came out as DA: 95%; Ra: 3.163 μm; Rz: 22.99 μm; WL: 0.0182 g; and DR: 0.1277 mm. The validation experiments at the optimized setting showed the close agreement between predicted and experimental values. The morphological study by scanning electron microscopy (SEM) at the optimized setting revealed a significant reduction in surface defects such as micro cracks, micro cavities, globules and sub-surfaces, etc. In a nutshell, the study justified the effectiveness of EDAS-PSO in efficiently predicting the results for machining of pure titanium (Grade 2) using the WEDM process.

Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy

Nickel-based superalloys find their main use in missile engines, atomic devices, investigational aircraft, aerospace engineering, industrial applications, and automotive gas turbines, spacecraft petrochemical tools, steam power, submarines, and broader heating applications. These superalloys impose certain difficulties during the process fabrication owing to their levels of higher hardness. In the current study, the precise machining of Waspaloy was attempted through the wire electrical discharge machining (WEDM) technique. A multi-objective optimization has been performed, and the influence of multi-walled carbon nanotubes (MWCNTs) has been assessed using the passing vehicle search (PVS) algorithm. The effects of machining variables like current, T_off, and T_on were studied using the output measures of material removal rate (MRR), recast layer thickness (RLT), and surface roughness (SR). The Box-Behnken design was applied to generate the experimental matrix. Empirical models were generated which show the interrelationship among the process variables and output measures. The analysis of variance (ANOVA) method was used to check the adequacy, and suitability of the models and to understand the significance of the parameters. The PVS technique was executed for the optimization of MRR, SR, and RLT. Pareto fronts were derived which gives a choice to the user to select any point on the front as per the requirement. To enhance the machining performance, MWCNTs mixed dielectric fluid was utilized, and the effect of these MWCNTs was also analyzed on the surface defects. The use of MWCNTs at 1 g/L enhanced the performance of MRR, SR, and RLT by 65.70%, 50.68%, and 40.96%, respectively. Also, the addition of MWCNTs has shown that the machined surface largely reduces the surface defects.

Minimization of Surface Roughness and Temperature during Turning of Aluminum 6061 Using Response Surface Methodology and Desirability Function Analysis

Aluminum alloy is the second most abundant metal on Earth, known for its wide range of utilization in commercial goods due to its heat capacity and tensile strength. This study examines the effect of nose radius on the turning process. Further, it explores the implications of cutting parameters such as the cutting speed, the rate of feed, the cutting depth, and the nose radius of the tool. The trials were carried out with an Al 6061 workpiece and an Al₂O₃-coated carbide tool as the cutter, utilizing the response surface methodology. A mathematical model was developed to investigate the performance characteristics of the turning operation using the analysis of variance method. The multi-response desirability function analysis combines individual desirability values to create a composite desirability value. The ideal parameter levels were determined using the composite desirability value, and the significant influence of parameters was assessed. The obtained optimum surface roughness and temperature parameters are at a cutting speed of 116.37 m/min, a rate of feed of 0.408 mm/rev, a cutting depth of 0.538 mm, and a tool nose radius of 0.20 mm. The related ideal surface roughness and temperature values are 0.374 µm and 27.439 °C. The optimal overall desirability value is 0.829, close to the target response.

Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA

In the present study, the effect of alumina (Al₂O₃) nano-powder was investigated for the electrical discharge machining (EDM) of a Nitinol shape memory alloy (SMA). In addition to the nano-powder concentration, other parameters of pulse-on-time (T_on), pulse-off-time (T_off), and current were selected for the performance measures of the material removal rate (MRR), surface roughness (SR), and tool wear rate (TWR) of Nitinol SMA. The significance of the design variables on all the output measures was analyzed through an analysis of variance (ANOVA). The regression model term has significantly impacted the developed model terms for all the selected measures. In the case of individual variables, Al₂O₃ powder concentration (PC), T_off, and T_on had significantly impacted MRR, TWR, and SR measures, respectively. The influence of EDM variables were studied through main effect plots. The teaching-learning-based optimization (TLBO) technique was implemented to find an optimal parametric setting for attaining the desired levels of all the performance measures. Pursuant to this, the optimal parametric settings of current at 24 A, PC at 4 g/L, T_off at 10 µs, and T_on of 4 µs have shown optimal input parameters of 43.57 mg/min for MRR, 6.478 mg/min for TWR, and 3.73 µm for SR. These results from the TLBO technique were validated by performing the experiments at the optimal parametric settings of the EDM process. By considering the different user and application requirements, 40 Pareto points with unique solutions were generated. Lastly, scanning electron microscopy (SEM) performed the machined surface analysis. The authors consider this to be very beneficial in the nano-powder-mixed EDM process for appropriate manufacturing operations.

Integrated fuzzy AHP and TOPSIS as innovative student selection methodology at institutions of higher learning

BACKGROUND: The selection of students at academic institutions has been a challenging affair given multiple criteria that need to be considered by the institution. Additionally, multiple evaluators and decision makers are involved in the student selection process, rendering it inconsistent. The complexity and subjectiveness in such decisions making requires new and innovative approach in order to be more systematic and transparent. OBJECTIVE: This paper presents an innovative methodology for student selection for admission into an Institute of Higher Learning (IHL) using Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Drawing on the success of using these methods in other fields, this study applies the technique and principles on student selection process. METHOD: Fuzzy Analytical Hierarchy Process (FAHP) is used in determining the weights of the criteria by the decision makers which avoids the vagueness and inconsistencies in decision making process and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method ranks finds out the best alternative solution for student selection by calculating the relative closeness from the positive ideal solution. RESULTS AND CONCLUSION: This research finds using the hybrid method is effective in student selection for IHL and makes the process efficient and bias-free. This method can be applied to various fields and uses where multi-criteria decision making is involved.

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (T_on), and pulse-off-time (T_off) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and T_on were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm³/s at T_on of 95 µs, T_off of 9 µs, current of 6 A. Least SR was obtained at T_on of 35 µs, T_off of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

Surface Modification Techniques to Produce Micro/Nano-scale Topographies on Ti-Based Implant Surfaces for Improved Osseointegration

Titanium and titanium alloys are used as artificial bone substitutes due to the good mechanical properties and biocompatibility, and are widely applied in the treatment of bone defects in clinic. However, Pure titanium has stress shielding effect on bone, and the effect of titanium-based materials on promoting bone healing is not significant. To solve this problem, several studies have proposed that the surface of titanium-based implants can be modified to generate micro or nano structures and improve mechanical properties, which will have positive effects on bone healing. This article reviews the application and characteristics of several titanium processing methods, and explores the effects of different technologies on the surface characteristics, mechanical properties, cell behavior and osseointegration. The future research prospects in this field and the characteristics of ideal titanium-based implants are proposed.

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

Shape memory alloy (SMA), particularly those having a nickel-titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al₂O₃ nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (T_on), pulse-off time (T_off), and Al₂O₃ nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi's three levels L₉ approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al₂O₃ nanopowder was found to have the highest contributing effect of 76.13% contribution, T_on was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at T_on of 90 µs, T_off of 5 µs, and powder concentration of 2 g/L), the lowest SR value of 3.13 µm, and the lowest RLT value of 10.24 (both responses at T_on of 30 µs, T_off of 25 µs, and powder concentration of 2 g/L). A specific multi-objective Teaching-Learning-Based Optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effectiveness of both the regression model and the TLBO algorithms. Confirmatory trials have shown an extremely close relation which shows the suitability of both the regression model and the TLBO algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA. A considerable reduction in surface defects owing to the addition of Al₂O₃ powder was observed in surface morphology analysis.

Sustainability Assessment of a Low-income Building: A BIM-LCSA-FAHP-based Analysis

The construction industry is one of the most significant consumers of environmental resources worldwide. Faced with the need to produce new buildings, but without further burdening the environment, attempts to improve social, economic, and environmental indicators have turned attention to building construction in recent decades. The objective of this research is to develop a novel framework to assess the most sustainable choice of materials applied to the construction of low-income buildings, according to the three pillars of the Triple Bottom Line (TBL). A BIM-LCSA-FAHP-based model was proposed with the creation of nine different scenarios, where the materials of the structure (precast concrete, cast-in-place concrete, and structural masonry), painting (PVA water-based and acrylic), and roofing (ceramic and fiber cement tiles) varied. The proposed procedure consists of the elaboration of a 3D Building Information Modeling (BIM) model, for which the parameters described above were evaluated according to the Life Cycle Sustainability Assessment (LCSA)-TBL-based criteria, divided into ten sub-criteria, that includes: (1) environmental (acidification, eutrophication, global warming, ozone depletion, smog formation, primary energy, non-renewable energy, and mass total), (2) economic (construction cost) and (3) socio-political issues (community impact). Finally, the Fuzzy Analytical Hierarchy Process (AHP) was used as a multi-criteria decision-making technique that helps in aggregating and classifying the impacts of each scenario in a sustainability index (SI). Regarding the best option for low-income construction, the results indicated that precast concrete when combined with acrylic paint and fiber cement tiles (scenario 3) proved to be the most advantageous and achieved first place in the sustainability index (SI) developed in this work. This methodology is replicable for different construction typologies and several categories of materials, making it a robust decision-aiding tool for engineers, architects, and decision makers.