Investigation and Statistical Analysis for Optimizing Surface Roughness, Cutting Forces, Temperature, and Productivity in Turning Grey Cast Iron

This paper investigated the influence of cutting parameters, including feed rate, cutting speed, tool nose radius, and wet or dry cutting conditions, on the resultant force, cutting edge/workpiece temperature, and surface roughness when turning grey cast iron. Results showed that increasing the feed rate increased the resultant force, cutting temperature, and surface roughness. At the same time, increasing the cutting speed and nose radius increased the cutting temperature, which in turn reduced the resultant force. For practical applications, basic mathematical calculations based on the sole effect of each parameter on the output of the experiments were used to estimate the extent of percentage increase in cutting temperature due to increasing feed rate, cutting speed, and nose radius. Similarly, the same approach was used to estimate the effect of increasing feed rate, cutting speed, and nose radius on average surface roughness. Results showed that increasing the feed rate increases the cutting temperature by 5 to 11% depending on the nose radius and cutting speed. On the other hand, increasing the cutting speed was found to have limited effect on cutting temperature with small nose radius whereas this effect increases with increasing the nose radius reaching about 11%. Increasing the nose radius also increases the cutting temperature, depending mainly on cutting speed, reaching a maximum of 21% at higher cutting speeds. Results also showed that increasing the feed rate increased the average surface roughness considerably to about 120% at high cutting speeds and a large nose radius. On the other hand, increasing the cutting speed and nose radius reduced the surface roughness (i.e., improved surface quality) by a maximum of 29 and 23%, respectively. In order to study the combined effects of the cutting parameters on the three responses, namely, the resultant cutting force, cutting temperature, and surface roughness, full factorial design and ANOVA were used, where it was found to be in good agreement with mathematical calculations. Additionally, the desirability function optimization tool was used to minimize the measured responses whilst maximizing the material removal rate

A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

This article reports an extended investigation into the precision hard turning of AISI 4340 alloy steel when machined by two different types of inserts: wiper nose and conventional round nose. It provides a closer look at previously published work and aims at determining the optimal process parameters for simultaneously minimizing surface roughness and maximizing productivity. In the mathematical models developed by the authors, surface roughness at different cutting speeds, depths of cut and feed rates is treated as the objective function. Three robust multi-objective techniques, (1) multi-objective genetic algorithm (MOGA), (2) multi-objective Pareto search algorithm (MOPSA) and (3) multi-objective emperor penguin colony algorithm (MOEPCA), were used to determine the optimal turning parameters when either the wiper or the conventional insert is used, and the results were experimentally validated. To investigate the practicality of the optimization algorithms, two turning scenarios were used. These were the machining of the combustion chamber of a gun barrel, first with an average roughness (Ra) of 0.4 µm and then with 0.8 µm, under conditions of high productivity. In terms of the simultaneous achievement of both high surface quality and productivity in precision hard turning of AISI 4340 alloy steel, this work illustrates that MOPSA provides the best optimal solution for the wiper insert case, and MOEPCA results are the best for the conventional insert. Furthermore, the results extracted from Pareto front plots show that the wiper insert is capable of successfully meeting both the requirements of Ra values of 0.4 µm and 0.8 µm and high productivity. However, the conventional insert could not meet the 0.4 µm Ra requirement; the recorded global minimum was Ra = 0.454 µm, which reveals the superiority of the wiper compared to the conventional insert

Effects of pressure sensitivity on notch-tip fields in plastics.

The effects of pressure-sensitive yielding on the $\eta$ factor and the J integral estimation for compact tension specimens are investigated. The analytical expressions for $\eta$ and J for von Mises materials are generalized to pressure-sensitive materials under plane stress and plane strain conditions. Numerical results indicate that $\eta$ decreases as the pressure sensitivity increases. The effects are more pronounced under plane strain conditions than under plane stress conditions. For rigid perfectly-plastic materials, J for pressure-sensitive materials is reduced to a simple expression of the tensile yield stress times the crack tip opening displacement as for the von Mises materials. Also, plane-strain stress and slip-line fields near the sharp and round tips of wedge-shaped notches in perfectly plastic pressure-sensitive materials are investigated to illustrate the effects of pressure sensitivity and notch geometry on notch-tip fields. Results show that for sharp notches, as the pressure sensitivity or the wedge angle increases, the opening stress and hydrostatic tension ahead of the tip decrease. For round notches, as the pressure sensitivity increases, the extent of the exponential spiral zone ahead of the tip increases. As the wedge angle increases, the extent of the exponential spiral zone ahead of the tip decreases and the maximum opening stress and hydrostatic tension ahead of the tip decrease. The notch-tip fields in pressure-sensitive non-porous and porous solids are investigated by finite element analysis. The yielding behavior of porous solids which are used to represent rubber modified epoxies, is based on a generalized Gurson yield criterion that accounts for both the matrix pressure sensitivity and porosity effects. Due to the geometry and loading of the specimen modeled, some regions are under negative mean stresses, for which the yield criterion is modified. The computational results are compared with experimental cavitation and intense shear zones near the notch tip in specimens. The lowering of the mean stress ahead of the tip in epoxies with higher rubber contents changes the fracture mode from being controlled by mean stress at the elastic-plastic boundary to being controlled by plastic strain closer to the notch tip.Ph.D.Applied SciencesMechanical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/130645/2/9811019.pd

Electrospun Polymer Nanofibers: Processing, Properties, and Applications

Electrospun polymer nanofibers (EPNF) constitute one of the most important nanomaterials with diverse applications. An overall review of EPNF is presented here, starting with an introduction to the most attractive features of these materials, which include the high aspect ratio and area to volume ratio as well as excellent processability through various production techniques. A review of these techniques is featured with a focus on electrospinning, which is the most widely used, with a detailed description and different types of the process. Polymers used in electrospinning are also reviewed with the solvent effect highlighted, followed by a discussion of the parameters of the electrospinning process. The mechanical properties of EPNF are discussed in detail with a focus on tests and techniques used for determining them, followed by a section for other properties including electrical, chemical, and optical properties. The final section is dedicated to the most important applications for EPNF, which constitute the driver for the relentless pursuit of their continuous development and improvement. These applications include biomedical application such as tissue engineering, wound healing and dressing, and drug delivery systems. In addition, sensors and biosensors applications, air filtration, defense applications, and energy devices are reviewed. A brief conclusion is presented at the end with the most important findings and directions for future research

Insulation Performance of Building Components and Effect on the Cooling Load of Homes in Saudi Arabia

A common practice in the construction of residential and commercial buildings in Saudi Arabia is to insulate the outer walls and windows only. Other building components such as the roof, columns and slabs, and doors are usually neglected. Moreover, vital components such as the roof and windows are especially neglected in commercially built residential and commercial buildings. The aim of this study is to put this common impression and practice to the test by quantifying the contribution of every building component to the overall air-conditioning load of the building. The hypothesis evaluated in this paper is that despite the common practices, there could be an optimum selection of insulators for the building components that yields the lowest energy consumption and maximum savings not only in energy costs but also installation costs. The required air-conditioning load is determined using manual calculations and the HAP software package for 1022 possible configurations. The findings of the analysis point to the importance of the roof, as it is the major contributor to the thermal load, followed closely by columns and slabs, with 44.2% of the overall cooling load. It is found that a single wall consisting of 2 cm of cement plaster, 20 cm of cement–polyurethane brick, and 2 cm of cement plaster is less expensive and has higher thermal resistance than any of the more expensive double walls. The study found one scenario of possible configurations with the optimized selection of building materials and their insulation materials that provides the most effective insulation at the lowest cost

Multi-Objective Optimization of Performance Indicators in Turning of AISI 1045 under Dry Cutting Conditions

In machining operations, minimizing the usage of resources such as energy, tools, costs, and production time, while maximizing process outputs such as surface quality and productivity, has a significant impact on the environment, process sustainability, and profit. In this context, this paper reports on the utilization of advanced multi-objective algorithms for the optimization of turning-process parameters, mainly cutting speed, feed rate, and depth of cut, in the dry machining of AISI 1045 steel for high-efficient process. Firstly, a number of experimental tests were conducted in which cutting forces and cutting temperatures are measured. Then the material removal rate and the obtainable surface roughness were determined for the examined range of cutting parameters. Next, regression models were developed to formulate the relationships between the process parameters and the four process responses. After that, four different multi-objective optimization algorithms, (1) Gray Wolf Optimizer (GWO) and (2) Weighted Value Gray Wolf Optimizer (WVGWO), (3) Multi-Objective Genetic Algorithm (MOGA), and (4) Multi-Objective Pareto Search Algorithm (MOPSA), were applied. The results reveal that the optimal running conditions of the turning process of AISI 1045 steel obtained by WVGWO are a feed rate of 0.050 mm/rev, cutting speed of 156.5 m/min, and depth of cut of 0.57 mm. These conditions produce a high level of material removal rate of 4460.25 mm3/min, in addition to satisfying the surface quality with a roughness average of 0.719 µm. The optimal running conditions were found to be dependent on the objective outcomes’ order. Moreover, a comparative evaluation of the obtainable dimensional accuracy in both dry and wet turning operations was carried out, revealing a minimal relative error of 0.053% maximum between the two turning conditions. The results of this research work assist in obtaining precise, optimal, and cost-effective machining solutions, which can deliver a high-throughput, controllable, and robust manufacturing process when turning AISI 1045 steel

Precision Face Milling of Maraging Steel 350: An Experimental Investigation and Optimization Using Different Machine Learning Techniques

Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges

A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

This article reports an extended investigation into the precision hard turning of AISI 4340 alloy steel when machined by two different types of inserts: wiper nose and conventional round nose. It provides a closer look at previously published work and aims at determining the optimal process parameters for simultaneously minimizing surface roughness and maximizing productivity. In the mathematical models developed by the authors, surface roughness at different cutting speeds, depths of cut and feed rates is treated as the objective function. Three robust multi-objective techniques, (1) multi-objective genetic algorithm (MOGA), (2) multi-objective Pareto search algorithm (MOPSA) and (3) multi-objective emperor penguin colony algorithm (MOEPCA), were used to determine the optimal turning parameters when either the wiper or the conventional insert is used, and the results were experimentally validated. To investigate the practicality of the optimization algorithms, two turning scenarios were used. These were the machining of the combustion chamber of a gun barrel, first with an average roughness (Ra) of 0.4 µm and then with 0.8 µm, under conditions of high productivity. In terms of the simultaneous achievement of both high surface quality and productivity in precision hard turning of AISI 4340 alloy steel, this work illustrates that MOPSA provides the best optimal solution for the wiper insert case, and MOEPCA results are the best for the conventional insert. Furthermore, the results extracted from Pareto front plots show that the wiper insert is capable of successfully meeting both the requirements of Ra values of 0.4 µm and 0.8 µm and high productivity. However, the conventional insert could not meet the 0.4 µm Ra requirement; the recorded global minimum was Ra = 0.454 µm, which reveals the superiority of the wiper compared to the conventional insert