Parametric optimization of friction stir welding process of age hardenable aluminum alloys-ANFIS modeling

A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.

Thermal,corrosion and wear analysis of copper based metal matrix composites reinforced with alumina and graphite

The wear and corrosion resistances are important in marine applications, especially when it comes to structural support components like bearings, bushes and blocks. The copper hybrid metal matrix components are the new avenues explored in this front. A novel combination of alumina and graphite were considered as the reinforcements in a copper base for the development of a metal matrix composite.Power metallurgical techniques were used for the development of the MMC. The Vickers' s hardness value of 64.9 Hv has been observed by increasing the volume of alumina. Thermogravimetric analyses were carried out on material samples to estimate the exact sintering temperature and identified that 450-700℃ would be conducive. The TGA curves shows two step decomposition exists between 430 ℃-460 ℃. FT-IR analysis was done to confirm the peak values of the materials. FTIR exposed the peak value of 1600 cm^(-1) for alumina where as for Copper and graphite peak values have been 2840 cm^(-1) and 17260 cm^(-1) respectively. The potentio dynamic analysis was done to estimate the rate of corrosion on the samples. The sample with nano and micro reinforcements offered intensive resistance to corrosion. The presence of graphite minimized the weight loss of the samples during the corrosion test. Finally the wear rates of the samples were estimated using the Pin On Disc experimental setup. The samples with nano material reinforcement and with a maximum proportion of graphite exhibited a better wear rate of 1.52×10^(-12) m^2/kg under maximum load conditions.

Friction surfacing of AISI 316 over mild steel: A characteriation study

An attempt has been made with overlaying of stain-less steel on mild steel by the technique of friction surfacing. This investigation elaborates the excellence acquired by different combination of the process parameters used in friction surfacing specifically traverse speed of the cross slide, speed of rotation of the spindle and the uniaxial compressive load. Excellent overlaying has been obtained amongst the chosen materials. To which, the coating can be done with various set of process parameters. It has been observed that the bond strength of the coating was found to be at a maximum of 502 MPa by ram tensile test.Based upon this results the surface methodology was characterized with scanning electron microscope.For authenticating the results, the coated specimen was subjected to salt spray test. The bonding microstructure was characterized using optical microscopy and X-ray diffraction. Corrosion resistance of surfaced coatings was found to be more inferior to that of mechtrode material and greater with the substrate.

SYNTHESIS,CHARACTERIZATION AND PROPERTIES OF ORGANOCLAYMODIFIED POLYSULFONE/EPOXY INTERPENETRATING POLYMER NETWORK NANOCOMPOSITES

Organoclay-modified hydroxylterminated polysulfone (PSF)/epoxy interpenetrating network nanocomposites (oM-PSF/EP nanocomposites) were prepared by adding organophilic montmorillonite (oMMT) to interpenetrating polymer networks (IPNs) of polysulfone and epoxy resin (PSF/EP) using diaminodiphenylmethane (DDM) as curing agent.The mechanical properties like tensile strength,tensile modulus,flexural strength,flexural modulus and impact properties of the nanocomposites were studied as per ASTM standards.Different...

Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

Human Action Recognition(HAR)and pose estimation from videos have gained significant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,etc.Though considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so on.From the literature,it is observed that hard to deal with the temporal dimension in the action recognition process.Convolutional neural network(CNN)models could be used widely to solve this.With this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity recognition.The KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose estimation.In the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human pose.Then,an optimal DCNN model is developed to classify the human activities label based on the extracted key points.For improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch count.The experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

Hybrid approach for loss recovery mechanism in OBS networks

This letter reports a study of a hybrid burst assembly and a hybrid burst loss recovery scheme （delay-based burst assembly and hybrid loss recovery （DBAHLR）） which selectively employs proactive or reactive loss recovery techniques depending on the classification of traffic into short term and long term, respectively. Traffic prediction and segregation of optical burst switching network flows into the long term and short term are conducted based on predicted link holding times using the hidden Markov model （HMM）. The hybrid burst assembly implemented in DBAHLR uses a consecutive average-based burst assembly to handle jitter reduction necessary in real-time applications, with variations in burst sizes due to the non-monotonic nature of the average delay handled by additional burst length thresholding. This dynamic hybrid approach based on HMM prediction provides overall a lower blocking probability and delay and more throughput when compared with forward segment redundancy mechanism or purely HMM prediction-based adaptive burst sizing and wavelength allocation （HMM-TP）.