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The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.

This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.

This research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.

The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

The main purpose of this paper is to investigate the influence of build orientation on the tensile properties of PolyJet 3D printed parts. Effects on manufacturing time and total cost per part are the secondary objectives.

Solid tensile specimens were prepared as per the American Society for Testing and Materials D638 standards and were manufactured in six different orientations by using the Objet260 Connex 3D printer. VeroWhitePlus RGD835 was used as the build material, with FullCure 705 as the support material. The specimens were tested for their tensile strength and elongation. The side surface and the fracture surface were analyzed using the Field Emission Scanning Electron Microscope-SIGMA HV-Carl Zeiss with Bruker Quantax 200-Z10 EDS detector. Scanning electron microscope images of each surface were obtained at various magnifications.

From the study, it can be concluded that build orientation has an influence on the tensile strength and the manufacturing cost of the parts. The microstructural analysis revealed that the orientation of surface cracks/voids may be the reason for the strength.

From literature survey, it is inferred that this study is sure to be among the first few under this topic. These results will help engineers to decide upon the right build orientations with respect to print head so that parts with better mechanical properties can be manufactured.

Friction stir processing (FSP) as a solid-state process has the potential for the production of effective aluminum matrix composites (AMCs). In this investigation, various ceramic particles including B₄C, TiC, SiC, Al₂O₃ and WC were incorporated as the dispersed phase within AA6082 aluminum alloy by FSP. The wear rate of the composite is then investigated experimentally by making use of a design of experiments technique where wear rate is evaluated as the output parameter. The input parameters considered include tool rotational speed, traverse speed, groove width and ceramic particle type. An artificial neural network (ANN) simulation was then used to describe the wear rate of the surface composites. The weights of the network were adjusted to minimize the mean squared error using a feed forward back propagation technique. The effect of the individual input parameters on wear rate was then inferred from the ANN models. Trends are presented and related to the associated microstructures observed. The TiC infused AMC displayed the lowest wear rate whereas the Al₂O₃ infused AMC displayed the highest, within the scope of the current investigation. The paper aims to discuss these issues.

The paper used ANN for the research study.

The finding of this paper is that the wear rate of AA6063 aluminum surface composites is influenced remarkably by FSP parameters.

Original work of authors.

The purpose of this paper is surface roughness prediction using pattern recognition for the aluminium hybrid metal matrix composite (HMMC).

Hybrid composites were manufactured using liquid metallurgy technique. The cast HMMC was machined using an industrial CNC turning centre and the machining vibration signals were acquired using an accelerometer. The acquired signals were processed and used to build a machine learning model for predicting surface finish based on the tool signature.

The authors established a technique for predicting and monitoring the surface quality during machining using a low cost accelerometer. It is capable of being integrated with the machine controller for online warning of deviations in surface roughness. The system is reconfigurable for any machining condition with a very short training period. The use of this model facilitates online surface roughness monitoring, avoiding the need for costly measuring equipment.

The model developed is innovative and not reported widely to the best of the authors' knowledge. The use of accelerometer‐based surface roughness prediction and control is an innovative approach for automation of machining process monitoring. These can be integrated into any existing machining centre as a standalone system or can be integrated into the CNC controller like Fanuc or Siemens.

The purpose of this study is to optimize the process parameters (wire feed rate (F), voltage (V), welding speed (S) and torch angle (A)) in order to obtain the optimum bead geometry (bead width (W), reinforcement (R) and depth of penetration (P)), considering the ranges of the process parameters using evolutionary algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm.

The modeling of welding parameters in flux cored arc welding process using a set of experimental data and regression analysis, and optimization using GA and SA algorithm.

The adequate mathematical model was developed. The multiple objectives were optimized satisfactorily by the GA and SA algorithms. The feasible solution results are very closer to the optimized results and the percentage error was found to be negligibly small.

The optimal welding parameters were identified in order to increase the productivity. The welding input parameters effect was found.

This Friction stir welding study aims to weld thick AA8011 aluminium plates, and the interface joints created with a variety of tool pin profiles were examined for their effects on the welding process.

Scanning electron microscopy and optical microscopy and X-ray diffraction were used to examine the macro and micro-structural characteristics, as well as the fracture surfaces, of tensile specimens. The mechanical properties (tensile, hardness tests) of the base metal and the welded specimens under a variety of situations being tested. Additionally, a fracture toughness test was used to analyse the resilience of the base metal and the best weldments to crack formation. Using a response surface methodology with a Box–Behnken design, the optimum values for the three key parameters (rotational speed, welding speed and tool pin profile) positively affecting the weld quality were established.

The results demonstrate that a defect-free junction can be obtained by using a cylindrical tool pin profile, increasing the rotational speed while decreasing the welding speeds. The high temperature and compressive residual stress generated during welding leads to the increase in grain size. The grain size of the welded zone for optimal conditions is significantly smaller and the hardness of the stir zone is higher than the other experimental run parameters.

The work focuses on the careful examination of microstructures behaviour under various tool pin profile responsible for the change in mechanical properties. The mathematical model generated using Taguchi approach and parameters was optimized by using multi-objectives response surface methodology techniques.

In the presented research work, the authors fabricated the multiple MS plate (Grade: IS 2062) specimens and applied a novel integrated computational TR_IF_MR_G approach with grey relational analysis (GRA) toward solving weld bead optimization problem in MIG welding procedure. The objective of research is to determine the optimum setting between MIG welding input process parameters, e.g. welding current, open circuit voltage and thickness of plate in attaining high tensile strength with weld bead geometry quality characteristics, e.g. bead width, reinforcement, penetration and dilution in investigating define MS specimens.

The Taguchi's L₉ orthogonal array (OA) design is respected to conduct the experiments on MS plate specimens to attain output objectives. Later, the evaluated multiple output objectives are transformed into single response by applying a novel integrated computational TR_IF_MR_G approach with GRA. Thereafter, the outset of signal-to-noise ratio (S/N ratio) accompanied by ANOVA (Analysis of variance) is explored to optimize objective function.

The computed results are confirmed by conducting the experiments on same identical specimens. The outcome of the confirmation tests yielded an improvement of 0.24454, 0.372486, 0.686635 and 0.4106846 in grey relational grade (GRG), overall ratio index, reference grade and full multiplicative index, respectively, after validating the results.

In the presented work, the authors constructed a novel integrated computational TR_IF_MR_G approach via clustering GRA, overall ratio index (ORI), full multiplicative index (FMI) with GRA-reference grade (RG) and tested as well as applied with Taguchi concept to attain objective of the research work.

The purpose of this paper is to investigate the influence of most predominant heat-treatment parameters on the wear behavior of Al6061 hybrid composite reinforced with 10 weight per cent SiC and 2 weight per cent graphite particles.

The aluminum hybrid composite was produced using stir casting process. Wear testing of heat-treated samples was carried out using a pin-on-disc apparatus. Experiments were conducted by applying design of experiments (DOE) technique. The experimental values were used for formulation of a mathematical model. The wear surfaces of composite specimens were analyzed using scanning electron microscope (SEM).

The volume loss of heat-treated composite initially decreased with increasing aging duration. This was followed by the attainment of a minimum and then a reversal in the trend at longer aging times. SEM micrographs of the wear surfaces of the composite show that the wear mechanisms were abrasion, delamination and adhesion.

In this paper, the hybrid composite was produced using stir casting route, and its wear properties after heat treatment were tested using pin-on-disc apparatus. It was found that heat treatment had a profound effect on the wear behaviour of the developed composite.

Once an uneven substrate is aligned, traditional control theories and methods can be used on it, so aligning is of great significance for the development of wire and arc additive manufacturing (WAAM). This paper aims to propose a shape-driven control method for aligning a substrate with slopes to expand the application of WAAM.

A substrate with slopes must be aligned by depositing weld beads with slopes. First, considering the large height differences of slopes, multi-layer deposition is needed, and the number of layer of weld beads must be ascertained. Second, the change in the deposition rate is controlled as a ramp function to generate weld beads with slopes. Third, the variation of the deposition rate must be fine-tuned to compensate for the deviation between the actual and theoretical layer heights at the deposition of each layer. Finally, the parameters of the ramp functions at the deposition of each layer are determined through an optimization method.

First, to model the response function of layer height to deposition rate, the experiments are conducted with the deposition rate jumping from 4 to 8 mm/s and from 8 to 4 mm/s. When the deposition rate jumps from 4 to 8 mm/s and from 8 to 4 mm/s, the difference in the height of each layer decreases as the number of layer increases. Second, the variation of the deposition rate can be fine-tuned based on the deviation between the measured and theoretical layer heights because the variation of the deposition rate is proportional to the layer height when the initial and end deposition rates are near 4 or 8 mm/s, respectively. Third, the experimental results demonstrate that the proposed method is effective for single-layer aligning and aligning a substrate with one or more slopes.

The proposed method can expand the application of WAAM to an uneven substrate with slopes and lays the foundation for aligning tasks focused on uneven substrates with more complex shapes.

The purpose of this paper is to verify how distributed cognition enhances collaborative problem-solving in the context of projects.

Using qualitative research and in-depth interviews, a sample of 32 project managers with experience in traditional and agile methods acting in Brazil and internationally participated in the research process. The analysis process, utilising coding techniques, involved stages: open, axial, coding and selective coding. These stages encompassed the evaluation of categories based on a hierarchy, in order to determine an appropriate level of abstraction that properly explains theoretical findings.

The results indicate that distributed team cognition is significant for collaborative problem-solving. The data from the interviews allowed the proposal of a model of cognition, and the identification of the elements that support it.

Understand how aspects of distributed team cognition can impact the behaviours of the project professional and contribute to problem-solving in the project environment.

The elements observed affects the collaborative problem-solving by presenting a model of distributed cognition, which is composed by directed communication, collective interaction, trust building and collaborative behaviour.