Experimental investigation of bi-directional flax with ramie fibre-reinforced phenol-formaldehyde hybrid composites

Modern research focuses on natural, green, and sustainable materials that can be used to replace conventional materials. Because of their beneficial qualities, natural fibre composites are being thoroughly researched. This research focuses on the development of a flax fibre reinforced with phenol-formaldehyde resin hybridization with ramie fibre through a vacuum infusion process. Eight different sequences were fabricated using a core-sheath structure and were mechanically characterized as per ASTM standards. The fabrication technique influences the adhesion of the matrix with reinforcement. The results also reveal that composite having ramie as a sheath layer and flax as a core delivers good mechanical characteristics compared to vice versa. The laminate H exhibited highest mechanical properties among all the eight laminates produced for this study. It exhibited a tensile strength of 54 MPa, tensile modulus of 0.98 Gpa, elongation of 7.1%, flexural strength of 143 Mpa, and compressive strength of 63.65 Mpa. The stress strain curves revealed that all the laminates exhibited ductile behaviour before failing during the tensile test and flexural test, respectively. The stacking sequence of the laminate H influenced the mechanical properties exhibited by it and its counterparts. A morphological study was carried out to analyse the failure surfaces. Morphological analysis exhibited few defects in the laminate after the tests. The composites developed delivers better mechanical properties than commercial composites available on the market, which can be used in lightweight structural applications.Web of Science1422art. no. 488

Optimization of Process Control Parameters for WEDM of Al-LM25/Fly Ash/B4C Hybrid Composites Using Evolutionary Algorithms: A Comparative Study

In this work, wire electrical discharge machining (WEDM) of aluminum (LM25) reinforced with fly ash and boron carbide (B4C) hybrid composites was performed to investigate the influence of reinforcement wt% and machining parameters on the performance characteristics. The hybrid composite specimens were fabricated through the stir casting process by varying the wt% of reinforcements from 3 to 9. In the machinability studies, the WEDM process control parameters such as gap voltage, pulse-on time, pulse-off time, and wire feed were varied to analyze their effects on machining performance including volume removal rate and surface roughness. The WEDM experiments were planned and conducted through the L27 orthogonal array approach of the Taguchi methodology, and the corresponding volume removal rate and surface roughness were measured. In addition, the multi-parametric ANOVA was performed to examine the statistical significance of the process control parameters on the volume removal rate and surface roughness. Furthermore, the spatial distribution of the parameter values for both the responses were statistically analyzed to confirm the selection of the range of the process control parameters. Finally, the quadratic multiple linear regression models (MLRMs) were formulated based on the correlation between the process control parameters and output responses. The Grass–Hooper Optimization (GHO) algorithm was proposed in this work to identify the optimal process control parameters through the MLRMs, in light of simultaneously maximizing the volume removal rate and minimizing the surface roughness. The effectiveness of the proposed GHO algorithm was tested against the results of the particle swarm optimization and moth-flame optimization algorithms. From the results, it was identified that the GHO algorithm outperformed the others in terms of maximizing volume removal rate and minimizing the surface roughness values. Furthermore, the confirmation experiment was also carried out to validate the optimal combination of process control parameters obtained through the GHO algorithm

Parameter optimization of titanium-coated stainless steel inserts for turning operation

This study discusses the three essential process parameters cutting speed, feed and depth of cut on the quality of the tool during turning operation. A high-strength stainless steel tool coated with tungsten carbide is used. The tool is further strengthened using cryogenic treatment by immersing it in liquid nitrogen for 24 h and 36 h respectively. The surface roughness of the simple coated tool and the processed tool is compared using optimization techniques like the Taguchi technique and ANOVA. The analysis revealed that the surface roughness of the simple coated tool insert was 0.5 μm, whereas the surface roughness of the tool inserts immersed in liquid nitrogen for 36 h was 12.5 μm. The processed tool insert became brittle which lead to an increase in surface roughness after the turning operation. Three different algorithms like Grass Hopper Optimization, Moth Flame Optimization, and Salp Swarm Optimization were used to observe the feasibility of the optimization techniques. The Moth Flame Optimization algorithm had good convergence and also delivered results that were correlating with the ANOVA. It is concluded that while keeping a high tool rotation speed of 984.46 rpm, a low feed of 91.4 mm/min and a depth of cut of 0.25 mm resulted in a low surface roughness of simple coated tool insert was 0.59 μm

Experimental Investigation of Bi-Directional Flax with Ramie Fibre-Reinforced Phenol-Formaldehyde Hybrid Composites

Modern research focuses on natural, green, and sustainable materials that can be used to replace conventional materials. Because of their beneficial qualities, natural fibre composites are being thoroughly researched. This research focuses on the development of a flax fibre reinforced with phenol-formaldehyde resin hybridization with ramie fibre through a vacuum infusion process. Eight different sequences were fabricated using a core–sheath structure and were mechanically characterized as per ASTM standards. The fabrication technique influences the adhesion of the matrix with reinforcement. The results also reveal that composite having ramie as a sheath layer and flax as a core delivers good mechanical characteristics compared to vice versa. The laminate H exhibited highest mechanical properties among all the eight laminates produced for this study. It exhibited a tensile strength of 54 MPa, tensile modulus of 0.98 Gpa, elongation of 7.1%, flexural strength of 143 Mpa, and compressive strength of 63.65 Mpa. The stress strain curves revealed that all the laminates exhibited ductile behaviour before failing during the tensile test and flexural test, respectively. The stacking sequence of the laminate H influenced the mechanical properties exhibited by it and its counterparts. A morphological study was carried out to analyse the failure surfaces. Morphological analysis exhibited few defects in the laminate after the tests. The composites developed delivers better mechanical properties than commercial composites available on the market, which can be used in lightweight structural applications

Enhancement of Thermal Behaviour of Flax with a Ramie Fibre-Reinforced Polymer Composite

Plant-derived fibres, called lignocellulosic fibres, are a natural alternative to synthetic fibres in polymer composite reinforcement. Utilizing renewable resources, such as fibre-reinforced polymeric composites made from plant and animal sources, has become a crucial design requirement for developing and producing parts for all industrial goods. Natural-fibre-based composites are used for door panels, trays, glove boxes, etc. This study involves developing and thermal analysing a flax fibre reinforced with phenol–formaldehyde resin hybridization with ramie fibre by way of a vacuum infusion process. As per ASTM Standard, eight different sequences were fabricated and thermally characterized. In the present study, three stages of weight loss (%) are shown by the thermogravimetric analysis (TGA). The sample loses less weight during the first stage, more during the second, and more during the third. The sample’s overall maximum temperature was recorded at 630 °C. It was discovered that sample D (80.1 °C) had the highest heat deflection temperature, and sample B had the lowest (86.0 °C). Sample C had a low thermal expansion coefficient, while sample G had a high thermal expansion coefficient. Sample E had the highest thermal conductivity, measured at 0.213 W/mK, whereas sample A had the lowest conductivity, at 0.182 W/mK. From the present study, it was found that sample H had better thermal characteristics. The result of the present investigation would generate thermal data regarding hybrid ramie and flax composites, which would be helpful for researchers and practitioners involved in the field of biocomposites

Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance

Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance

An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method

This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the various tolerance ranges of parts. The L16 orthogonal array is used for identifying the effectiveness of the proposed EAUB method through varying the number of bins of the parts of an assembly. Because of qualities such as minimal setting parameters, ease of understanding and implementation, and rapid convergence, the moth–flame optimization (MFO) algorithm is put forward in this work for identifying the optimal combination of bins of the parts of an assembly toward maximizing the percentage of the success rate of making assemblies. Computational results showed a 5.78% improvement in the success rate through the proposed approach compared with the past literature. The usage of the MFO algorithm is justified by comparing the computational results with the harmony search algorithm

Harmony Search Algorithm for Minimizing Assembly Variation in Non-linear Assembly

The proposed work aims to acquire the maximum number of non-linear assemblies with closer assembly tolerance specifications by mating the different bins’ components. Before that, the components are classified based on the range of tolerance values and grouped into different bins. Further, the manufacturing process of the components is selected from the given and known alternative processes. It is incredibly tedious to obtain the best combinations of bins and the best process together. Hence, a novel approach using the combination of the univariate search method and the harmony search algorithm is proposed in this work. Overrunning clutch assembly is taken as an example. The components of overrunning clutch assembly are manufactured with a wide tolerance value using the best process selected from the given alternatives by the univariate search method. Further, the manufactured components are grouped into three to nine bins. A combination of the best bins is obtained for the various assembly specifications by implementing the harmony search algorithm. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The results show that the contribution of the proposed novel methodology is legitimate in solving selective assembly problems