Optimal design based on fabricated SiC/B4C/porcelain filled aluminium alloy matrix composite using hybrid AHP/CRITIC-COPRAS approach

In this present study, SiC, B4C and waste porcelain reinforced AA7075 alloy composites are fabricated by adopting stir casting approach. Twelve formulations based on different weight percentages of reinforcers (3 wt.%, 4.5 wt.%, 6 wt.% and 7.5 wt.%.) were manufactured and afterward analysed in terms of physical, mechanical, corrosion and tribological performances. The reinforcers of less than 53 μm size were consistently blended in molten AA7075 accompanied by stirring process. To identify the best suitable formulation the density, hardness, tensile strength, compressive strength, flexural strength, friction coefficient, wear and corrosion rate were fixed as selection criteria. The composite containing 7.5 wt.% B4C (ASBP-8) exhibited the highest mechanical strength (Hardness=162 Hv; Tensile strength= 298 MPa; Compressive strength= 221 MPa; and Flexural strength= 267 MPa), whereas wear performance (at 40 N load and 1300 m SD= 0.00261 g; and at 5.026 m/s SV and 1300 m SD= 0.0231 g) and coefficient of friction (at 40 N load and 1300 m SD= 0.536 g; and at 5.026 m/s SV and 1300 m SD= 0.47 g) remain the lowest for 6 wt.% porcelain (ASBP-11) based composites. The density and corrosion rate remains lowest for the composite containing 7.5 wt.% porcelain (ASBP-12).Since no single composite(ASBP-1 to ASBP-12) could merely satisfy all the desired characteristics; to this end, this study applied a novel hybrid AHP/CRITIC-COPRAS method for the selection of optimal alternative material for automotive components. The weight of each material evaluated was determined by establishing a criterion of importance by applying inter-criteria correlation (CRITIC) and analytic hierarchy process (AHP) methods. The alternative ranking was evaluated using the complex proportional assessment (COPRAS) method. The evaluation indicated that the AA7075 containing 7.5 wt.% porcelain (ASBP-12) composite possesses the best material solution to be used in automotive applications

Multi-objective optimization of energy consumption and surface quality in nanofluid SQCL assisted face milling

Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality

Effect of the relative position of the face milling tooltowards the workpiece on machined surfaceroughness and milling dynamics

In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter's diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given95sem informaçãosem informaçã

Green machining for the dry milling process of stainless steel 304

Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This paper presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed (V), depth of cut (a), feed rate (f), and nose radius (r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85 % and 57.65 %, respectively, and the power factor improves around 28.83 %, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices

Internal cracks and non-metallic inclusions as root causes of casting failure in sugar mill roller shafts

The sugar mill roller shaft is one of the critical parts of the sugar industry. It requires careful manufacturing and testing in order to meet the stringent specification when it is used for applications under continuous fatigue and wear environments. For heavy industry, the manufacturing of such heavy parts (>600 mm diameter) is a challenge, owing to ease of occurrence of surface/subsurface cracks and inclusions that lead to the rejection of the final product. Therefore, the identification and continuous reduction of defects are inevitable tasks. If the defect activity is controlled, this offers the possibility to extend the component (sugar mill roller) life cycle and resistance to failure. The current study aims to explore the benefits of using ultrasonic testing (UT) to avoid the rejection of the shaft in heavy industry. This study performed a rigorous evaluation of defects through destructive and nondestructive quality checks in order to detect the causes and effects of rejection. The results gathered in this study depict macro-surface cracks and sub-surface microcracks. The results also found alumina and oxide type non-metallic inclusions, which led to surface/subsurface cracks and ultimately the rejection of the mill roller shaft. A root cause analysis (RCA) approach highlighted the refractory lining, the hot-top of the furnace and the ladle as significant causes of inclusions. The low-quality flux and refractory lining material of the furnace and the hot-top, which were possible causes of rejection, were replaced by standard materials with better quality, applied by their standardized procedure, to prevent this problem in future production. The feedback statistics, evaluated over more than one year, indicated that the rejection rate was reduced for defective production by up to 7.6%

Surface modification for osseointegration of Ti6Al4V ELI using powder mixed sinking EDM

Biomedical implant rejection due to micromotion and inflammation around an implant leads to osteolysis and eventually has an implant failure because of poor osseointegration. To enhance osseointegration, the implant surface modification both at the nano and micro-scale levels is preferred to result in an enhanced interface between the body tissue and implant. The present study focuses on the modification of the surface of Titanium (α+β) ELI medical grade alloy using powder-mixed electric discharge machining (PMEDM). Pulse current, on/off time, and various silicon carbide (SiC) powder concentrations are used as input parameters to comprehend desired surface modifications. Powder concentration is considered as the most important factor to control surface roughness and recast layer depth. A significant decrease in surface fracture density and roughness is observed using a 20 g/l concentration of SiC particles. Elemental mapping analysis has confirmed the migration of Si and the generation of promising surface texture and chemistry. Oxides and carbides enriched surface improved the microhardness of the re-solidified layer from 320 HV to 727 HV. Surface topology reveals nano-porosity (50–200 nm) which enhances osseointegration due to the absorption of proteins especially collagen to the surface

Analyses of variability, euclidean clustering and principal components for genetic diversity of eight Tossa Jute (Corchorus olitorius L.) genotypes

An investigation was done to assess the genetic variability, character associations, and genetic diversity of eight jute genotypes for seven morphological traits in a randomised complete block design at Bangladesh Jute Research Institute during 15 March, 2018 to 31 December, 2019. Analyses results revealed significant differences (P<0.01) among all genotypes for studied traits indicating the presence of variability. All the lines performed better than one control (JRO-524), and the line (O-0412-9-4) provided good results for desired traits than all controls. Jute fibre yield showed the highest broad sense heritability (98.54%). The studied jute morphological traits i.e. Plant population, the plant height, green weight, dry fibre yield and dry stick yield gave high heritability along with high genotypic and phenotypic variances, genetic advances in percent of the mean, highly significant and positive correlations. It indicates the possibility of crop improvement through phenotypic selection and maximum genetic gain, simultaneously at the genotypic-phenotypic level. Clustering analysis grouped all genotypes into three distinct clusters. The cluster II showed the highest mean values for all traits followed by cluster I and III. The first two principal components with higher Eigen values (>1.0) accounted for 90.88% of the total variation in the principal component analysis. PCA and cluster analyses indicated that the advanced breeding line O-0412-9-4 made its individual cluster II with higher inter-cluster distance and higher fibre yield (3.12 t ha-1). The investigation was done to select the genotype(s) with good fibre yield and distinct features in respect of developing high yielding Tossa jute variety for cultivation in the farmers’ field. This genotype O-0412-9-4 was selected based on higher plant height, base diameter, fibre yield content. It will be developed as a high yielding variety considering its’ higher genetic variability, heritability, genetic advance, significant associations for desirable characters

Response surface and neural network based predictive models of cutting temperature in hard turning

The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trials, conducted in both dry and high pressure coolant (HPC) environments, were planned using full factorial design. The temperature was measured by using the tool-work thermocouple. In RSM model, two quadratic equations of temperature were derived from experimental data. The analysis of variance (ANOVA) and mean absolute percentage error (MAPE) were performed to suffice the adequacy of the models. In ANN model, 80% data were used to train and 20% data were employed for testing. Like RSM, herein, the error analysis was also conducted. The accuracy of the RSM and ANN model was found to be ⩾99%. The ANN models exhibit an error of ∼5% MAE for testing data. The regression coefficient was found to be greater than 99.9% for both dry and HPC. Both these models are acceptable, although the ANN model demonstrated a higher accuracy. These models, if employed, are expected to provide a better control of cutting temperature in turning of hardened steel

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Though the complex programming, parameters selection, and ability to obtain the global optimal solution are major concerns of evolutionary based algorithms, in the present paper, the optimization was performed by using efficient algorithms i.e., teaching–learning-based optimization and bacterial foraging optimization. Furthermore, the weighted sum method was used to transform the diverse responses into a single response, and then multi-objective optimization was performed using the teaching–learning-based optimization method and the standard bacterial foraging optimization method. Finally, the optimum results reported by these methods are compared to choose the best method. In fact, owing to better convergence within shortest time, the teaching–learning-based optimization approach is recommended. It is expected that the outcome of this research would help to efficiently and intelligently perform the hard-turning process under automatic and optimized environment

Energy Harvest from Water Wave – Bangladesh Prospect

Searching the alternative energy sources has become essential to meet the energy crisis especially for the developing country like Bangladesh. This article focuses on the importance, possibilities, environmental effects and challenges of harvesting energy from water waves and different wave projects that are ongoing in different countries around the world in order to meet the energy demand of the rapidly rising population and to reduce the use of the conventional energy source mainly fossil fuel, which is one of the key factors held responsible for environmental degradation and Global warming. Furthermore the energy sector scenario of Bangladesh, a country which has the longest uninterrupted shoreline and a good prospect of harnessing energy from waves from its various suitable sites, is signified. The cost analysis of wave devices are shown and a comparison between the cost of electricity generation from the present energy sources and the different wave energy devices is made in order to see the feasibility of the wave devices for power generation in the context of Bangladesh. Study shows that there is huge prospect of wave energy in Bangladesh to meet the demand and reduce the dependency of the fossil fuel