Mustafa Kemal Kulekci

Processes and apparatus developments in industrial waterjet applications

Abstract This paper is based on an abrasive waterjet cutting process that helps solve problems in processing of modern hard-to-cut materials, enabling wider industrial application. A detailed explanation of the recent developments in the main components of abrasive waterjet systems are given. Factors such as water pressure, grain diameters of abrasive feed rate, and traverse speed influencing surface roughness and depth of cut are studied using experimental data. Taking account of industrial applications, advantages–disadvantages, and limitations of the process are assessed.

Use of Grey-Taguchi Method for the Optimization of Oblique Turning Process of AZ91D Magnesium Alloy

This study investigated the multi-response optimization of turning process for an optimal parametric combination to yield minimum cutting forces and surface roughness with maximum material removal rate (MRR) using the combination of Grey relational analysis (GRA) and Taguchi method. Nine experimental runs based on an orthogonal array of Taguchi method were performed to derive objective functions to be optimized within experimental domain. The objective functions have been selected in relation to parameters of cutting process: cutting force, surface roughness and MRR. The Taguchi approach followed by Grey relational analysis to solve the multi-response optimization problem. The significance of factors on overall quality characteristics of the cutting process has also been evaluated quantitatively by the analysis of variance method (ANOVA). Optimal results have been verified through additional experiments. This shows proper selection of the cutting parameters produces, high material removal rate with better surface roughness and lower cutting force.Abstract This study investigated the multi-response optimization of turning process for an optimal parametric combination to yield minimum cutting forces and surface roughness with maximum material removal rate (MRR) using the combination of Grey relational analysis (GRA) and Taguchi method. Nine experimental runs based on an orthogonal array of Taguchi method were performed to derive objective functions to be optimized within experimental domain. The objective functions have been selected in relation to parameters of cutting process: cutting force, surface roughness and MRR. The Taguchi approach followed by Grey relational analysis to solve the multi-response optimization problem. The significance of factors on overall quality characteristics of the cutting process has also been evaluated quantitatively by the analysis of variance method (ANOVA). Optimal results have been verified through additional experiments. This shows proper selection of the cutting parameters produces, high material removal rate with better surface roughness and lower cutting force.

Abrasive wear behaviour of bio-active glass ceramics containing apatite

In this study, abrasive wear behaviour of bio-active glass ceramic materials produced with two different processes is studied. Hot pressing process and conventional casting and controlled crystallization process were used to produce bio-active ceramics. Fracture toughness of studied material was calculated by fracture toughness equations using experimental hardness results of the bio-active glass ceramic material. Two fracture toughness equations in the literature were used to identify the wear behaviour of studied ceramics. Wear resistance results that identified with both of the equations were similar. The results showed that the abrasive wear resistance of the bio-active glass ceramics produced with hot pressing process was found to be higher than that of the ceramics produced by conventional casting and controlled crystallization process.

Predictive Modelling of Ball Burnishing Process Using Regression Analysis and Neural Network

Abstract The present paper focuses on two techniques, namely regression and neural network techniques, for predicting surface roughness in ball burnishing process. Values of surface roughness predicted by the two techniques were compared with experimental values. Also, the effects of the main burnishing parameters on surface roughness have been determined. Surface roughness (Ra) was taken as response (output) variable and burnishing force, number of passes, feed rate, and burnishing speed were taken as input parameters. Relationship between the surface roughness and burnishing parameters was found out for direct measurement of the surface roughness. Results showed the application of the regression and neural network models to accurately predict the surface roughness.

Process Capability Analysis in Machining for Quality Improvement in Turning Operations

Abstract Process capability indices are effective tools for both, process capability analysis and quality assurance. In quality assurance programs, process capability indices reflect the performance of key quality characteristics for a control process. Quality assurance in mass production is enabled by using statistical process control techniques. In this study, various statistical process control techniques were carried out using the measured values taken from the workpieces that represent the whole process in the medium sized company. The chances for using statistical techniques for quality estimation processes have been discussed. For this purpose, normal probability plots and histograms were prepared and the process capability indices were calculated. As a result of this study, it turned out that the process capability for the whole process was inadequate and the mass production was unstable. Some actions must be taken by engineers to improve the quality level by shifting the process mean to target value and reducing the process variation.

Application of Genetic Algorithm (GA) for Optimum Design of Module, Shaft Diameter and Bearing for Bevel Gearbox

Abstract In this study selection of optimum module, shaft diameter and rolling bearing for conical gear has been done using genetic algorithm (GA). GA, is a novel stochastic method of optimization. GAs are based on the principles of natural selection and evolutionary theory. Objective function was optimized for the design variables between determined boundary values. The GA was constrained by taking into account the power, moment, velocity, wall thickness and bearing distances. Tooth strength and surface crush were considered to be design constraints for module optimization. The other algorithm constraints are maximum bending and torsion moments for shaft optimization, and working life for bearing optimization.

Nucleus geometry and mechanical properties of resistance spot welded coated-uncoated DP automotive steels

In this study, mechanical properties of resistance spot welding of DP450 and DP600, galvanized and ungalvanized automotive sheets have been investigated. The specimens have been joined by resistance spot welding at different weld currents and times. Welded specimens have been examined for their mechanical, macrostructure and microstructure properties. Depending on the weld current and time, effects of zinc coating on tensile properties, microhardness values as well as microstructure nugget geometry and nucleus size ratio have been investigated. X-ray diffraction analysis has been used to investigate the phase that formed at the joint interface. Result of the experiment show that nugget diameter, indentation depth and tensile load-bearing capacity are affected by weld parameters. Coating prevents full joining at low parameters. Microhardness increased in heat-affected zone and weld metal.

Advanced hybrid welding and manufacturing technologies

Abstract In this study, a detailed analysis of hybrid weld manufacturing technologies that can significantly contribute to the joining of materials has been carried out. Past, present and future projection, advantages, dis advantages, technological barriers and drawbacks of the processes are given. Detailed explanations of the recent developments of hybrid weld manufacturing technologies and main components are given. Potential industrial applications are assessed and evaluated using economic and technological results. The developments in hybrid welding manufacturing technologies generally improved metallurgical and mechanical properties of weld joints. Hybrid processes usually combine the benefits of each individual process. Due to low heat input, hybrid welds create fine grain structures, minimize base material dilution and achieve high toughness and mechanical properties. These processes are especially appropriate for high performance alloys and dissimilar metal joining. The results of this study conclude that reasonable costs and improved properties of the processed materials will lead to massive use of hybrid welding manufacturing technologies.

Evaluating Lifewide Learning Habits of Academicians for Sustainable Development.

Abstract In today’s higher education institutions in which sustainable development has been highly emphasized, individuals have changed the understanding of graduates of higher education; as such universities have emerged into a reconstruction period. In such a process, universities have been in need of academicians who are well development in both personal and professional domains. The concept of Lifewide learning, which is an important sustainable development tool, has underlined the fact that people should graduate as wholly-developed people to fulfill the needs of future societies, which releases the idea that academicians are to be role models for students. This study reflects on the research designed to develop and test an instrument that could identify the component of an academician’s Lifewide learning habits. Because of the complex nature of the Lifewide learning, considerable attempts were made in order to handle the process of classifying the cognitive, affective, social, technical and cultural domains related to academicians working in faculties of education. The developed instrument was trialled with 50 academicians, and the data was subjected to an explanatory factor analysis, allowing the identification of 6 sub-dimensions of Lifewide learning. These dimensions appeared to be capable of differentiating between problem-solving, professional habits, cultural interaction, leadership, care-based habits and leisure habits of academicians. The final version of the scale was applied to 211 academicians from faculties of education at 30 universities via “Google Drive”, and Lifewide habits of related people were assessed regarding their gender, title and department. Depending on the collected data, Lifewide learning habits of academicians were discussed, and some suggestions were proposed to support their professional and personal development.

Grey-based fuzzy algorithm for the optimization of the ball burnishing process

Abstract In the present study, Grey based fuzzy algorithm was used for the optimization of complex multiple performance characteristics of the ball burnishing process. Experiments have been planned according to Taguchis L16 orthogonal design matrix. Burnishing force, number of passes, feed rate and burnishing speed were selected as input parameters, whereas surface roughness and microhardness were selected as output responses. Using Grey relation analysis (GRA), Grey relational coefficient (GRC) and Grey relation grade (GRG) were obtained. Then, Grey-based fuzzy algorithm was applied to obtain Grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) was carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, a confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the feasibility of the Grey-based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.