Implementation of Box–Behnken design to study the factors interaction impacts and modelling of the surface roughness of AL 6063 alloys during turning operations

This study focuses on the experimental investigation of the relationships between cutting parameters and their effects on surface roughness during the turning process of aluminum alloy 6063 when dry machining is used. In order to construct a model utilizing Box–Behnken Design and analyze the surface quality of the three machining variables, experiments were conducted. The factors employed in this study are input factors Spindle speed depth of cut and feed rate, in order to predict surface roughness. The experiment was designed by using Box–Behnken Design in which 17 samples were machined in a lathes machine. Each of the experimental results was measured using an SRT-6210S surface roughness tester. After achieving the data the Box–Behnken Design was used to predict the surface roughness. The ANOVA shows the significant factors and their interaction effects on the surface roughness and the model developed shows an accuracy of 95% which is realistically reliable for surface roughness prediction. With the obtained optimum input factors of 165 rev/min, depth of cut 1 mm, and feed rate 0. 5 mm/rev achieved predicted surface roughness of 9 μm. Therefore, the optimum input factors will greatly reduce the surface roughness and it will have improved manufacturing operations

Modelling of Nicotiana Tabacum L. oil biodiesel production : comparison of ANN and ANFIS

Among the modern computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are preferred because of their ability to deal with non-linear modelling and complex stochastic dataset. Nondeterministic models involve some computational complexities while solving real-life problems but would always produce better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling tobacco seed oil methyl ester (TSOME) production from underutilized tobacco seeds in the tropics. The dataset for the models was obtained from an earlier study which focused on design of the experiment on TSOME production. This study is an an exposition of the influence of transesterification parameters such as reaction duration (T), methanol/oil molar ratio (M:O), and catalyst dosage on the TSOME/biodiesel yield. A multi-layer ANN model with ten hidden layers was trained to simulate the methanolysis process. The ANFIS approach was further implemented to model TSOME production. A comparison of the formulated models was completed by statistical criteria such as coefficient of determination (R2), mean average error (MAE), and average absolute deviation (AAD). The R2 of 0.8979, MAE of 4.34468, and AAD of 6.0529 for the ANN model compared to those of the R2 of 0.9786, MAE of 1.5311, and AAD of 1.9124 for the ANFIS model. The ANFIS model appears to be more reliable than the ANN model in predicting TSOME production in the tropics.http://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin

A multi-objective optimisation approach for small-scale standing wave thermoacoustic coolers design

Thesis submitted in fulfilment of the requirements for the degree Doctor of Technology: Mechanical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2014Thermoacoustic heat engines provide a practical solution to the problem of heat management where heat can be pumped or spot cooling can be induced. This is new among emerging technology with a strong potential towards the development of sustainable and renewable energy systems by utilising solar energy or wasted heat. The most inhibiting characteristic of current thermoacoustic cooling devices is the lack of efficiency. Although simple to fabricate, the designing of thermoacoustic coolers involves significant technical challenges. The stack has been identified as the heart of the device where the heat transfer takes place. Improving its performance will make thermoacoustic technology more attractive. Existing efforts have not taken thermal losses to the surroundings into account in the derivation of the models. Although thermal losses can be neglected for large-scale applications, these losses need to be adequately covered for small-scale applications. This work explores the use of a multi-objective optimisation approach to model and to optimise the performance of a simple thermoacoustic engine. This study aims to optimise its geometrical parameters—namely the stack length, the stack height, the stack position, the number of channels and the plate spacing—involved in designing thermoacoustic engines. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. Acoustic work, viscous loss, conductive heat loss, convective heat loss and radiative heat loss have been used to measure the performance of the thermoacoustic engine. The optimisation task is formulated as a five-criterion mixed-integer nonlinear programming problem. Since we optimise multiple objectives simultaneously, each objective component has been given a weighting factor to provide appropriate user-defined emphasis. A practical example is provided to illustrate the approach. We have determined a design statement of a stack describing how the design would change if emphasis is placed on one objective in particular. We also considered optimisation of multiple objective components simultaneously and identified global optimal solutions describing the stack geometry using the augmented ε-constraint method. This approach has been implemented in GAMS (General Algebraic Modelling System). In addition, this work develops a novel mathematical programming model to optimise the performance of a simple thermoacoustic refrigerator. This study aims to optimise its geometrical parameters—namely the stack position, the stack length, the blockage ratio and the plate spacing—involved in designing thermoacoustic refrigerators. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. The cooling load, the coefficient of performance and the acoustic power loss have been used to measure the performance of the device. The optimisation task is formulated as a three-criterion nonlinear programming problem with discontinuous derivatives (DNLPs). Since we optimise multiple objectives simultaneously, each objective component has been given a weighting factor to provide appropriate user-defined emphasis. A practical example is provided to illustrate the approach. We have determined a design statement of a stack describing how the geometrical parameters described would change if emphasis is placed on one objective in particular. We also considered optimisation of multiple objective components simultaneously and identified global optimal solutions describing the stack geometry using a lexicographic multi-objective optimisation scheme. The unique feature of the present mathematical programming approach is to compute the stack geometrical parameters describing thermoacoustic refrigerators for maximum cooling or maximum coefficient of performance. The present study highlights the importance of thermal losses in the modelling of small-scale thermoacoustic engines using a multi-objective approach. The proposed modelling approach for thermoacoustic engines provides a fast estimate of the geometry and position of the stack for maximum performance of the device. The use of a lexicographic method introduced in this study improves the modelling and the computation of optimal solutions and avoids subjectivity in aggregation of weight to objective functions in the formulation of mathematical models. The unique characteristic of this research is the computing of all efficient non dominated Pareto optimal solutions allowing the decision maker to select the most efficient solution. The present research experimentally examines the influence of the stack geometry and position on the performance of thermoacoustic engines and thermoacoustic refrigerators. Thirty-six different cordierite honeycomb ceramic stacks are studied in this research. The influence of the geometry and the stack position has been investigated. The temperature difference across the stack and radiated sound pressure level at steady state are considered indicators of the performance of the devices. The general trends of the proposed mathematical programming approach results show satisfactory agreement with the experiment. One important aspect revealed by this study is that geometrical parameters are interdependent and can be treated as such when optimising the device to achieve its highest performance. The outcome of this research has direct application in the search for efficient stack configurations of small-scale thermoacoustic devices for electronics cooling

Production Quality and Operation Management as a Sustainable Tool for Advance Development of the Food and Beverages Manufacturing Industry in Nigeria

Manageability has gotten a significant measure of interest and has been assigned to the board. As a contextual investigation, this paper presents a survey of articles focusing on the meaning of creation and the activities of executives in the manageability of the assembling business, with a specific focus on fossil fuel by-products involving the food and drink industry. This study reviewed current articles that cut across production quality, operation management, and tools for the sustainable development of food and beverages in manufacturing industries. The papers are arranged and examined by the three fundamental areas of activities of the board (issue, execution methodology, and improvement). The concentrate additionally dissects the papers as indicated by whether they address the importance of the activity the board manages towards Nutrition, Health, and Wellness Company, which will be the case study. From this research, it was observed that the activity of the executives shows up as a pertinent variable in all center areas of supportable assembly research; however, the bearing and strength of its effect remain dubious. The research looks at how administrators can effectively manage the problems associated with fossil fuels through products and quality nutrition, health, and wellness of the company for both consumers and employees

Prediction Analysis of Surface Roughness of Aluminum Al6061 in End Milling CNC Machine Using Soft Computing Techniques

Computer numerically controlled (CNC) milling has been one of the most commonly used manufacturing processes for the performance of multiple operations, from tiny integrated circuits to heavy-duty mining machine gearboxes. It is a well-known machining process that offers close tolerances and repeated operations. However, the choice of machining parameters to achieve a desired part’s surface roughness (SR) remains a challenge. In the present study, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) approaches have been used to predict and monitor the surface roughness of aluminum Al6061 machined blocks. Furthermore, both models have been hybridized with genetic algorithm (GA) and particle swarm optimization (PSO) to investigate the potential enhancement in the prediction performance of the hybrid approach. The results show that factors such as the population size, the acceleration values, the choice of membership functions, and the number of neurons and layers significantly influence the prediction performance of the proposed models. Through a parametric analysis, this study demonstrates how the configuration of the models could affect the prediction performance. While exhibiting the impact of models’ hyperparameter combination on the prediction ability, this study provides insight into the development of suitable prediction models and the potential of soft computing techniques to predict the surface roughness of aluminum Al6061 blocks on CNC machines

Experimental and 3D-Deform Finite Element Analysis on Tool Wear during Turning of Al-Si-Mg Alloy

Aluminum alloys are becoming increasingly significant in the manufacturing industry due to their light weight and durable properties. Widely applied in aerospace and construction, precision machining is required to ensure the best possible surface quality. The surface quality of a machined component is directly affected by the tool wear incurred during machining. This research investigated the effect of process parameters and machining conditions on tool wear. The critical process parameters selected were cutting speed, feed rate, and depth of cut. Multi-walled carbon nanotube particles were dispersed in a base fluid of mineral oil to create a new lubricant applied during machining. Pure mineral oil was also used as a lubricant to reduce friction. Machining experiments were carried out with the two lubricants, and the tool wear incurred was measured and compared using a Dinolite microscope. All experiments were carried out with high-speed steel (HSS) cutting tools. Taguchi’s L9 orthogonal array was employed as a methodology to design the experiments. A finite-element 3D simulation was also carried out using DEFORM-3D to provide a scientific explanation of the turning process. Results showed a significant reduction in tool wear when machining with multi-walled carbon nanotubes (MWCNTs), with an average reduction of 14.8% compared to mineral oil. The depth of cut was also the most influential process parameter in terms of tool wear

Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network

Thermo-acoustic systems can convert thermal energy into acoustic waves and vice-versa. This conversion is due to the thermo-viscous interaction between the acoustically oscillating gas fluid within a porous medium, referred to as a regenerator, and the pore internal walls. The thermo-acoustic approach is proposed in this study as an alternative sustainable solution for addressing the issue of electricity in remote areas of developing countries. This approach is environmentally friendly as it utilises air as the working medium and therefore does not generate harmful emissions. In this study, a two-stage travelling-wave thermo-acoustic engine has been modelled using DeltaEC. The simulation was performed by considering various input heat for both of the engine stages. The heat input for the first stage was set within the range of 359.48 to 455.75W, while in the second stage was within the range of 1307.99 to 1656.35W. Hundred (100) data were generated. This dataset was used to build an Artificial Neural Network (ANN) model. The ANN model was validated using the data extracted from DeltaEC. A good agreement between DeltaEC simulation results and ANN predictions was observed. This study shows that the ANN approach is capable of analysing intricate nonlinear thermoacoustic issues

Study of the corrosion, electrical, and mechanical properties of aluminium metal composite reinforced with coconut rice and eggshell for wind turbine blade development

The gradual global shift from fossil fuels to renewable energy sources such as wind energy for power generation has stirred up a perpetual exigency for sustainable materials for manufacturing wind turbine blades. AMMCs are categories of materials that, over time, have proven reliable as they have been successful in meeting engineering needs in applications requiring high stiffness, moderate strength, and lightweight. The Al8112 alloy was used as the base metal in this study, reinforced with coconut rice and eggshell, to study the corrosion, electrical, and mechanical properties as a viable material for the development of the wind turbine blade. The stir-casting method was used in the preparation. Microstructures, Vickers hardness, tensile strength, electrical conductivity, and corrosion analysis (via the weight loss method) of the prepared composites were analysed. The 3 samples A, B, and C were analysed under 3 media for the corrosion study, such as rainwater, coolant (soluble oil plus water), and nano-lubricants. The results showed that introducing the reinforcements in the matrix of coconut rice and eggshell caused a rise in the hardness and tensile strength. SEM and EDX microstructural analysis revealed a uniform distribution of reinforcement particles in the matrix with the elemental reactions. The corrosion resistance of sample C of the composition (92% aluminium alloy, 5% coconut rice, and 3% eggshell) proved to be higher than that of sample B of the composition (95% aluminium alloy, 2.5% coconut rice, and 2.5% eggshell, and that of the aluminium alloy sample A, with sample C having a corrosion rate of 0.020 mg, sample B having a corrosion rate of 0.021 mg, and sample A having a corrosion rate of 0.022 mg. The composition of the AMMC that exhibits these properties is 92% aluminium, 5% coconut rice, and 3% eggshell. This newly developed material is recommended for applications involving the wind blade

Additional file 1 of Development of eco-friendly brake pads using industrial and agro-waste materials

Additional file 1