This study aims at analysing the velocity profiles in a straight rectangular channel having a constant width and depth throughout the channel length. Experiment was conducted for five altered depths using the straight rectangular flume in hydraulic flow laboratory available at VSS University of Technology, Odisha, India. Furthermore, the distributions of stream wise velocity at the different flow depth were computed numerically using hydraulic software ANSYS FLUENT 18.1. The results of numerical simulation showed sensibly good agreement with experimental data, which was ±10% for both across and along the channel of rectangular flume. The Reynolds number in this study lay between10,853 to 79,000. In case of channel flow, the velocity varied both in longitudinal and transverse direction. The isovel lines joining points of equal velocity normally curved upward due to the effect of turbulence. Peak velocity was found below the free surface of water. The law of logarithmic and power law was applied to study velocity distribution in terms of turbulent flow condition. The model could be validated by considering the various parameters of flow measurement such as the resultant velocity and the velocity at both horizontal and vertical direction. The inclusive idea of this study was to comprehend flow characteristic over a plane bed through experimental and numerical simulation methods.
The physical characteristics of lipids are important determining factors for appropriate food applications. The objective of this research is to study the physical characteristics, especially thermal (melting and crystallization) behavior, smoke point and solid fat content (SFC), of structured lipids (SLs) produced by interesterification of coconut and palm oils catalyzed by two commercial lipases (Thermomyces lanuginosa imobil/TLIM and Novozyme 435). The results showed that SLs produced by 5 hours of interesterification had physical characteristics differing from the original blended lipids. SLs obtained by both enzyme systems exhibited a higher and wider range of melting temperature (higher enthalpy value, ΔH), lower and wider range of crystallization temperature (higher ΔH), lower smoke point, and lower SFC at 0°C and 10°C than those of their blended lipid counterpart. Furthermore, TLIM lipase produced SLs with a higher and wider range of melting temperature, lower and wider range of crystallization temperature, lower smoke point and SFC at 0°C and 10°C compared to those produced by Novozyme 435. The SLs produced have the potential to be used as an ingredient in refrigerated foodstuffs and more plastic than the original oil.
Mix uniformity is a critical quality control point in food manufacturing. Process analytical technology (PAT) provides new technological opportunities for fulfilling and perhaps replacing conventional sampling methods by proposing spectroscopic analyzers for measuring blend homogeneity. Many spectroscopic analyzers have been used in powder blending processes. Light-induced fluorescence (LIF) is the most rapid and consistent underutilized PAT. An experiment was conducted to evaluate the effects of DL-methionine concentration, moisture content (MC) and bulk density (BD) on LIF responses. Fluorescent responses to powder mixtures comprising 0.05−0.50% w/w fluorescent active pharmaceutical ingredient (API) were reported. Approximate density ranges of 6.5−20% w/w for MCs and 0.55−0.65 g/ml for animal food were also evaluated. Results indicated that DL-methionine concentration and MC were statistically significant factors affecting the LIF response, but the effect of BD was not statistically significant. DL-methionine concentration from 0.05% to 0.50% caused a linear increase of LIF signals with y = 41.04x + 715.8, R2 = 0.990 fitted to the data. Increasing MC from 6.5% to 20% w/w caused decreasing LIF although y = -45.50x + 1037 could not explain LIF variation versus MC because of low coefficient of determination (R2 = 0.851).
The present paper deals with the novel approach for clustering using the image feature of stabilization diagram for automated operational modal analysis in parametric model which is stochastic subspace identification (SSI)-COV. The evolution of automated operational modal analysis (OMA) is not an easy task, since traditional methods of modal analysis require a large amount of intervention by an expert user. The stabilization diagram and clustering tools are introduced to autonomously distinguish physical poles from noise (spurious) poles which can neglect any user interaction. However, the existing clustering algorithms require at least one user-defined parameter, the maximum within-cluster distance between representations of the same physical mode from different system orders and the supplementary adaptive approaches have to be employed to optimize the selection of cluster validation criteria which will lead to high demanding computational effort. The developed image clustering process is based on the input image of the stabilization diagram that has been generated and displayed separately into a certain interval frequency. and standardized image features in MATLAB was applied to extract the image features of each generated image of stabilisation diagrams. Then, the generated image feature extraction of stabilization diagrams was used to plot image clustering diagram and fixed defined threshold was set for the physical modes classification. The application of image clustering has proven to provide a reliable output results which can effectively identify physical modes in stabilization diagrams using image feature extraction even for closely spaced modes without the need of any calibration or user-defined parameter at start up and any supplementary adaptive approach for cluster validation criteria.
Automated OMA (AOMA), automatization, clustering, operational modal analysis, stabilization diagram
Plant acoustic frequency technology (PAFT) is a technology that utilizes sound waves in the form of frequencies within the audiosonic threshold. The purpose of this study was to test PAFT by using Javanese gamelan music entitled puspawarna on the productivity of vegetative growth in Kailaan (Brassica alboglabra) plant. PAFT exposure time was given to plants during the morning and evening. The frequencies ranged in 3-5 kHz, 7-9 kHz and 11-13 kHz. In addition, sound exposure times were for 1 hour, 2 hours and 3 hours. Based on the statistical analysis, the results indicated that the frequency and sound exposure time had a significant effect on plant wet weight, plant length, and stomata openings. The best frequency for Kailaan plant growth was in 3-5 kHz with the best exposure time of 3 hours. The combination of frequency and sound exposure time resulted in the most optimal stomata openings (stomata diameter at the top of the leaves of 89.19 ~ 93.45 µm and stomata diameter at the bottom of the leaves of 136.69 ~ 140.74 µm) with chlorophyll of 80.86 chlorophyll content index (cci), plant length of 47.33 cm, plant wet weight of 84 g, area of leaves of 207.06 cm2, plant height of 9.4 cm, and number of leaves of 11 strands.
Most of the classification algorithms discover flat Fuzzy Classification Rules (FCRs) in If- Then form. The knowledge discovered in the form of FCRs allows us to deal with vague, inexact and incomplete premises, however, it ignores exceptions and hierarchies that may exist in data. The simple FCRs enlarge the size of Rule Bases (RBs) with the presence of duplicate clauses that can be removed by arranging the rules in a hierarchical fashion. Moreover, such rules infer incorrect conclusions in the presence of exceptional conditions. This paper proposes the discovery of accurate, interpretable and interesting rules in a novel form named as Fuzzy Hierarchical Censored Classification Rules (FHCCRs) using a Genetic Algorithm approach. The GA design for discovering FHCCRs includes designing of suitable encoding scheme, fitness function and genetic operators. The suggested approach works in three phases: i) fuzzifying a dataset in a pre-processing step, ii) applying a genetic algorithm for discovering FHCCRs and iii) merging FHCCRs into bigger hierarchies in a post-processing step. The proposed approach is applied to five benchmark datasets. It successfully discovers FHCCRs which contain exceptions (also referred as censors) as well as hierarchies. The knowledge discovered in the form of FHCCRs enriches rule bases in respect of interpretability and interestingness.
Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPIs (Key Performance Indicators) of the individual cloud providers. This paper proposes an FLA-SLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability.
This paper proposes a clustering approach based on Modified Mutation strategy in the Differential Evolution (MMDE). Differential evolution is an evolutionary computation technique used for optimization. Though DE is very efficient, it sometimes suffers from the issue of slow convergence and the difficulty of achieving a global solution. To overcome these issues, in this paper, a modified mutation method was developed, which maintained the balance between exploration and exploitation. The objectives of modification were to achieve a higher rate of convergence and to obtain better cluster efficiency. The proposed form of modification had been applied on probabilistic environment to define the differential vector through randomly selected members and to obtain the best solution. Over the number of benchmark dataset, clustering efficiency had been estimated and compared with Conventional Differential Evolution (CDE) as well as Particle Swarm Optimization. The proposed method had been tested on a number of benchmark datasets. Experimental results had shown that MMDE had better and consistent clustering efficiency when compared to Conventional Differential Evolution (CDE) and Dynamic Weighted Particle Swarm Optimization (DWPSO).
Monocular depth estimation is gaining much interest in the computer vision community because it has broad applications in autonomous driving systems, robotics, and scene understanding. Significant progress has been made in solving the monocular depth estimation problem using deep learning techniques. Unsupervised learning methods are particularly appealing since the problem can be treated as an image reconstruction task, thereby forgoing the need for ground-truth depths. This paper presents an unsupervised approach to training convolutional neural networks for monocular depth estimation by introducing a novel architecture called DFRNets. DFRNets shares weight parameters between the image reconstruction sub-network and the disparity refinement sub-network and adopts a multi-scale structure for disparity predictions. The proposed method computes dense disparity maps directly from monocular images and refines them in an end-to-end fashion to reduce visual artifacts and blurred boundaries, thereby improving the methods overall performance. Experiment results using the KITTI test set showed that the proposed method outperformed many state-of-the-art methods, since it achieved the best performance on the two distance ranges: 0−80 meters and 1−50 meters. Moreover, the qualitative results revealed that the method generated more detailed and accurate depth maps of the scenes, with no border artifacts around the image boundary.
The use of graphical knowledge representation formalisms with a representational vocabulary agreement of terms of conceptualization of the universe of discourse is a new high potential approach in the ontology engineering and knowledge management context. Initially, concept maps were used in the fields of education and learning. After that, it became popular in other areas due to its flexible and intuitive nature. It was also proven as a useful tool to improve communication in corporate environment. In the field of ontologies, concept maps were explored to be used to facilitate different aspects of ontology development. An essential reason behind this motivation is the structural resemblance of concept maps with the hierarchical structure of ontologies. This research aims to demonstrate quantitative evaluation of 4 different hypotheses related to the effectiveness of using concept maps for ontology conceptualization. The domain of Quran was selected for the purpose of this study and it was conducted in collaboration with the experts from the Centre of Quranic Research, Universiti Malaya, Kuala Lumpur, Malaysia. The results of the hypotheses demonstrated that concept mapping was easy to learn and implement for the majority of the participants. Most of them experienced improvement in domain knowledge regarding the vocabularies used to refer to the structure of organization of the Quran, namely Juz, Surah, Ayats, tafsir, Malay translation, English translation, and relationships among these entities. Therefore, concept maps instilled the element of learning through the conceptualization process and provided a platform for participants to resolve conflicting opinions and ambiguities of terms used immediately.
There are many variables involved in the real life problem so it is difficult to choose an efficient model out of all possible models relating to analytical factors. Interaction terms affecting the model also need to be addressed because of its vital role in the actual dataset. The current study focused on efficient model selection for collector efficiency of solar dryer. For this purpose, collector efficiency of solar dryer was used as a dependent variable with time, inlet temperature, collector average temperature and solar radiation as independent variables. Hybrid of the least absolute shrinkage and selection operator (LASSO) and robust regression were proposed for the identification of efficient model selection. The comparison was made with the ordinary least square (OLS) after performing a multicollinearity and coefficient test and with a ridge regression analysis. The final selected model was obtained using eight selection criteria (8SC). To forecast the efficient model, the mean absolute percentage error (MAPE) was used. As compared to other methods, the proposed method provides a more efficient model with minimum MAPE.
model selection, ordinary least square, robust regression, selection criteria, sparse regression
Since its debut in 2009, League of Legends (LoL) has been on a rise in becoming an extremely favoured multiplayer online battle arena (MOBA) game. This paper presented a logic mining technique to model the results (Win / Lose) of the LoL games played in 3 regions, namely South Korea, North America and Europe. In this research, a method named k satisfiability based reverse analysis method (kSATRA) was brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games was used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams from each region, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games was tested based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and CPU time. Results acquired from the computer simulation showed the robustness of kSATRA in exhibiting the performance of the LoL teams.
2 satisfiability, 2 satisfiability reverse analysis method, hopfield neural network, league of legends, logic mining
Clonal selection algorithm and discrete Hopfield neural network are extensively employed for solving higher-order optimization problems ranging from the constraint satisfaction problem to complex pattern recognition. The modified clonal selection algorithm is a comprehensive and less iterative immune-inspired searching algorithm, utilized to search for the correct combination of instances for Very large-scale integrated (VLSI) circuit structure. In this research, the VLSI circuit framework consists of Boolean 3-Satisfiability instances with the different complexities and number of transistors are considered. Hence, a hybrid modified clonal selection algorithm with discrete Hopfield neural network is well developed to optimize the configuration of VLSI circuits with different number of electronic components such as transistors as the instances. Therefore, the performance of the developed hybrid model was assessed experimentally with the standard models, HNNVLSI-3SATES and HNNVLSI-3SATGA in term of circuit accuracy, sensitivity, robustness and runtime to complete the verification process. The results have demonstrated the developed model, HNNVLSI-3SATCSA produced a minimum error (consistently approaching 0), better accuracy (more than 80%) and faster computational time (less than 125 seconds) against changes in the complexity in term of the number of transistors. Furthermore, the developed hybrid model is able to minimize the computational burden and configurational noises for the variant of VLSI circuits.
Interactions between multispecies are usual incidence in their habitats. Such interactions among the species are thought to be asymmetric in nature, which combine with environmental factors can determine the distributions and abundances of the species. Most often, each species responds differentially to biotic interactions and environmental factors. Therefore, predicting the presence-absence of species is a major challenge in ecology. In this paper, we used mathematical modelling to study the combined effects of biotic interactions (i.e. asymmetric competition) and environmental factors on the presence-absence of the species across a geographical region. To gain better insight on this problem, we performed invasion and numerical simulation analyses of the model of multispecies competitive dynamics. Different threshold values of competition coefficients were observed, which result in different phenomena; such as coexistence of species and priority effects. Consequently, we propose that asymmetric biotic interactions, combined with environmental factors can allow coexistence of relatively weak and strong species at the same location x
Coexistence, competition, invasion point, priority effects, threshold values
Dengue fever (DF) is a global health problem and considered to be endemic in Malaysia. Conventional mosquito traps currently applied as vector control do not effectively reduce Aedes mosquito population. AedesTech Mosquito Home System (AMHS) is an autocidal ovitraps for Aedes mosquitoes that uses the lure and kill concept and is expected to be able to reduce Aedes mosquito population. The effectiveness of AMHS in reducing Aedes mosquito population was investigated in Block A, B and D (control) of the 17th College, Universiti Putra Malaysia (UPM). For the first two weeks (pre-intervention), the conventional ovitraps were used to obtain the initial abundance of mosquito population in Block A, B and D. Subsequently, AMHS was used for the next three months and again followed by the conventional ovitrap for the final two weeks (post-intervention). Ovitrap Index, Hatching Index and percentage of emergence of adult mosquitoes were calculated once every two weeks. Data were analysed using Paired Sample T-test. Values were considered significant at p≤0.05. The Ovitrap Index that indicates the mosquito population at Block A and B was significantly higher (p≤0.05) than of Block D. Hatching Index of AMHS was significantly lower (p≤0.05) then conventional ovitraps. All mosquito eggs collected in AMHS did not develop into adult mosquitoes. There was a significant reduction (p≤0.05) in the mosquito population between the pre- and post-intervention. In conclusion, AMHS was effective in reducing the mosquito population in 17th College, UPM. Therefore, it is believed to be a very promising vector management option to control the incidence of DF.
High-intensity exercise acutely improves suppression of appetite in populations with normal body mass index (BMI). However, whether moderate intensity exercise (MIE) and high-intensity exercise (HIE) can elicit similar (or greater) appetite suppression effects for obese populations are still relatively unknown. The main aim is to investigate the acute effects of MIE and HIE on the appetite score, eating behaviour and blood glucose regulation among the obese population. Twelve obese participants (age: 20.8 ± 1 yr, BMI: 34.1 ± 3 kg·m-2, VÌ‡o2max: 30.7 ± 3 ml·kg·min-1) were randomly allocated, in a crossover manner, with a 7-day interval in between (1) MIE (cycling at 60-75% HRmax), (2) HIE (cycling at 80-95% HRmax, 8-sec sprint x 12 sec rest) and (3) control (CON) condition after a 10-hr overnight fast. Physiological (fasting blood [glucose] and 24-hr calorie intake) and psychological responses (Three Factor Eating Questionnaire-R18, TFEQ-R18, and appetite score using Visual Analog Scale, VAS) were recorded prior to and after exercise interventions. Both MIE and HIE significantly reduced the calorie intake compared to CON (P<0.05), despite no changes in psychological measures were related to appetite (i.e. TFEQ-R18 and VAS) between the groups (P>0.05). A difference was found in fasting blood [glucose] level between trials in MIE (P<0.05), but not following the HIE condition (P>0.05). In response to acute intervention, both MIE and HIE improved some psychological appetite score and attenuated daily energy consumption; these positive effects could benefit obese and diabetic populations.
Appetite, endurance, energy intake, high intensity exercise, moderate intensity exercise, obese
The potential of Moringa oleifera Lam. (Moringaceae) and Centella asiatica (L.) Urban (Apiaceae) extracts (TGT-PRIMAAGE) in slowing the decline of memory and learning activity was investigated using D-galactose-induced ageing rat model. The extracts were profiled and standardised based on markers identified using LC/MS-QTOF. Toxicity study of the extract was done, and the rat did not show any sign of toxicity. The extract was orally administered to the rat and dose dependent (100, 500 and 1000 mg/kg) efficacy were investigated. The rats were subjected to Morris Water Maze whereby 3 parameters were studied (number of entry to platform, latency and novel object recognition). Plasma was collected for the determination of catalase (CAT) activity and levels of malondialdehyde (MDA) and advanced glycation end products (AGEs). The activity of acetylcholinesterase (AChE), level of acetylcholine (ACh) and lipid peroxidation (LPO) were measured using the brain lysates. Significant improvement (p < 0.05) was seen in the memory and learning abilities in the aged rats that received medium and high dose of TGT-PRIMAAGE, and tocotrienol. Rats treated with TGT-PRIMAAGE had also shown improved CAT activity and resulted in reduced LPO. The level of ACh was found increased in parallel with the reduced AChE activity. The capabilities of learning and memory of the TGT-PRIMAAGE treated rats were enhanced via inhibition of AChE activity and subsequently increased level of ACh.
Gallic acid (GA) is a phenolic compound found in almost all plants and has been reported to possess powerful health benefits such as anti-oxidant, anti-inflammatory, anti-cancer, and anti-diabetic properties. However, GA suffers a short half-life when administered in vivo. Recent studies have employed graphene oxide (GO), a biocompatible and cost-effective graphene derivative, as a nanocarrier for GA. However, the toxicity effect of this formulated nano-compound has not been fully studied. Thus, the present study aims to evaluate the toxicity and teratogenicity of GA loaded GO (GAGO) against zebrafish embryogenesis to further advance the development of GA as a therapeutic agent. GAGO was exposed to zebrafish embryos (n ≥ 10; 24hr post fertilization (hpf)) at different concentrations (0-500 µg/ml). The development of zebrafish was observed and recorded twice daily for four days. The toxicity of pure GO and GA was also observed at similar concentrations. Distilled water was used as control throughout the experiment. A significantly high mortality rate, delayed hatching rate and low heartbeat were recorded in embryos exposed to GO at concentrations of ≥ 150 µg/ml at 48 hr (p<0.01), 72 hr (p<0.001) and 96 hr (p<0.0001) post-exposure. Interestingly, all measured parameters were significantly improved in embryos exposed to the same concentration of GAGO (100-150 µg/ml), which was comparable to control group at all-time points. The present data demonstrated that GAGO is safe to be used at low concentration exposure (0-150 µg/ml), but further study has to be conducted to correlate the toxicity of GAGO with its effective concentration in in vitro and in vivo model.
Studies on the spatiotemporal distribution monitoring of light fishing fleets are limited due to extensive study area, data availability, dynamic distributions, limited monitoring technology, and perception of the fishers. This study aims to monitor and estimate the density of light fishing fleets, representing the centre of fishing areas. Using the visible infrared imaging radiometer suite of boat detection data combined with actual fishing data, the pattern of spatiotemporal distribution of light fishing fleets was analysed, displayed with the variations in sea surface temperature and chlorophyll-a concentrations. This study was carried out at west Sumatera waters. The actual fishing data, light fishing fleets data, and environment parameter data were collected in 2014-2018. The calculation of the geographical distribution was carried out using the geographical information system models with four spatial indicators, i.e., central tendency, spatial dispersion, directional dispersion, and directional trends. The results showed various patterns and behaviours on light fishing fleets spatial distribution. We also revealed the spatiotemporal pattern dynamic of the geographic distribution of light fishing fleets in the west Sumatera waters. The distribution pattern was random compared to the sea surface temperature distribution. On the other hand, it was quite centralized following the chlorophyll-a concentration. The distribution of light fishing fleets was dominant in the area with high chlorophyll-a concentration.
The removal efficiency of malachite green (MG) dye ions by using a bulk liquid membrane was investigated. The transport of MG dye ions was accomplished using a bulk liquid membrane, which contained salicylic acid as carrier, sodium hydroxide as extractant, and acetic acid as acceptor. Different factors were examined for removal efficiency, such as pH of the acceptor phase in the range of pH 3−7, initial dye concentration at 20−60 mg/L, and concentration of carrier in the range of 8−12 mg/L. Box-Wilson method for experimental design was adopted to establish the relationships between these operating variables attributed to affecting the treatment process, and the mechanism of dye transport from feeding to acceptor phase. The results indicated that the optimum conditions for dye extraction were achieved at pH 6, dye concentration of 20 mg/L, and carrier concentration of 12mg/L. The implementation of these parameters on the prepared dye solution revealed a relatively high removal efficiency of MG dye (98.4%). A Box-Wilson model was modified and found to fit the effect of variable response, with a correlation coefficient (R) = 0.977 and root-mean square error (S) = 1.8%. This work proved that liquid membrane was effectively useful for dye removal from the wastewater.
The petroleum industry is facing a critical issue in transporting crude oil through the pipelines from the seashore where crude oil is being drilled off. The problem arises when crude oil exhibits higher sensitivity to the changes of temperature. This actually causes some alterations occurring in the composition, pour point of the oil and flow of the crude oil itself. Thickening of some components such as wax and asphaltenes causes the deposition to occur in the pipelines due to changes in temperature. Eventually, these depositions cause blockage of the pipelines due to reduction in the diameter of the pipelines and causing disruption in the flow of crude oil. The experiments were carried by mixing different ratio of polymer and solvent such as ethylene-vinyl acetate (EVA40) with 40% vinyl acetate, methylcyclohexane (MCH), toluene and butanol together to form an inhibitor. The response surface methodology (RSM) had been used to identify the best formulation of solvents that could act as inhibitors. The final results show that the most optimum ratio of inhibitor that gives the highest reduction in viscosity of the crude oil is 30% EVA, 30% MCH and finally 40% ratio of solvent which is either toluene or butanol.
Fine resolution (hourly rainfall) of rainfall series for various hydrological systems is widely used. However, observed hourly rainfall records may lack in the quality of data and resulting difficulties to apply it. The utilization of Bartlett-Lewis rectangular pulse (BLRP) is proposed to overcome this limitation. The calibration of this model is regarded as a difficult task due to the existence of intensive estimation of parameters. Global optimization algorithms, named as artificial bee colony (ABC) and particle swarm optimization (PSO) were introduced to overcome this limitation. The issues and ability of each optimization in the calibration procedure were addressed. The results showed that the BLRP model with ABC was able to reproduce well for the rainfall characteristics at hourly and daily rainfall aggregation, similar to PSO. However, the fitted BLRP model with PSO was able to reproduce the rainfall extremes better as compared to ABC.
The present study deals with reaming of Al6061/SiC metal matrix composite. For the fabrication of the composite, stir casting technique was used. In the castings, 5 and 10 weight percentages of Silicon Carbide (SiC) 23µm size was used as the reinforcing material. The tensile and hardness tests carried out on the specimen indicated that it increased with the addition of SiC. The images from scanning electron microscope showed the fair distribution of reinforcement. After drilling 7.8 mm diameter holes, reaming was performed with 8mm diameter straight fluted HSS reamer under dry condition at cutting speeds of 18 and 24 m/min and feed rates of 0.2 and 0.4 mm/rev. Torque required for reaming was measured using 4 component Drill tool Kistler dynamometer 9272A. The estimation of progressive wear of the reamer was undertaken using a profile projector. With the introduction of SiC as reinforcement, the wear rate of the reamer increased as the reinforcement was highly abrasive in nature. The performance of HSS machine reamer was evaluated in terms of reaming torque, tool wear and surface roughness of the hole.