Endüstri Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.12428/2646

Listeleniyor 1 - 20 / 25

Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Turkiye
(Public Library Science, 2025) Ayaz Atalan, Yasemin; Şahin, Hasan; Keskin, Abdulkadir; Atalan, Abdulkadir
Providing electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage rate must be estimated accurately to make reliable decisions in strategic planning and future investments in renewable energy. This study aims to accurately estimate the renewable energy production rate to meet T & uuml;rkiye's electricity needs from renewable energy sources. For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. In obtaining forecast data, 15 variables were considered under the oil resources, environmental parameters, and economic factors which are the main parameters affecting renewable energy usage rates. The RF algorithm performed best with the lowest mean absolute percentage error (MAPE, 0.084%), mean absolute error (MAE, 0.035), root mean square error (RMSE, 0.063), and mean squared error (MSE, 0.004) values in the test dataset. The R-2 value of this model is 0.996% and the MAPE value is calculated lower than 10%. The AB model, on the other hand, has the highest error values in the test data set, but still provides an acceptable prediction accuracy. The R-2 value was 0.792% and the MAPE value (0.371%) of this model was calculated to be in the range of 20% < MAPE <= 50%. This study, with its proposed forecasting models, makes significant contributions to energy policies to develop appropriate policies only for planning the amount of electricity usage needed in the future. In this context, this study emphasizes that renewable energy-based electricity generation transformation should be considered as an important strategic goal in terms of both environmental sustainability and energy security.
Numerical and Statistical Investigation of The Effect of Composite Layer Thickness on Low-Velocity Impact Behaviour in Fibre Metal Laminate Materials
(Gazi University, 2025) Dündar, Mustafa; Uygur, İlyas; Ekici, Ergün; Taşcıoğlu, Cihat; Gülenç, Behçet
In the field of aviation, reducing fuel costs by designing lighter vehicles and thus producing more environmentally friendly aircraft is one of the most important issues. This situation has led aircraft manufacturers to search for lighter and more durable materials. For this reason, Fibre Metal Laminate (FML) structures, which are used especially in the aerospace industry due to their superior fatigue and impact resistance properties, attract attention. Carbon fibre reinforced aluminium plates (CARALL), the most unique member of the FML hybrid structure family, has attracted the attention of researchers. In this study, the low velocity impact behaviour of CARALL FML structures with different composite layer thicknesses at different energy loading (8J-12J-18J) and different impactor types (Ø15 and Ø20) were statistically investigated. CARALL FML structures were modelled in 2/1 arrangement (Al-〖0°〗_([1])-Al, Al-〖0°〗_([3])-Al, Al-〖0°〗_([5])-Al) in LS-DYNA finite element programme. It is observed that the peak load Fmax increases with increasing energy loading. The increase in striker diameter decreased the amount of absorbed energy and increased the rebound.
Statistical Optimization and Analysis of Factors Maximizing Milk Productivity
(Mdpi, 2025) Kurtuluş, Yücel; Şahin, Hasan; Atalan, Abdulkadir
This study was conducted to determine the biological and environmental factors affecting milk yield and dry matter consumption and to analyze the effects of these factors on animal production. The study determined the variables affecting milk yield as input factors, such as lactation period, number of days of gestation, age, TMR dry matter ratio, and environmental factors. As a result of regression analyses, it was determined that each 1% increase in the TMR dry matter ratio decreased the milk yield by 0.9148 L, and each increase in the number of lactations increased the daily milk yield by 3.753 L. However, it was observed that the increase in the number of lactation days caused a decrease in milk production, and milk yield decreased as the gestation period extended. The most appropriate independent variable values were determined using statistical optimization analyses to maximize milk yield and optimize dry matter consumption. As a result of the analyses, the optimum value for the TMR dry matter ratio was calculated as 46.77%, 5 for lactation number, 6 for lactation day number, 230 days for gestation period, 55.8 months for cow age, and 20 degrees C for air temperature. The optimum values of the dependent variables were determined to be 61.145 L for daily milk yield and 19.033 units for dry matter consumption. The prediction intervals provided by the model served as reference points for future observations and showed that milk production was strongly affected by certain environmental and biological factors.
Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance
(Mdpi, 2025) Atalan, Yasemin Ayaz; Atalan, Abdulkadir
This study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R-2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R-2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.
Optimization of low-velocity impact behavior of FML structures at different environmental temperatures using taguchi method and grey relational analysis
(Sage Publications Ltd, 2025) Dündar, Mustafa; Uygur, İlyas; Ekici, Ergün
Carbon fiber-reinforced Aluminum Laminate (CARALL) is a new generation of Fibre Metal Laminate (FML) material. This study investigates the low-velocity impact behavior of CARALL structures at different environmental temperatures (-40 degrees C, 23 degrees C, and 80 degrees C). Two different groups of CARALL composite structures with varying fiber orientations were produced by hot pressing in a 3/2 arrangement: C1 (Al/0 degrees 90 degrees/Al/90 degrees 0 degrees/Al) and C2 (Al/0 degrees 0 degrees/Al/0 degrees 0 degrees/Al). Low-velocity impact tests were conducted at 23 J, 33 J, and 48 J energy levels using a & Oslash;20 mm spherical impactor tip. The area of damage was detected by ultrasonic C-Scan. In addition, analysis of variance (ANOVA) was applied to reveal the influential parameters and their effect levels. After conducting experiments using the Taguchi L18 test set, it was observed that the C2-coded specimen yielded better results in terms of maximum peak load, maximum displacement, and damage area. While the decrease in temperature increased the damage and maximum peak load, the increase in temperature did not cause a significant change in the maximum peak load. The primary damage mechanisms observed in damage investigations were matrix cracks and delamination between composite layers. Although delamination is present between the Al/CFRP layer, it is not significant. According to ANOVA results, impact energy was the most effective parameter for maximum impact force, maximum displacement, and damage area, with contribution rates of 81%, 74%, and 76%, respectively. The optimal experimental conditions (23 degrees C temperature and 23 J impact energy with the C1-coded sample) were determined using grey relational analysis based on principal component analysis.
Examining and Optimizing the Weld Area and Mechanical Performance of Thermoplastic Parts Manufactured by Additive Manufacturing and Welded by Friction Stir Welding
(Univ Belgrade, Fac Mechanical Engineering, 2024) Güden, Şehmus; Motorcu, Ali Rıza; Yazıcı, Murat
This study presents an experimental investigation into the weldability of ABS M30 (acrylonitrile butadiene styrene) plates produced by Additive Manufacturing (AM) using Friction Stir Welding (FSW). The effects of FSW process parameters on the yield stress and their optimal levels were determined using the Taguchi method. The optimal welding parameters were found to be a 16 mm tool shoulder diameter, 800 rpm tool rotation speed, and 10 mm/min traverse speed. The weld area of each sample welded using FSW was examined at a macroscopic level. The direction of tool rotation significantly affects the quality and strength of the FSW. When the FSW was performed with a clockwise rotation of the welding tool, a perfect weld could not be achieved. The tunnel effect resulted in gaps in the weld area of the samples at high rotation speeds. Differences were observed in the density between the weld area of the samples and the main parts.
Exploring ChatGPT's role in healthcare management: Opportunities, ethical considerations, and future directions
(Routledge Journals, Taylor & Francis Ltd, 2024) Atalan, Abdulkadir; Keskin, Abdulkadir; Özer, Süleyman
The ChatGPT initiative, an advanced AI application, has gained significant attention in diverse sectors, including business, technology, and research. Despite its novelty, it has been accepted across various domains, sparking discussions and shaping roadmaps. This article reviews different perspectives on ChatGPT, highlighting existing literature's prevailing lack of standardized evaluation methodologies. The study examines ChatGPT among natural language processing models, exploring its merits, drawbacks, limitations, and future expectations for its application in health quality and management. A meticulous analysis of 104 publications, covering titles, abstracts, and keywords, was independently conducted, addressing four main and seven sub-subjects. The review utilized PubMed, Scopus, Google Scholar, ScienceDirect, and preprint databases such as medRxiv, arXiv, and SSRN to select relevant articles. ChatGPT is expected to efficiently process and analyze extensive healthcare data, facilitating data-driven decision-making for healthcare organizations to enhance their services. However, it is crucial to acknowledge that ChatGPT models are incomplete substitutes for human healthcare professionals and have limitations in addressing complex medical issues. Consequently, this study underscores the imperative for full human supervision in medical services, emphasizing the essential role of humans in ensuring the quality and effective management of healthcare services.
Evaluating the optimum abrasive water jet machinability for CARALL composites with various fiber orientations
(Wiley, 2024) Altın Karataş, Meltem; Motorcu, Ali Rıza; Ekici, Ergün
Carbon Fiber Reinforced Aluminum Laminated (CARALL) composites are widely used in aircraft structures due to their ability to be produced in different shapes with desired properties and their high impact resistance properties. As with other layered composite materials, processing of CARALL composites by conventional manufacturing methods results in many damage mechanisms such as fiber breakage, deformation in the hole region, stress concentration, resin-fiber separation and microcracks. One of the modern manufacturing methods, Abrasive Water Jet (AWJ), is a processing method in which the material is removed by abrasion and almost any material can be cut without thermal degradation. There are no experimental studies in the literature on the drilling of CARALL composites by modern manufacturing methods. The aim of this study is to investigate the impact of machining parameters on the output variables (kerf taper angle (K), roundness error (Re) and material removal rate (MRR)) as well as the effect of fiber orientation on the drilling of CARALL composites with different fiber orientations on an AWJ machine. PROMETHEE-GAIA weighted by Entropy Weighting Method were used to ascertain the optimum levels of control factors. CARALL composites with different fiber orientations were drilled with an 8 mm diameter AWJ with three different water pressures, three different nozzle feed rates. With PROMETHEE-GAIA multi-criteria optimization method, the optimum levels of the factors that provide both minimum Re and K values and maximum MRR value were obtained with twill woven material, 1680 mm/min feed rate and 1680 bar water pressure. Highlights center dot CARALL composite materials with two different fiber orientations (twill weave and UD) were used. center dot CARALL composite materials were drilled at different machining parameters. center dot Abrasive water jet was used in drilling experiments. center dot Optimum drilling parameters were determined to achieve minimum roundness error, minimum kerf angle and maximum material removal rate. center dot PROMETHEE-GAIA was used as a multi-criteria decision-making method.
Development of a heavy vehicle torque rod applying continuous fiber reinforced thermoplastic composite materials instead of forged steel material: Design, analysis and optimization
(Sage Publications Ltd, 2024) Hayırkuş, Aslıhan; Motorcu, Ali Rıza; Yazıcı, Murat
The torque rod is an important component of the suspension system that connects the axle to the chassis in heavy commercial vehicles. The main motivation of this study is the development of a torque rod made of (1) plus cross-section, (2) continuous carbon and glass fiber reinforced hybrid thermoplastic composites, which can replace a forged steel torque rod used in heavy vehicles, has superior mechanical properties, provides minimum cost and weight. This study aims to develop a torque rod before its production within the framework of integration with Computer Aided Design (CAD), Finite Element Analysis (FEA), and Multi-Criteria Decision Making (MCDM). In this study, the design data and the properties of the composite materials were chosen as control factors. The minimum displacement, the best mechanical properties such as tensile stress, compressive stress, torsional shear stress, maximum critical buckling load, and the minimum part weight minimum production cost per piece were selected as quality characteristics. As a result of the FEA study, considering the experimental set of the Taguchi Method L25 orthogonal array, the data on mechanical properties, weight, and production cost per piece were subjected to the MCDM process using the Entropy Weighted TOPSIS method. As a result of the MCDM study, a torque rod made of continuous fiber-reinforced thermoplastic composite material, instead of a torque rod produced by forged steel, had the highest mechanical properties produced, the weight of the torque rod can be reduced by 64.76%, and the production costs per piece can be reduced by 37.5%. This study's findings have shown that the torque rod produced from continuous fiber-reinforced thermoplastic composite materials in a new geometry with a plus cross-section can be substituted for the torque rod produced by steel forging. Thus, it will contribute to reducing the fixed vehicle weight, especially in heavy commercial vehicles, reducing CO2 emissions, and increasing the range of the vehicles.
Delamination and thrust force analysis in GLARE: Influence of tool geometry and prediction with machine learning models
(Sage Publications Ltd, 2024) Ekici, Ergün; Pazarkaya, İbrahim; Uzun, Gültekin
The multi-layered (fiber/metal) structure of glass fibre aluminium reinforced epoxy (GLARE) makes it difficult to obtain acceptable damage-free holes that meet aerospace standards. This paper investigated the effects of tool geometry and drilling parameters on reducing delamination damage and uncut fibers at the hole exit surface in drilling GLARE. The hole surfaces were examined by scanning electron microscope (SEM) at various magnifications. In addition, deep neural network (DNN) and long-short-term memory (LSTM) machine learning models were used to predict delamination (Fda), uncut fiber (UCF), and thrust forces using experimental data. No positive contribution of the special geometry tool was observed, while the standard geometry tool was found to be ideal for drilling conditions. Analysis of variance (ANOVA) revealed that feed rate contributed 57.83% to delamination damage, while tool geometry contributed 74.31% and 92.33% for uncut fiber and thrust force, respectively. SEM analysis revealed high deformation zones in the aluminum layers and fiber fracture and separation in the glass fibre reinforced polymer (GFRP) layers. DNN and LSTM models were found to provide accurate predictions with R2 values greater than 95% and 98%, respectively.
Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms
(Mdpi, 2024) Atalan, Abdulkadir; Dönmez, Cem Çağrı
Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.
Evaluation of Machining Characteristics and Tool Wear During Drilling of Carbon/Aluminium Laminated
(Univ Belgrade, Fac Mechanical Engineering, 2024) Motorcu, Ali Rıza; Ekici, Ergün; Kesarwani, Shivi; Verma, Rajesh Kumar
In the past few decades, fibre metal laminate (FML) machining has been facing critical challenges in quality control and tool wear monitoring due to the material's intrinsic heterogeneity and abrasiveness. Different drill tools have been used to investigate the effect of process parameters on machining performances. Composite holes and tool wear was studied for drilling forces and surface roughness. An emphasis was made on examining the tool morphologies and wear processes that influence the drilling of CARALL composites. The drilling responses obtained from both the drill bits were optimized using a decision-making approach viz; Combined Compromise Solution Analysis (CoCoSo). The SEM investigation of the machined samples was used to examine the hole quality and surface finish. A lower point angle drill with a longer chip flute length produced the best results for drilling CARALL composites up to a specific point with minimum flank wear and chip adhesion.
Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy
(Mdpi, 2023) Atalan, Yasemin Ayaz; Atalan, Abdulkadir
The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system's forecast data are under control for the future.
PRICE ESTIMATION OF SELECTED GRAINS PRODUCTS BASED ON MACHINE LEARNING FOR AGRICULTURAL ECONOMIC DEVELOPMENT IN TÜRKİYE
(Pakistan Agricultural Scientists Forum, 2024) Keskin, Abdulkadir; Ersin, İrfan; Atalan, Abdulkadir
This study aims to estimate the price fluctuations of essential grain products, namely bread wheat (Triticum aestivum), durum wheat (Triticum durum), barley (Hordeum vulgare), and corn (Zea mays), in T & uuml;rkiye using machine learning (ML) algorithms. Using data from January 2, 2020, to January 10, 2023, the study employs algorithms such as random forest (RF), neural network (NN), support vector machine (SVM), and linear regression (LR). Independent variables include oil prices, currency exchange rates, and grain production volumes. The random forest (RF) algorithm provided the best results with the highest R2 values, while NN and LR showed relatively lower performance. The study highlights the significant impact of production and consumption volumes on grain prices and underscores the importance of ML algorithms in predicting these prices amidst changing conditions. Investments in agricultural technologies should be increased to improve data collection and analysis processes, as this is crucial for preventing price fluctuations in the agricultural sector.
Forecasting of the Dental Workforce with Machine Learning Models
(Bandırma Onyedi Eylül Üniversitesi, 2024) Atalan, Abdulkadir; Şahin, Hasan
The aim of this study is to determine the factors affecting the dental workforce in Turkey to estimate the dentists employed with machine learning models. The predicted results were obtained by applying machine learning methods; namely, generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM) were compared. The RF model, which has a high correlation value (R2=0.998) with the lowest error rate (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7), provided the best estimation result. The SVM model provided the worst estimate data based on the values of the performance measurement criteria. This study is the most comprehensive in terms of the dental workforce, which is among the healthcare resources. Finally, we present an example of future applications for machine learning models that will significantly impact dental healthcare management.
Optimization of Low-Calorific Coal Application at Different Loads in 600 MW Supercritical Thermal Power Plant with the PROMETHEE-GAIA Method
(Çanakkale Onsekiz Mart University, 2024) Emir, Aykut; Motorcu, Ali Rıza; Demirören, Hülya
This study examined the 600 MW supercritical unit of a 1200 MW imported coal-fired thermal power plant in Çanakkale, Türkiye. Coal blends consisting of low-calorific domestic coal (4087 kcal/kg) and high-calorific imported coal (5954 kcal/kg) were combusted at the single mill and burner level to analyze unit parameters at different loads. Initially, input parameters, levels affecting unit parameters, and output parameters influenced by different coal types were identified and prioritized. Using criteria weights determined by the entropy method, the optimal load and domestic-imported coal blend ratio were determined using the Preference Ranking Organization Method for Enrichment Evaluation-Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) multicriteria decision-making method. The optimization study concluded that a 450 MW load with a 14.6% domestic coal feed rate is the most suitable alternative.
Green Machining of Ceramics
(CRC Press, 2024) Ekici, Ergün; Bayraktar, Şenol
Manufacturing is an important part of the production industry and different machining operations are frequently used in this process. Depending on the material type and machining operation, cutting fluids are often used in the manufacturing process to achieve the desired geometric and dimensional limits in the interaction between the tool and the workpiece or to increase the cutting tool’s life. However, in recent years, the use of chemical fluids has attracted more attention and has emerged as an environmental problem that needs to be solved. In this study, researches on more environmentally friendly machining of ceramic materials with different machinability properties than traditional engineering materials are included. These researches consist of two main parts. First, alternative applications for reducing the use of cutting fluids in conventional machining methods, which are harmful to the environment/human health and cause an additional serious burden on machinability costs. The second one consists of the use of non-conventional machining methods where the use of cutting fluids is reduced or eliminated in ceramic structures that are difficult to be machined by conventional methods. Thus, the study purposes to provide an in-depth contribution to the current literature and to provide support for industrial users to solve problems that may be encountered in applications.
Fabrication and Machinability (Drilling) Properties of Fiber Metal Laminate (FML) Composites (CARALL and GLARE)
(CRC Press, 2023) Ekici, Ergün; Motorcu, Ali Rıza
The increasing need for lightweight and high-performance composite structures in the aerospace industry increases the demand for fiber metal laminate (FML) composite materials. This situation makes it necessary to improve existing FML materials’ mechanical properties and develop production and assembly conditions. In this section, the pre-production conditions of carbon fiber-reinforced aluminum laminates (CARALL) and glass laminate aluminum reinforced epoxy (GLARE) composites, which are currently under development, are evaluated. The effects of the metal group and fiber type on mechanical properties are presented. In addition to the traditional thermoset-based FML, the forming capabilities and mechanical properties of the new-generation thermoplastic-based fiber metal laminate (TFML) are also discussed. The effects of nanoparticles on the mechanical properties of FML were evaluated. FML production technologies are presented in detail. In FML composites, drilling is a common post-production joining method, as is the assembly of fiber-reinforced polymer (FRP) composite and metallic stacks. For this reason, the manufacturing processes required for assembling FML composites have been examined, and the drilling process has been comprehensively evaluated.
Effects of cutting parameters on tool wear in milling inconel 625 superalloys with a SiAlON ceramic and the prediction of tool life
(Elsevier Ltd, 2024) Güven, Sedat; Gökkaya, Hasan; Sur, Gökhan; Motorcu, Ali Rıza
Inconel 625 superalloys are widely preferred in various industries because of their superior mechanical properties at high temperatures. The superior properties they exhibit make the machinability of Inconel 625 alloys difficult. This situation can cause rapid wear of the cutting tools, making the cutting tool unusable in very short periods of time and causing deterioration of the workpiece surface quality. Therefore, improving the machining performance of Inconel 625 alloys, optimizing the machining parameters and using the optimum cutting tool material and geometry are important. In this study, SiAlON ceramic cutting tools, which have been widely used in recent years, were preferred. SiAlON tools supplied in raw rod form were subjected to various processes and finalized into final products. The effects of three different cutting speeds (vc), feed rates (f) and axial depth of cut (ap) on tool life (T), wear patterns and mechanisms were investigated with 9 tests designed according to the Taguchi L9 orthogonal array. The results revealed that the f values, the effects of which were analysed, had the most significant effect on T. By performing regression analysis with T values, the coefficients used in the extended Taylor T equation were determined, and T predictions were obtained. The regression model was found to be consistent with the experimental results, with an effect of 99.34 %, and the model was significant according to analysis of variance. The predominant wear patterns of the cutting tools were flaking at a low vc, nose collapse at a high vc and an adhered workpiece under all conditions. The wear mechanism was found to be predominantly adhesion and fracture. The presence of Ni, Cr, Mo, Nb and Fe on the cutting tool surface was determined, and the effect of the concentrations was observed to increase/decrease significantly depending on the parameter values examined.
Study on delamination factor and surface roughness in abrasive water jet drilling of carbon fiber-reinforced polymer composites with different fiber orientation angles
(Springer Science and Business Media Deutschland GmbH, 2021) Altın Karataş, Meltem; Motorcu, Ali Rıza; Gökkaya, Hasan
Carbon fiber-reinforced polymer (CFRP) composites are used in aerospace applications because of their superior mechanical properties and light weight. Avoiding damage in the machining of CFRP composites is difficult using traditional methods. Abrasive water jet (AWJ) has recently become one of the preferred machining methods for CFRP composites. This study evaluated the AWJ machinability of CFRP composites having three different fiber orientation angles (M1: [0°/90°]s, M2: [+ 45°/− 45°]s, and M3: [0°/45°/90°/− 45°]s) according to the delamination factor (Df), and the average surface roughness (Ra) as quality characteristics of the drilled holes. The aim of the study was to investigate the effects of different levels of AWJ drilling parameters on the delamination factor and surface roughness and to determine the optimum drilling parameter levels that provide minimum delamination formation and surface roughness values. For this purpose, AWJ drilling experiments were carried out using the Taguchi L16 (44) orthogonal array. Water pressure (WP), stand-off distance (L), traverse feed rate (F), and hole diameter (D) were chosen as process parameters. Analysis of variance was used to determine the percentage effects of the AWJ drilling process parameters. The microscopic surface roughness and delamination formation properties of the machined surfaces were revealed using a scanning electron microscope and an optical microscope, respectively. The most effective parameters on Df and Ra in the AWJ drilling of M1, M2 and M3 CFRP materials were determined to be water pressure, and stand-off distance. Minimum Df and Ra values were obtained when AWJ drilling the M3 CFRP composite with a fiber orientation angle of [0°/45°/90°/− 45°]s. Minimum delamination formation and very good surface quality can be obtained when the optimum process parameters determined in this study are used in the planning process for the AWJ drilling of CFRP composites having different fiber orientation angles.

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