Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming

Machining of particle-reinforced metal matrix composites has been considerably difficult due to the extremely abrasive nature of the reinforcements that causes rapid tool wear and high machining cost. Abrasive water jet (AWJ) machining has proven to be a viable technique to machine such materials compared to conventional machining processes. The present study is focused on the surface roughness of AWJ cut surfaces and genetic expression programming (GEP) was proposed to predict surface roughness in AWJ machining of 7075 Al alloy composites reinforced with Al₂O₃ particles. In the development predictive models, characteristics of materials such as size and weight fraction of reinforcement particles, and depth of cut were considered as model variables. The training and testing data sets were obtained from the well-established machining test results. The weight fraction of particle, size of particle, and depth of cut were used as independent input variables, while arithmetic mean of surface roughness, maximum roughness of profile height, and mean spacing of profile irregularity as dependent output variables. Different models for the output variables were predicted on the basis of training data set using GEP and accuracy of the best model was proved with testing data set. The test results showed that output variables increased with increasing input variables. The predicted results were compared with experimental results and found to be in good agreement with the experimentally observed ones.     

The effects of long-term storage on the cold flow properties and viscosity of canola-based biodiesel

In this study, the effects of long-term storage on the viscosity and cold flow properties of biodiesel were investigated. Canola oil with a high content of unsaturated fatty acid was used to produce biodiesel in the experiments. Biodiesel sample was kept in ordinary atmospheric storage conditions for 6 months. The samples were taken from the biodiesel feedstock in every 30 days and cold flow properties and kinematic viscosity of the samples were measured. During 6-month storage, no significant deterioration was observed in cold flow properties and kinematic viscosity of biodiesel. Additionally, the same pour point (PP) and cold filter plugging point (CFPP) values (?11°C) were obtained during this period.     

Wear Behavior of Borided AISI D2 Steel under Linear Reciprocating Sliding Conditions

Protection of Metals and Physical Chemistry of Surfaces - AISI D2 steel is the most commonly used cold-work tool steel in its grade. In this study, micro-structural characterization and some...     

A new formulation for martensite start temperature of Fe–Mn–Si shape memory alloys using genetic programming

This study presents genetic programming (GP) soft computing technique as a new tool for the formulation of martensite start temperature (M_s) of Fe–Mn–Si shape memory alloys for various compositions and heat treatments. The objective of this study is to provide a different formulation to design composition at certain ranges and to verify the robustness of GP for the formulation of such characterization problems. The training and testing patterns of the proposed GP formulation is based on well established experimental results from the literature. The GP based formulation results are compared with experimental results and found to be quite reliable.     

Microabrasion Wear Behavior of Fast-Borided Steel Tooth Drill Bits

In this study, the surface of steel tooth drill bits (Ni-Cr-Mo based) was subjected to the solid-state boriding treatment with 10- to 50-nm nanoboron powder. Boriding processes were carried out at a constant temperature of 1273 K for 30, 45, 60, 75, 90, and 105 min using a solid-state box boriding technique. Borided drill bit samples were characterized by conventional methods (microstructure, microhardness, X-ray diffraction, and chemical analysis). The wear behavior of borided samples was tested at different loads and sliding speeds by a microabrasion experimental setup. Metallographic studies showed that the boride layers have a sawtooth morphology and consist of FeB and Fe₂B. The thickness and hardness of the boride layer were 35.29–202.56 μm and 1300–2333 HV_0.1, respectively, depending on the duration. The wear resistance of borided samples increased significantly due to the increase in surface hardness and lubricating effect, both of which were caused by the boriding process. A groove wear mechanism prevailed in borided samples, whereas that of bare steel tooth drill bits (STDBs) was grooving, rolling, and mixed.     

Abrasive Wear Model for Al2O3 Particle Reinforced MMCs Using Genetic Expression Programming

In this investigation, a new model was developed to predict the wear rate of Al2O3 particle-reinforced aluminum alloy composites by Genetic Expression Programming (GEP). The training and testing data sets were obtained from the well established abrasive wear test results. The volume fraction of particle, particle size of reinforcement, abrasive grain size and sliding distance were used as independent input variables, while wear rate (WR) as dependent output variable. Different models for wear rate were predicted on the basis of training data set using genetic programming and accuracy of the best model was proved with testing data set. The two-body abrasive wear tests of the specimens was performed using a pin-on-disc abrasion test apparatus where the sample slid against different SiC abrasives under the loads of 2N at the room conditions. The test results showed that GEP model has produced correlation coefficient (R) values about 0.988 for the training data and 0.987 for the test data. The predicted wear rate results were compared with experimental results and found to be in good agreement with the experimentally observed ones.     

The investigation of corrosion behavior of borided AISI 304 austenitic stainless steel with nanoboron powder

In this study, corrosion behavior and mechanical properties of AISI 304 austenitic stainless steel, which was borided with Nanoboron powder, was investigated. The commercially available steel was subjected to a boriding treatment with a size of 10–50 nm Nanoboron powders, at the temperatures of 1223 K to 1273 K with boriding durations of 2 to 4 h. Microstructure characterization of the steel was carried out with optical microscopy, scanning electron microscopy, microhardness and X-ray diffraction analyses. Corrosion tests were made by static immersion into a 10% H₂SO₄ acid solution and weight loss calculations as well as salt spray tests were carried out in accord with the ASTM B-117 standard. Boriding thermal treatment, increased the corrosion resistance of the steel against the acid solution, up to about 4.3 times while in the salt spray tests, weight loss corrosion resistance increased up to tier 2. However, anti-corrosion resistance decreased by 40%, its untreated value.     

Wet bonding: effect of drying time and distance

PURPOSE: To examine the effect of various drying times and air syringe-to-tooth distances on the shear bond strength of a dentin adhesive that requires a wet surface for maximum effectiveness. MATERIALS AND METHODS: Seventy extracted human molars were acid etched. The surface of the etched dentin was rinsed and dried with compressed air for 1, 3, or 5 seconds from a distance from either a distance of 1 or 10 cm. The adhesive One-Step was then applied, composite cylinders were attached and shear bond strengths were obtained. RESULTS: Drying time and distance had a significant impact on the resultant shear bond strengths. Longer drying times and shorter syringe-to-tooth distances negatively affected the bond strength of the adhesive studied.     

Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

In this study, martensite start (Ms) and austenite start (As) temperatures of Fe-based shape memory alloys (SMAs) were predicted by using a back-propagation artificial neural network (ANN) that uses gradient descent learning algorithm. An ANN model is built, trained and tested using the test data of 85 Fe-based SMAs available in literature. The input parameters of the ANN model are weight percentages of seven elements (Fe, Mn, Si, Ni, Cr, Cu and Al) and three different treatment conditions (hot rolling, homogenizing temperature and quenching). The ANN model was found to predict the Ms and As temperature well in the range of input parameters considered. A computer program was devised in MATLAB and different ANN models were constructed with this program for prediction of As and Ms temperatures of iron-based SMAs. A comprehensive analysis of the prediction errors of Ms and As temperatures made by the ANN is presented. This study demonstrate that ANN is very efficient for predicting the Ms and As temperatures of iron-based SMAs.     

Microstructural and Tribological Behavior of Pack-Borided Ni-Based Hastelloy C-276 Superalloy

Metallurgical and Materials Transactions A - Although the toughness and corrosion resistance of Ni-based superalloys are high due to their face-centered-cubic structure, their surface hardness and,...