Measuring the influence of materials composition on nano scale roughness for wood plastic composites by AFM

Composition of mango wood Polypropylene composites (WPCs) are prepared through melt compounding with the help of micro conical twin screw co-rotating extruder then injection molding of WPCs pellets. Polymer matrix composites are formulated for five compositions with a different weight ratio of wood, virgin polypropylene, recycled polypropylene and coupling agent. Average roughness (Ra), mean peak-to-valley height (Rz) and maximum roughness (Rmax) used to evaluate surface characteristics of samples by AFM. Also measures the 3D surface and surface roughness profile to examine the same. Result from this work clearly shows that recycled Polypropylene based composites with and without coupling agent have a smooth surface in comparison with virgin Polypropylene based composites for same operating variable conditions. The surface smoothness of WPCs improved with decreasing of wood flour content for all samples. By addition of coupling agent surface smoothness of the WPCs increases and the value of Ra decrease from 2.17 to 1.04 nm for recycle polypropylene wood based composite. Surface of composites is also examined with the help of FESEM images. FESEM feature proved that MAPP coupled is shown good bonding strength and smoothness in comparison to none coupled composite for the same class.     

Theoretical–experimental evaluation of different biomaterials for parts obtaining by fused deposition modeling

Although microFDM (microFused Deposition Modeling) has been widely used with biomaterials, there is not enough information about their flow models and the appropriate values for operating conditions. The aim of this paper is to provide a criterion to establish feasible ranges of temperature and shear stress to carry out fused deposition of the biomaterials studied at microscale (hundreds of μm). Materials used were (acrylonitrile–butadiene–styrene), PLA (polylactic acid), and PCL (polycaprolactone). Polyvinyl alcohol was also included in this study, although its quick thermal degradation has led to poor dimensional stability parameters and, therefore, it has been considered inappropriate for this application. Viscosity models were obtained in a 300 μm nozzle microFDM device manufactured by electroforming techniques. These models were used in a simulation analysis whose results show a relationship between the convergence of the algorithm and the characteristics of the filament obtained in equivalent experimental testing.Besides, melt fracture and relevance of swelling was assessed by optical microscopy observation. This information allows to define operating conditions (in terms of temperature and shear rate) to obtain homogeneous morphological characteristics of the microextrudate. Furthermore, the procedure stated could be used in tissue engineering to delimit feasible operating conditions to manufacture scaffolds by fused deposition modeling.     

Combining Scott-Knott and GR&R methods to identify special causes of variation

Measurement error is an unavoidable source of variation in any decision-making process based on experimental research. Components of variation due to measurement system and manufacturing process must be estimated and special causes of variation should be reduced whenever possible. GR&R (gage repeatability and reproducibility) studies quantify these sources of variation by using analysis of variance. The main contribution of this paper is to conjoin GR&R and the multiple comparisons method of Scott-Knott in order to help practitioners identifying special causes of variation in empirical studies. Stainless steel cladding process has been evaluated to validate the proposed procedure. The experimental findings have shown that the well-structured method based on Scott-Knott test was effective in indicating the source of error due to reproducibility.     

Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors

This paper investigates the current monitoring for effective fault diagnosis in induction motor (IM) by using random forest (RF) algorithms. A rotor bar breakage of IM does not derive in a catastrophic fault but its timely detection can avoid catastrophic consequences in the stator or prevent malfunctioning of those applications in which this sort of fault is the primary concern. Current-based fault signatures depend enormously on the IM power source and in the load connected to the motor. Hence, homogeneous sets of current signals were acquired through multiple experiments at particular loading torques and IM feedings from an experimental test bench in which incipient rotor severities were considered. Understanding the importance of each fault signature in relation to its diagnosis performance is an interesting matter. To this end, we propose a hybrid approach based on Simulated Annealing algorithm to conduct a global search over the computed feature set for feature selection purposes, which reduce the computational requirements of the diagnosis tool. Then, a novel Oblique RF classifier is used to build multivariate trees, which explicitly learn optimal split directions at internal nodes through penalized Ridge regression. This algorithm has been compared with other state-of-the-art classifiers through careful evaluation of performance measures not encountered in this field.     

Source detection: Influence of the Doerbecker’s noise reduction algorithm; Simulation in the case of binaural cochlear implant coding

Cochlear implant (CI) provides good auditory performances in quiet, but recognition in noisy environments is still a challenge for CI users. Bilateral hearing can improve speech perception; it has been shown with normal hearing aids, but how source localization is affected is still an open question.In this study source localization is studied in the case of a binaural cochlear implant coding. Signal was received on the two ears and then processed using the classical noise reduction algorithm, suggested by Doerbecker and based on the spectral properties of the signal. Two correction strategies, Ephreim & Malah and Scalart, have been inserted in the processing chain.Tests were conducted, “in simulation” with 20 normal hearing listeners who received a cochlear implant coded signal. An analytic interpretation of the results offers a frame to quantify the different contributions.Results show that the Doerbecker’s algorithm affects the source localization. This effect was lowered when a percentage of the input signal was re-injected into the system. Both correction strategies were equivalent.     

Investigation for dimensional accuracy of AMC prepared by FDM assisted investment casting using nylon-6 waste based reinforced filament

In the recent years, the whole world is facing a serious problem in handling nylon-6 wastes of various societies like: fibres/textile, household, carpets, tires, military supplies, etc. Ordinary recycling process of such wastes is costlier process and also omits its major mechanical properties. In the present research work, nylon-6 waste (collected from local plastic based industry) has been recycled, through extrusion process, in the form of fused deposition modelling (FDM) feedstock filament. This alternatively developed FDM filament has been successfully used to fabricate sacrificial patterns for investment casting process (ICP). The process was started with investigating the melt flow index (MFI) of collected nylon-6 waste which was matched with the commercial FDM filament through reinforcement. Finally, single screw extruder of has been used for the development of FDM filament proportion with a mixture of 60% nylon-6, 30% Al and 30% Al₂O₃ (by wt.). The resulting FDM patterns have been used in ICP for development of aluminum matrix composite (AMC). Taguchi L9 was used to investigate the affect of process parameters (volume of pattern, density of pattern and number of IC coatings) on dimensional accuracy of AMC developed. Apart from suggesting an alternative method for management and recycling of nylon-6 waste, the present research work also described a new approach for the development of AMC with tailor made properties.     

An investigative study into the sensitivity of different partial discharge φ-q-n pattern resolution sizes on statistical neural network pattern classification

This paper investigates the sensitivity of statistical fingerprints to different phase resolution (PR) and amplitude bins (AB) sizes of partial discharge (PD) φ-q-n (phase-amplitude-number) patterns. In particular, this paper compares the capability of the ensemble neural network (ENN) and the single neural network (SNN) in recognizing and distinguishing different resolution sizes of φ-q-n discharge patterns. The training fingerprints for both the SNN and ENN comprise statistical fingerprints from different φ-q-n measurements. The result shows that there exists statistical distinction for different PR and AB sizes on some of the statistical fingerprints. Additionally, the ENN and SNN outputs change depending on training and testing with different PR and AB sizes. Furthermore, the ENN appears to be more sensitive in recognizing and discriminating the resolution changes when compared with the SNN. Finally, the results are assessed for practical implementation in the power industry and benefits to practitioners in the field are highlighted.     

Measurement of pressure distribution on a membrane of a pump for biomedical applications through capacitive film sensors

The analysis of loads applied on pumps used for biomedical applications is very important for their design, to obtain high reliability and duration of their components.In this work, a capacitive sensor matrix is proposed for the measurement of the time history of pressure distribution on the membrane of a pump for biomedical applications. Capacitive sensor matrix are usually employed for quasi-static analysis, thus specific calibration methods are proposed for the metrological characterization of the measurement system. Pressure measurements are then performed to provide information about the loads acting on the most critical components of the pump.     

Comparative study on optimum design of rolling element bearing

Long fatigue life is the most important objective in the optimum design of rolling element bearing. In the present study the fatigue life of a radial ball bearing is maximized. The nonlinear constrained optimization problem has been solved using particle swarm optimization algorithm and a hybrid PSO and Teaching Learning based Optimization algorithm. The algorithm uses a ranking method of constraint handling and contact stress has been introduced as a new constraint. The results have been compared with the established available results. A constraint violation study has been carried out to prioritize the constraints. A convergence study has been performed to identify the key design variables. Encouraging results in terms of objective function values and CPU time have been reported in this study.