Surface Roughness Prediction in Grinding Ti using ANFIS Hybrid Algorithm

Intelligent manufacturing is needed, and many techniques and tools have been developed with this in mind. Over time, many of these techniques have been combined, and hybrid approaches have provided better results in shorter times, leading to a more precise prediction of outcomes when compared to the use of individual tools. This research focused on grinding Ti-6Al-4V workpiece material with a Carbon nanotube (CNT) incorporated grinding wheel. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict surface roughness which was taken as the output of choice for this study. A new hybrid of ANFIS with Genetic Algorithm (ANFIS-GA) was then proposed to see if this prediction method could obtain greater precision. The regression analysis predicted the experimental model’s linear relationship to surface roughness, and the effect of grinding process parameters on surface roughness was analysed using the sensitivity analysis method

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed

Optimization of robot plasma coating efficiency using genetic algorithm and neural networks / S.Prabhu and B.K.Vinayagam

This work describes the Taguchi analysis coupled with Artificial Neural network and Genetic algorithm to optimize the robot deposition parameters used for plasma coating on titanium aluminum alloy material. L27 orthogonal array have been used for coating the work piece using robot. The Arc current (Amp), Arc voltage (volt), powder feed rate(mm/sec), substrate Surface Roughness (μm), Spray gun distance (mm) and TiO2 content in feedstock (%) have been considered as input parameters and coating efficiency is considered as output parameters. Using feed forward Artificial Neural Networks (ANNs) trained the experimental values with the Levenberg–Marquardt algorithm, the most influential of the factors were determined. Regression analysis are used to predict the robot coating efficiency and ANOVA analysis are used to contribute the individual process parameter on robot deposition coating efficiency. The developed mathematical model was further analyzed with Genetic algorithm to find out the optimum conditions leading to the maximum coating efficiency

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Ultrasonic assisted machining

A commercially available DMG MORI ULTRASONIC 65 monoBLOCK machining centre was installed in WMG in 2013 and has been primarily used to machine aerospace grade materials such as carbon fibre reinforced plastic (CFRP) and titanium alloy Ti 6Al-4V (individually and stacked) and 2000 / 6000 series aluminium alloys. Rather than discuss a single set of experimental work in detail, this paper discusses some of the issues that have been encountered when applying the technique of ultrasonic assisted machining (UAM) and some of the effects that have been observed using examples from the research conducted so far to illustrate some of the more important findings

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Investigation into stability and thermal-fluid behaviour of hybrid nanofluids as heat transfer fluids

Thesis (PhD (Mechanics))--University of Pretoria, 2021.The need to improve the poor thermal conductivity of conventional fluids to produce adequate heat transfer fluid cannot be over-emphasized, knowing fully well that heat transfer is key in any engineering process line. Hence, the birth of nanofluids, which is the formulation of a composite of suspended nanoparticles in a basefluid. Nanofluids have found wide applications ranging from heat exchangers, electronic cooling, automotive industry, medical, military, solar energy, manufacturing industry, to mention but a few. But the limitations of nanofluids led to the entrance of a new working fluid named binary nanofluid and ternary nanofluid. This study experimented with the trio influence of temperature (T), percent weight ratios (PWRs), nanoparticles size (NS) on the thermophysical behaviour of MgO–ZnO/Deionised water binary nanofluids (BNFs). 20 nm nano-size of ZnO nanoparticles were hybridised with MgO nanoparticles of nano-sizes 20 nm and 100 nm, and dispersed in deionised water to prepare 0.1 vol% binary nanofluids for percent weight ratios of MgO:ZnO (20:80, 40:60, 60:40 and 80:20). The viscosity (μ), electrical conductivity (σ), pH, and thermal conductivity (κ) of the binary nanofluids were experimentally evaluated for temperature 20 to 50 °C. Morphology was checked, and stability was monitored. The impact of temperature, PWRs, and nano-size on the pH, μ, σ, and κ of the binary nanofluid were ordered as PWR >NS >T, NS> PWR>T, T>NS >PWR, and T >NS >PWR, respectively. Using the obtained experimental dataset, correlations were proposed for the thermal property of each binary nanofluid as a function of temperature. Also, investigating the trio impact of PWR, temperature and � on the thermophysical characteristics of MgO-ZnO/DIW BNFs, to help close up the scarce literature gap. 20 nm nanoparticle sizes of MgO and ZnO were hybridized together and dissolved in deionized water to formulate 0.1 vol% and 0.05 vol.% binary nanofluids (NFs) for PWR of 20:80, 40:60, 60:40, 80:20 (MgO:ZnO). The κ for all BNFs was enhanced under the impact of rising temperature, with maximum κ enhancement of 5.60% and 22.07% relative to the deionised water (DIW) achieved for 0.05 vol% and 0.10 vol%, separately. The σ was enhanced slightly under the influence of increasing temperature, with maximum enhancement of 21.82% and 30.91% achieved for 0.050 vol% and 0.10 vol%, respectively. In addition, viscosity under temperature increase exhibited a decreasing pattern for all nanohybrids and basefluid. Furthermore, to better harness the benefit of the BNFs for thermal application, thermoelectrical conductivity (TEC) was evaluated with BNFs of 0.05 vol% observed to have higher TEC values than 0.10 vol% BNFs. The BNFs were found suitable as thermal fluids. A novel manner of furthering thermo-convection behaviour of thermal applications is the use of BNFs as heat transfer fluids. This study experimented the natural convection behaviour of MgO-ZnO NPs suspended in basefluid for � = 0.050 vol.% and 0.10 vol% at percent weight ratios of 20:80, 40:60, 60:40, 80:20 (MgO:ZnO) inside a square enclosure. Factors like Rayleigh number, Nusselt number (Nuav), coefficient of convective heat transfer (hav), and heat transfer rate (Qav) for various temperatures (20°C to 50°C) were examined. PWRs and temperature gradient of BNPs inside the binary nanofluids was observed to augment Nuav, hav, and Qav. Also, highest improvement of 72.60% (Nuav), 76.01% (hav), and 72.20% (Qav) was achieved. Employing BNFs in square enclosure yielded fine improvement for natural convection behaviour. Artificial intelligence (AI) methods, like artificial neural network (ANN) and surface fitting method were deployed to model the thermal conductivity of BNFs. For the ANN model, a learning algorithm was developed to determine the optimum neuron number. The ANN having 19 neurons in the inner layer got the optimized performance. A surface fitting method was also used on the experimental data, and the generated surface shows the behaviour of the BNFs. The outcome affirmed that the designed ANN model is best for predicting the thermal conductivity of MgO-ZnO/DIW binary nanofluids for different temperatures, nanoparticle sizes, PWRs and volume concentration over the surface fitting method.University of Pretoria Postgraduate Bursary for Doctoral Students.Olabisi Onabanjo University, Ago-Iwoye, Nigeria.Tertiary Education Trust Fund (TETFund), Abuja, Nigeria.Mechanical and Aeronautical EngineeringPhD (Mechanics)Unrestricte

Corrosion and Degradation of Materials

Studies on the corrosion and degradation of materials play a decisive role in the novel design and development of corrosion-resistant materials, the selection of materials used in harsh environments in designed lifespans, the invention of corrosion control methods and procedures (e.g., coatings, inhibitors), and the safety assessment and prediction of materials (i.e., modelling). These studies cover a wide range of research fields, including the calculation of thermodynamics, the characterization of microstructures, the investigation of mechanical and corrosion properties, the creation of corrosion coatings or inhibitors, and the establishment of corrosion modelling. This Special Issue is devoted to these types of studies, which facilitate the understanding of the corrosion fundamentals of materials in service, the development of corrosion coatings or methods, improving their durability, and eventually decreasing corrosion loss

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Proceedings of the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

This volume contains full papers presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, between September 4th and 6th, 2008.FC