Abstract The concurrent demand for lightweight structural materials and industrial waste valorisation has intensified interest in hybrid natural fibre composites. This work presents a computational framework built on a response surface methodology (RSM) surrogate base with two parallel optimisation approaches for the design and multi-objective optimisation of hybrid abaca fibre /red mud/ epoxy composites targeting simultaneous maximisation of tensile, flexural, and impact strengths. This three-stage framework comprises an RSM surrogate base (Stage 1) feeding two parallel optimisation routes as a multi-objective genetic algorithm (MOGA)-driven multi-objective search (Stage 2) and a Bayesian-optimisation (BO) benchmark for evaluation-efficiency comparison (Stage 3), with a SHapley Additive exPlanations (SHAP) explainability layer applied across the surrogate models. At Stage 1, RSM was applied to a 3 3 full-factorial experimental dataset (ASTM D638/D790/D256) to develop second-order quadratic surrogate models achieving R 2 ⩾ 88.77% across all mechanical responses. At Stage 2, a within three iterations (186 design evaluations) to a 70% Pareto front, yielding three candidate compositions, all at the upper abaca bound ( A w = 7.9 wt.%). Candidate Point 3 ( R w = 11.79%, R p = 82.07 μ m) achieved tensile strength 47.88 MPa, flexural strength 42.22 MPa, and impact strength 42.93 J m −1 , validated experimentally within 6.00% error. Stage 3 (benchmarking): BO with Gaussian process surrogates and expected improvement acquisition identified a model-predicted candidate optimum in only 50 evaluations, reducing computational cost by 73%, demonstrating the feasibility of rapid-screening campaigns for related fibre systems. The BO result (+29.0% tensile strength relative to MOGA Candidate Point 3) is a Random Forest (RF) surrogate prediction and has not been experimentally validated. Complementing the optimisation, SHAP analysis of RF surrogate models trained on the RSM-MOGA surrogate dataset (CV R 2 ⩾ 0.88) provides quantitative per-variable attribution of surrogate model predictions: red mud content ( R w ) is the dominant tensile driver (mean |SHAP| = 6.40 MPa), while abaca content ( A w ) governs both flexural (|SHAP| = 2.92 MPa) and impact (|SHAP| = 3.22 J m −1 ) responses, overturning the ranking produced by conventional linear sensitivity analysis and providing surrogate-model-level validation for the MOGA-optimal composition.

AluminIum 7075 hybrid metal matrix composites are widely utilized in aerospace, automotive, defense and marine applications due to their superior strength, stiffness, hardness and wear resistance properties. Friction stir welding (FSW) is a promising technique for joining such composites, but its industrial application is limited by the challenges such as excessive tool wear, reduced weld strength and weld defects caused by too degradation. This study focuses on development of an H13 tool with enhanced wear resistance for the FSW of AA7075 hybrid metal matrix composite (MMC) reinforced with 5%, 10%, 15% of boron carbide (B4C) particles and 8% blast furnace slag (BFS) by weight. A cryogenically treated H13 tool was proposed to improve tool durability and minimize the wear. The treated tool shows a remarkable 336% improvement in wear resistance under in situ welding compared to untreated tool. Micro-mechanical characterization of the welded joints was performed, revealing improved weld properties, particularly in plates with higher reinforcement percentages. Mechanical testing revealed that the ultimate tensile strength increased with reinforcement content with a maximum of 266.37 +/- 3.17 MPa (50.14% higher than the unreinforced sample) for the sample containing 15 wt% of B4C + 8 wt% of BFS, while microhardness improved by 12% achieving a peak value of 164.42 Hv. These findings demonstrate that the use of cryogenically treated H13 tools enables the FSW of highly reinforced AA7075 MMCs, producing defect-free joints with superior strength and hardness, thereby extending the potential of FSW in advanced structural applications.

Purpose Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM. Design/methodology/approach DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment. Findings The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment. Research limitations/implications The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success. Originality/value To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This paper presents an experimental and mathematical analysis of the laser process parameters by experimenting on a steel material, SS304, which was cut to the proper dimensions, and then a laser beam was passed through at varying velocities in the conditions when focal length, f = 0 and f = 5 mm. An inspection was done under an optical microscope after mirror polishing the cross-section of the workpiece. The diameter and depth of the beam can be observed, among other things. In the mathematical part, laser characteristics such as focal points like interaction time, power density, energy density, line energy, etc. are calculated. The graph is plotted, like interaction time versus depth of cut, interaction time versus beam diameter, etc., to study the behaviors of laser process parameters. the conduction at a focal length of 5 mm and keyhole at a focal length of 0 mm phenomenon was observed.

Predicting or forecasting crypto currency prices is now one of the most difficult tasks in crypto market trading due to its qualities and dynamic nature. The purpose of the present work is to analyse the exchange and blockchain data and develop a prediction model using machine learning. Ethereum (ETH) is one of the crypto currencies, and data has been taken from the price time series from January 1, 2017 to December 31, 2021, on a daily basis. The algorithm for gathering data has been trained and tested using a machine-learning algorithm. The adequacy of the developed machine learning models was validated using MAPE, RSME, MAE, and R2 scores. The developed model can predict future results with an accuracy of up to 85% for 7 days. Based on the findings, it is suggested that blockchain historical data and exchange data can be utilised as input characteristics in the development of a machine learning model to forecast Ethereum's future price.

In its early days, microwave was mainly used in food industries. Later, it was demonstrated by researchers that all metal powders absorb microwaves at room temperature and only the bulk metal reflects microwaves with slight surface penetration. Hence, in recent days, microwave energy is being aggressively used for an extensive range of applications in material processing. This is because of its several major advantages in terms of processing time, heating rates, improved mechanical properties, etc. The study deals with the compacted 2017 aluminium-copper-based alloy powder interacting with microwaves at 470oC and describes how density and hardness vary with respect to changes in wt.% (0%, 5% and 10%) of silicon carbide (SiC) in aluminium alloy. The study reveals that aluminium alloy with 10% SiC possesses the highest hardness and the lowest density among all. Further, microstructural investigation was carried out to validate results. Results showed that addition of SiC resulted in improvement in hardness and reduction in density of 2017 aluminium alloy which makes the alloy mechanically stronger and lighter, which are the two most desirable attributes for any structural applications.

Strength always remains the prime requirement of any produced products, which normally explicate the capability of the products to sustain stress into it. Ultimate tensile strength is principally used to clarify the maximum values of stress, which can be resist by any product or material entity before breaking. Accordingly, study is conducted to verify the methodological way of determining the predictive values of ultimate tensile strength in welded joint. Response surface methodology is used in present study to grace decision results. In present study, the Metal Inert Gas (MIG) welding process is experimentally performed in mild steel plate specimens by considering three distinguish values of welding current, voltage and plate thickness. The objective of the study is to enroll the predictive equation to assists in deriving the elevated values of ultimate strength of the welded joint. The primary objective of present study is to demonstrate the utilization of competent structure of Response surface methodology under the dimensional arena of welding process. Here, the authors devised equation, which competently possess caliber to define the predicted values of ultimate tensile strength for the precise values of process parameter. The same assist in precisely understanding the behavior of ultimate tensile strength (dependent variable) under the influence of independent variables i.e. welding current, voltage and plate thickness. Response surface methodology is used and experiments based on Box-Behnken Design are performed in present study. The work is supported by MINITAB software for generating graphs and originating driving equation between response and process parameters. The predictive values are determined based on multiple regression equation and compared with actual experimental values to demonstrate capability and applicability.

Ultrasonic welding is a revolutionary solid-state welding technique for joining metals as well as non-metals whereby high-frequency ultrasonic mechanical vibrations are locally applied to workpieces being held together under pressure. The high-frequency vibrations along with applied pressure create frictional heat at the mating surfaces leading to molecular bond. This work presents the effects of the different shapes of the sonotrode on the transient temperature distribution during the welding, using finite element analysis (FEA). The three different shapes of sonotrodes: stepped, conical and exponential are considered. Prepared FE models are capable of predicting the temperature distribution to different parts such as weld interface, sonotrode, and anvil. The results of the FE model are validated with experimental results using K-type thermocouple.

At present, hybrid natural fibre-reinforced polymer composites are popularly used for their remarkable specific strength. Natural fibre polymer composites have been explored by the researchers for their sprawling use in engineering applications. To achieve better mechanical properties, it is needed to test hybrid natural fibre composites with all possible combinations of their compositions which require a lot of resources. Thus, the present work deals with the investigation of mechanical properties of hybrid abaca–epoxy composites. Experiments were carried out according to full factorial design with three input parameters namely weight per cent of abaca fibre, particle size of red mud and weight per cent of red mud. Subsequent to this, a fuzzy model is developed to predict the mechanical properties such as tensile, flexural and impact strength of hybrid abaca–epoxy composites based on the experimental results obtained by their mechanical characterisation. Membership functions were constructed such that the fuzzy model can precisely predict the mechanical properties of hybrid composites. Moreover, a set of test case experiments were conducted so as to validate the fuzzy model. It was inferred from these test case results that the developed model can be used to predict mechanical properties of hybrid composites with a maximum accuracy of 87%.

Smart mobile devices of the present era offer many services i.e. SMS, gaming, a camera, navigation, the Internet, television, etc., and their utilization has significantly risen during the last decades. Today, individuals are addicted to mobiles and cannot think of living without using them. Conversely, these mobiles become obsolete due to certain shortcomings and are eventually replaced with new ones and thus create e-waste, which are alarmed as threat to the society. In this work, the authors describe mobiles and e-waste in a closed loop structure for supporting green issues. The work has rooted generalized interval-valued trapezoidal fuzzy numbers (GIVTFNs) with a degree of similarity measure approach to model the rationale and to furnish decision results. The authors developed a decision support system to prevent e-waste by defining significant inadequacy liable for the larger alteration of working mobiles. The present study demonstrates the technical model under an Indian context to verify its applicability, but it can be used it under any regional or worldwide scenario.

Hybrid natural fibre reinforced polymer composites have emerged as an eco-friendly alternative to conventional structural materials due to their low cost and high strength-to-weight ratio. It is evident from the literature that polymer composites have found their place in tribological applications too. Therefore, in this research work, hybrid abaca–epoxy composites were fabricated using hand layup technique with red mud as filler. The influence of wt. % of abaca (A), wt. % of red mud (B) and particle size of red mud (C) on sliding wear of hybrid composite is established using response surface method (RSM). RSM also yields a mathematical model for optimization of sliding wear of hybrid composites. Composite with optimum values of A, B and C was found to be most suited for minimum sliding wear of hybrid composites. Further, on the basis of experimental data, a fuzzy logic model is also framed for the prediction of sliding wear of hybrid composites. Nine test case experiments were performed to validate the developed fuzzy model. It was observed from the results that developed fuzzy model can predict the sliding wear of hybrid composites with an accuracy of 87%.

Natural fibre reinforced polymer composites are used in structural applications for production of light weight components due to their high specific strength. Abaca fibre as reinforcement in polymer matrices became popular due to applications of its polymer composite in production of exterior components of passenger cars. The present review emphasises on the properties, treatments and extraction of abaca fibre. It also provides an overview of research works related to preparation and properties (mechanical, structural and thermal properties) of abaca fibre reinforced polymer composites. Moreover, it also highlights the research gaps from available literatures, which brings out the paucity of literatures on modelling and simulation of mechanical properties of abaca composites based on polymer matrices like polyester, polylactide, epoxy, phenol formaldehyde, high density polyethylene (HDPE) and polystyrene.

Natural fibres are known for their bio-degradable nature and light weight. Natural fibres (Cellulosic material) are obtained from plants which are abundantly available in nature and thus these fibres could be a better alternative for conventional reinforcements in polymer composites. However, hydrophilic nature of natural fibre confines its applications due to their poor interfacial adhesion with hydrophobic polymer matrices. This work involves treatment of abaca fibres with 5wt.% NaOH solution. The effect of modification on hydrophilicity and interfacial adhesion of abaca fibres is analyzed via Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). The result suggests that alkali treatment of abaca fibre improves interfacial adhesion and reduces the hydrophilicity of fibre.

Polymer composites have become one of the most important domains in recent times for researchers. It is due to the fact that polymer composites possess better strength-to-weight ratio than the most of the conventional alloys and composites which are in use today for structural applications. Moreover, the researchers are also coming up with novel hybrid polymer composites so as to achieve the desired mechanical properties. Therefore, this review on hybrid polymer composites focuses on the mechanical properties like impact, flexural and tensile strengths of hybrid polymer composites so as to bring out the essence of their mechanical behaviour which are influenced by critical factors like selection of type, orientation and arrangement of reinforcements in polymer matrix composites. This detailed review is an endeavour to unfold the major aspects of this domain as research gaps which are untouched till date. The study shows that there is limited use of fillers (such as red mud and fly ash) and natural fibres (abaca, bamboo, ramie, coir, pineapple) in hybrid polymer composites to harness their full potential. It is also inferred from the study that there is a dearth of research pertinent to modelling, prediction and optimisation of mechanical properties of hybrid polymer composites like toughness, flexural strength, tensile strength and impact strength.

In this study, the authors consider the context of products whose component failures cannot be rectified through repair actions, but can only be fixed by replacement. The authors developed a cost effective decision model based on reliability for the replacement of street lights. The replacement policy for benchmarking maintenance time based on fuzzy reliability index, which can grab uncertainty, is proposed in this study. The study determined the link amongst individual replacement and the group replacement policies by defining the economic instant for replacing street lights. A decision support model incorporating uncertainty and impreciseness is presented to estimate prior maintenance efforts. The individual and the group replacement policies are exposed by housing a subjective theoretical framework and explained with a numerical procedure to illustrate the applicability of the proposed approach. The study provides valuable insight for using the real time information for designing a replacement model using fuzzy logic.

Hybrid natural fibre polymer composites have attracted attention of research community owing to their better mechanical properties as compared to conventional materials. Besides being inexpensive, natural fibres are eco-friendly in nature. In past literature, abaca has shown tremendous potential for its suitability in structural applications. Present work deals with mechanical characterization and modelling of hybrid abaca epoxy composites with red mud as filler. Hybrid composites were prepared by hand lay-up technique. Experiments were designed based on full factorial method having three control parameters, namely weight percentage of abaca (2.6, 5.26 and 7.9 wt%), weight percentage of red mud (4, 8 and 12 wt%) and particle size of red mud (68, 82 and 98 µm). Flexural and impact strength of composites were evaluated. Mathematical models for flexural and impact strength of hybrid abaca composites were developed using response surface method. Developed models for mechanical properties of composite were analysed using analysis of variance to recognize the significance of control parameters or input variables on the mechanical properties of hybrid composites. Moreover, interaction effects of input variables on flexural and impact strength of hybrid composites were also investigated. Developed model also enables us to predict mechanical properties of hybrid composites.

In present research work, discussions have been made to predict the bead geometries and shape profiles of weldments using statistical regression modeling and fuzzy logic techniques. However, the regression and fuzzy logic modeling techniques do not take into account the actual physical properties and phenomena that occur in welding. Moreover, techniques such as regression and fuzzy logic modeling are not suitable for predicting the transient temperature distribution and distortion of arc welded joints. To predict the transient temperature distributions, peak temperature distribution, and residual deformation in welding, deterministic modeling techniques such as thermomechanical analysis are preferred. However, while performing thermomechanical analysis of welded joints, size and reinforcement dimensions of the weld bead need to be incorporated into the model for accurate prediction of transient temperature distributions and distortions. In this work, circularly spread moving heat source has been used for transient thermal modeling of tungsten inert gas (TIG) welding process. In the subsequent sections of this article, the weld thermomechanical analyses for TIG square butt joints are discussed to predict the temperature distributions and angular distortion. The weld dimensions such as weld width, weld depression, and weld bulging have shown great influence on the angular distortion patterns. 1. Introduction The present work describes the thermomechanical analysis of an open arc process, i.e., tungsten inert gas (TIG) welding of square butt joints by considering the circularly spread moving heat source to predict the angular distortion and thermal profile (Pandey et al. 2016). TIG welding is commonly used for thin sheet joining (Pandey et al. 2018a, 2018b). Generally, for thin sheets, the TIG welding is performed autogenously such that no filler material is required. In some special cases, such as for fillet and groove welding, a filler rod is used in the TIG welding process. The distortion of the TIG weld square butt joints is primarily dependent on the weld width, bead depression, and bulging (Mahapatra et al. 2006). If these factors of weld bead geometries are within tolerable limits, then, the distortion observed in the weld joint is minimum. In TIG weld butt joints, the angular distortion is more prominent because of the presence of upper bead depression and lower bead bulging. Finite element analysis (FEA) simulation of TIG welding is highly effective in predicting the thermomechanical behavior such as temperature distributions and distortions. In this work, numerical and experimental approaches have been applied to predict the thermal profile and angular distortion in a TIG open arc welding process. An Finite element (FE) model has been developed for 3-D analysis of TIG square butt joints for predicting angular distortion based on circularly spread moving heat source and weld geometry.

The authors attempt to model a decision-making mechanism; which can fix multiple robot characteristics and can aid in investigating robots status for a particular manufacturing arena. The work exposed a series of applicable robot characteristics; which differentiated their working capacity and defines their value. A simple additive weighting (SAW) mechanism under a fuzzy concept is presented for investigating the status of industrial robots; which incorporates comprehensive aspects for sustainable robot selection. The authors have extended the application field of fuzzy sets theory and illustrated the significant application of linguistic terms in the robot decision-making problem. The work compounds fuzzy sets theory with SAW techniques and thus serves a flavor of fuzzy concept and a SAW technique under single platform. The study reveals the course of action for executing the proposed work by the managers. The work has applied fuzzy linguistic terms for griping the appropriate perceptions of the decision makers and applied the conception of SAW methodology to yield the decision results.