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In the present work, chemically treated kenaf fibre (10, 20 and 30 wt. %) reinforced epoxy resin composite formulations were fabricated using hand lay-up technique. The prepared composites were mechanically characterized using Taguchi L27experimental design to examine the influence of varying kenaf fibre wt. %, NaOH concentration % for chemical treatment, and fibre immersion duration. The grey-fuzzy optimization approach was utilized for optimizing prepared formulations mechanical properties such as tensile strength, impact strength and flexural strength. Further, analysis of variance (ANOVA) tests and response graphs were utilized for delivering model significance and determining the percentage contribution of input variables. The NaOH concentration % was found to be most significant parameter followed by kenaf wt. % in providing best mechanical properties of chemically treated kenaf fibre reinforced epoxy composites.

The paper presents a new mechanism for an index finger rehabilitation device. The mechanism was designed based on a pre-specified trajectory obtained by performing flexion/extension experimental trials with index finger. These experiments were performed 30 times and mean of the normalized trajectory was extracted. From this trajectory, six precision points were selected and based on the obtained trajectory, a path generating optimization problem was formulated to imitate the flexion/extension. Besides, rectification constraints were posed to avoid defects that typically encounter during synthesis. The formulated nonlinear optimization problem was solved using nature-inspired and metaphor-less algorithms. It is found that the summed error between the desired and generated trajectories was very less in case of nature-inspired algorithm in comparison with metaphor-less algorithm. Then, all rectification constraints were validated. It is found that obtained mechanism is completely defect-free and is able to imitate the desired flexion/extension trajectory. Therefore, design obtained using nature-inspired algorithm may prove to be beneficial for the patients with dysfunction index finger.

In this paper, an intelligent approach of adaptive neuro-fuzzy inference system (ANFIS) and non-dominated sorting genetic algorithm-II (NSGA-II) was delineated to establish model and optimize the wire electrical discharge machining (WEDM) process. The WEDM experiments were designed utilizing Taguchi L18 mixed orthogonal array for machining of Ti–6Al–4V titanium alloy. The ANFIS model was delineated to explain the influence of input machining characteristics, viz. peak current (IP), pulse on-time (Ton), pulse off-time (Toff) and wire feed (WF) on output response of material removal rate (MRR) and wire wear ratio (WWR). The proximity of results with confirmation experimental results revealed the effectiveness of the developed ANFIS model in prediction of output quality characteristics for the chosen input machining factors. The artificial neural network (ANN) and NSGA-II were integrated and applied for multi-objective optimization in determining optimal WEDM machining process conditions. The optimal results obtained with NSGA-II for multi-objective optimization of input control variables are within tolerance limits and realized an improvement in MRR and WWR (maximum absolute percentage errors as 6.784% and 7.589%) with optimal machining characteristics.

In industries, precise measurement of different form errors is critical and challenging for stringent geometric and dimensional control in manufactured part. These form errors have significant influence on the functional performance of an industrial product that arises due the inherent invariability in measurement techniques and manufacturing devices. Several research have been conducted for evaluation of important form errors such as straightness, flatness, circularity, cylindricity and sphericity using different computational methods. The need of computational methods is justified as they minimize calculation time, human errors and provides improved tolerance values in assessment of form errors. In the same context, this paper presents a comprehensive review and discussion on different computational techniques for distinctive form error evaluation in engineering components. The present work mainly focused on aspects of mathematical formulations and the computational techniques i.e., traditional methods and advanced optimization algorithms, employed for precised evaluation of these errors. Based on the detailed review, several future research directions were described. Finally, last section presents concluding remarks on computational methods in modelling and precise evaluation of form error in manufactured components.

In the present study, the graphene sheet (GS) - embedded aluminium (Al) composite under compressive strain rate is analyzed using molecular dynamics (MD) simulation. The effects of pin-hole and layering of GSs on compressive strengthening behaviour of the composite material are predicted. MD simulation has been performed with the aid of open-source code LAMMPS by modelling a periodic system of GS-Al nanocomposite unit cells. Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) and Embedded atom method (EAM) potentials are employed to model the interactions between carbon atoms and the atoms of Al respectively. The simulation results show that the compressive strength of nanocomposite material is substantially larger than that of pure Al material. It is also found that the compressive elastic modulus of GS-Al nanocomposite material deteriorates because of pin-holed GS on the contrary to layered GS reinforced nanocomposites.

Daily activities such as aerobic movements and athletic events found effective in mitigating bone loss as it promotes osteogenesis. Computational model considered normal strain, or strain energy density as a stimulus to predict site specific osteogenesis. This model, however, fails to predict site specific osteogenesis as cortical bone surfaces exhibit different remodelling rate to mechanical loading. Remodelling rate or mineral apposition rate depends upon the loading parameters such as loading cycle, frequency, and magnitude of strain. Therefore, the present study aims to develop an adaptive neuro-fuzzy inference system (ANFIS) model for finding a robust relationship between loading parameters like strain magnitude, frequency, and cycle, and a bone remodelling parameter i.e. mineral apposition rate (MAR). The model is trained, tested, and checked with the experimental data. The results indicate that ANFIS model outperformed state of the art Artificial Neural Network (ANN) models during the prediction of MAR at periosteal and endosteal surface. A strong corelation R

The present study analyzes the important characteristics of plastic injection molding machining process. The polypropylene (PP) material has used as a specimen and effect of melt temperature, packing pressure and injection pressure has been investigated on the tensile modulus and elongation. Total 20 experiments have been performed to analyses the results. Response surface methodology (RSM) was adopted for optimization of injection molding process parameters. The experiments were conducted by using central composite design. The analysis of variance (ANOVA) techniques was used for selection of significant and non-significant parameters. The experimental results show that the RSM influence elongation by 87.04%, 11.52%, 1.43% and tensile modulus by 85.35%, 11.4%, 3.25%. Keywords: Injection molding; polypropylene; tensile modulus; elongation

Development and selection of an adequate bearing material with distinct characteristics for wear resistant applications is one of the most crucial tasks. Inappropriate selection of material may cause hindrance and result in failure of components during its functioning. To this end, this research focusses on fabrication of bearing materials reinforced with varying weight percentages of industrial waste, i.e. marble dust, and the physical, mechanical and tribological characteristics were determined. The proportions of marble dust were varied from 0 wt. % to 10 wt. % with a gap of 2.5 wt. % and filled in a silicon-bronze alloy. The physical and mechanical properties were realized by the Archimedean principle, micro-hardness tester and Instron 3369 universal testing machine respectively. The tribological properties such as friction and wear behaviour were determined utilizing pin-on-disk tribometer under different condition at normal room temperature. Due to the conflicting nature of desired properties in a bearing material, the Vise Kriterijumska Optimizacija Kompromisno Resenjemeaning under fuzzy environment was applied to rank and select the optimal composite among available alternatives because it can obtain compromise solution considering overall satisfaction and regret of the selection of the wrong provinces. From the analysis of the results, it was found that silicon-bronze-3 marble dust bearing material containing 7.5 wt. % marble dust achieved the best possible set of the properties for wear resistant applications.

The bat algorithm (BA) is one of prominent swarm-based algorithm that has the suitability in solving only small dimension engineering problems and suffers from drawback of getting trapped in local minimum with slow convergence for multi-dimensional problems. In the context of improving its applicability in solving large scale and constrained engineering design problems, this paper presents a novel upgraded bat algorithm with cuckoo search and Sugeno inertia weight (UBCSIW). In the proposed UBCSIW algorithm, first, the bat algorithm with its competence to exploit the optimal solutions in search space is combined with cuckoo search with its ability to explore best solution globally using Levy flight in the search space. Secondly, a new velocity and position search equation is incorporated in which the bat searches around the best candidate solution. This step helps in establishing adequate balance between exploration and exploitation capability and improving the performance effectively by employing greedy selection to choose the best candidate solution. Finally, Sugeno fuzzy inertia weight is introduced in the velocity updation equation, boosting the flexibility and diversity of bat population and results in stability of results. The effectiveness of the proposed UBCSIW algorithm is tested on 16 standard benchmark functions (unimodal and multimodal) with different dimensions, 12 CEC2015 test functions and 7 well-known constrained engineering design problems. The outputs of the proposed UBCSIW algorithm are validated by comparison with classical BA and other swarm-based state-of-the art algorithms. The simulation results show that proposed UBCSIW algorithm achieves highly competitive results in terms of higher optimization accuracy and improved convergence that outperforms basic BA in all twenty-eight test functions while performs better than other competitive algorithms in 24 functions (13 benchmark and 11 CEC2015 functions).

Fabrication and selection of optimal material with diverse properties for ship body is often a troublesome and difficult task, as inadequate selection of materials may result in improper functioning and failure of the components at any stage during its functioning. To this end, this paper presents a novel hybrid fuzzy Preference Selection Method (f-PSI) method for selection of optimal alternative ship body material based on physical, mechanical and corrosion characteristics of a novel hybrid aluminium metal alloy composites. Four hybrids aluminium alloy (AL7075) composites are fabricated through stir casting method by reinforcing varying quantity of zirconium dioxide (2%, 4% and 6%) and silicon carbide (3%, 6% and 9%). The physical characterization results show that void contents and density continuously increases with increase in the wt.% of the reinforcement. The mechanical characterization and fractography analysis results confirms upturn in hardness, ultimate tensile strength, flexural strength and impact strength up to 10% wt. of reinforcement and decreases for 15% wt. of reinforcement. The corrosion characterization and microstructural analysis show that corrosion rate decreases considerably up to 10% wt. of reinforcement and increase after that due to the precipitation of carbide particles along the grain boundary region of hybrid metal alloy composite. For validation and robustness of the proposed method, sensitivity analysis is performed, and comparison is performed with already available fuzzy TOPSIS and fuzzy VIKOR methods. Seven evaluation criteria such as void content, density, micro-hardness, ultimate tensile strength, flexural strength, impact strength and corrosion rate are considered in the study to choose the best material for ship body. From the analysis of results, it was found that A-2 (having 4 wt.% ZrO2 and 6 wt.% SiC as reinforcement) alternative material possesses the best combination of all the properties for the given ship body application.

The aim of present study is to investigate the microstructural, mechanical and tribological behavior of Zinc Alloy (ZA-27) hybrid metal matrix composites reinforced with graphite (Gr) and alumina (Al2O3). In the present study graphite and Al2O3 content is varied with composition (0, 3.5, 7 and 10.5 wt. %) prepared with liquid state stir casting process. Microstructure, density, hardness, compressive strength, impact strength and wear rate of composites have been investigated. SEM micrograph shows the uniform distribution of reinforcement particles in ZA-27 matrix. It is found that mechanical properties of composites depend on the reinforcements content. Hardness, compressive strength and impact strength of composites increases with an increase in reinforcements content up to 7 wt. %. It is observed that reinforced composites show good tribological properties. Wear resistance of composites is increased with an increase in hybrid reinforcement content. Maximum wear resistance is found at 10.5 wt.% reinforcements content. Further, TOPSIS methodology is adopted for the optimization of reinforcements content which shows that 7 wt.% of reinforcements provide better mechanical properties among all the compositions. It is expected that this composite will be beneficial in automobile industries.

In this article, the elastic and shear moduli of the graphene sheet-reinforced aluminum nanocomposite have been investigated by molecular dynamics simulations. Different models have been simulated to study the effect of multilayer graphene sheet, perforation of GS, and temperature on the elastic and shear moduli of resulting nanocomposite. The simulation results show that the elastic and shear moduli of graphene sheet-reinforced aluminum are sensitive to the temperature changes, multilayer, and perforated graphene sheets. The temperature and perforation of graphene sheets exert adverse effects on the elastic and shear moduli of graphene sheet-reinforced aluminum nanocomposites. However, the multilayer graphene sheet leads to favorable effects on the stiffness properties of the nanocomposite. It is also observed that there is only a marginal effect of the chirality of graphene sheet on the out-of-plane shear moduli of the nanocomposite.

In this paper, a fuzzy logic-based artificial intelligence technique is delineated to optimize surface reconstruction parameters, initiating from raw point cloud data using contactless scanning of transtibial prosthetic socket. The prosthetic socket having a free-form surface is considered for reconstruction of an accurate and complete digital model, which is further crucial for subsequent design, analysis and production. The input factors considered for the socket surface reconstruction includes number of points, filtering, triangle counts and smoothing level. The experimental trials are performed using face-centered central composite design, and further experimental data are used to establish Mamdani fuzzy logic model to predict the surface standard deviation and required space in computer memory as the response parameters. The confirmation experiment results reveal that the fuzzy model shows good agreement of 96.53% with the experimental measured value. Based on the results, it is proved that established fuzzy model can be used for predicting the surface reconstruction parameters to improve the final accuracy of the transtibial prosthetic socket model. The results concluded that the fuzzy logic provides commendable results; in addition, it successfully predicts the effect of different parameters considered for surface reconstruction on surface accuracy and computer file size.

Determination of suitable three-dimensional scanning process conditions is of prime importance for quantitative inspection of reverse engineered models. However, it is observed that scanning distance, scanning angle and measuring field are some parameters which are rarely explored during preliminary experimental trials. Therefore, in this paper optimum value for scanning process parameters is determined using the preference selection index (PSI) and metaheuristic methods. The main motivation behind the use of PSI method is that it is a rarely explored multi-criteria decision making (MCDM) technique. In addition, this method does not require assessment of the considered criteria relative significances. For PSI analysis, experimental trials were performed for different scanning conditions using Taguchi L27 orthogonal array. Moreover, an analytical model for prediction of standard deviation of final surface is developed in terms of scanning parameters for PSI prediction. Furthermore, the ANOVA analysis has been employed to establish statistical significance of the developed analytical model. Lastly, a meta-heuristic algorithm namely HTLPSO is used to optimize the PSI model and results are compared with the experimental trials. The adopted integrated methodology of PSI method and metaheuristic algorithm is found to be very helpful in improving the final quality of reverse engineered model.

Sustainable Smart Manufacturing (SSM) has played an important role in current competitive business environment. Manufacturing organizations generate waste and environmental pollution through various manufacturing process. This research presents the development of TOPSIS based model for sustainable smart manufacturing practices in Indian industries. TOPSIS method gives an order of preference based on the separation from ideal solution. This paper provides suggestions and directions for manufacturing industries to implement sustainable smart manufacturing practices towards sustainable competitiveness. This research is very much useful for the industry people for selecting the right enabler to implement SSM in the organization.

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technique for developing complex features and geometries within the shortest possible time as per customer needs. Nowadays, the customization of biomedical parts is becoming possible due to the increasing accuracy and enhanced ability of FDM machines to control the process parameters. The control on surface quality of parts produced by FDM process is of prime concern for the researchers, which are induced due to the stair steps on sloping surfaces, and needs addressing. In addition, to meet customer demands rapidly, the FDM part build time must be reduced without much compromising the strength of the parts necessary for specific applications. In the same context, this paper presents the effect and control of FDM process parameters, i.e., layer thickness, raster angle, infill density and internal structure, on surface roughness, build time and compressive strength of developed biomedical implant parts. The face-centered central composite design is employed to consummate the experimental trials, and experimental data are used to establish an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the surface roughness, build time and compressive strength with respect to changes in FDM process parameters. At the end, whale optimization algorithm (WOA) has been applied to minimize surface roughness, minimize build time and maximize compressive strength simultaneously. Then, the optimal solutions obtained from WOA methodology have been compared with ANFIS predicted results. The results reveal that ANFIS-WOA methodology provides optimal combination of FDM process parameters accurately.

This paper presents a comprehensive research on the influence of boron carbide and molybdenum disulphide hybrid reinforcement on physical, mechanical and tribological characteristics of copper metal matrix composite dedicated for electrical appliances application. The synthesis of novel copper metal matrix composite reinforced with boron carbide (0 to 3%) and molybdenum disulphide (0 to 4.5%) were fabricated using liquid stir casting technique. There is an uptrend in the physical and mechanical characteristics of the copper metal matrix composite with higher reinforcement addition in comparison to pure copper. The Taguchi experimental design is applied to plan the experiments and S/N ratio is used to determine optimal factor settings for minimum wear loss of the copper metal matrix composite. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) model is developed to predict the wear loss with respect to changes in input parameters viz. composition, load, sliding speed, and sliding distance. The confirmation experiments results reveal conformity between the ANFIS model and experimental results with 97.99% accuracy implying that the established ANFIS model can be precisely used for predicting the wear loss. Moreover, the novel copper metal matrix composite reinforced with boron carbide and molybdenum disulphide shows enhanced wear resistance in respect to unreinforced composite that make it appropriate for electrical appliances application.

There are more than 30 million people worldwide having amputations and most of them involve the lower limb at the transtibial level. With the help of prosthesis, amputees can improve the quality of life. The prosthetic socket act as a critical interface between amputation and residual limb, which is designed and developed in an iterative process by the prosthetist. Soft computing techniques have ability to describe non-trivial complex problems where input and output relations are non-linear. Soft computing techniques such as Fuzzy logic, ANN, evolutionary, and nature-inspired algorithms provide an adequate solution to the variety of complex problems, while acknowledging uncertainty involved in the problem. The sensor is of small thickness, flexible printed circuit, lightweight custom shape, and size. It can measure force between any two contacting surfaces and is durable enough to stand up in most environments with a force range. The defuzzification process was performed to obtain fuzzy output and transform fuzzy set data into numerical data value.