Alüminyum 5083-H111 malzemenin delik delme işleminde kesme parametrelerinin yapay sinir ağları ile optimizasyonu
Optimization of cutting parameters in the drilling process of aluminum 5083-H111 material with ann
Yazar: EMRE TEKE
Danışman: DOÇ. DR. NESLİHAN ÖZSOY
Sakarya Üniversitesi / Fen Bilimleri Enstitüsü / Makine Mühendisliği Ana Bilim Dalı / Makine Tasarım ve İmalat Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Makine Mühendisliği = Mechanical Engineering

Özet:
Alüminyum alaşımlar gerek hafif olmaları gerekse dayanıklılıkları ve fiziksel 
özellikleri dolayısıyla birçok yeni kompozit ve alaşım malzemenin yanında sıkça 
tercih edilir duruma gelmiştir. Bu alaşımlar daha çok otomotiv, uçak sanayii, 
denizcilik, inşaat gibi sektörlerde rahatlıklar kullanılabilmektedir. Alüminyum 
alaşımlarının bu sektörlerde kullanılır hale gelmesiyle yine bu sektörlerin imalat 
aşamasında karşılaşılan bağlantı elemanlarının kullanımı noktasında delik delme 
ihtiyacı oldukça artmıştır. Nitekim delik delme işlemi talaşlı imalat sektöründe %33-
40'lık bir bölümü kapsamaktadır. Bu durumda hem çok sayıda hem de seri bir şekilde 
delik delebilmek önem arz etmektedir. Bu esnada malzemenin yıpranma miktarı ve 
kesici takımların yorulma ömrü, kullanım ömrü gibi parametreler daha da önemli 
olarak karşımıza çıkmaktadır. Bu sebeple bu çalışmada alüminyum alaşıma delik 
delinmesi işleminde optimum parametrelerin belirlenmesi ve daha sağlıklı ve uzun 
ömürlü delik delme prosesi geliştirmek amaçlanmıştır. 
Ayrıca bu çalışmada delik delme deneyleri haricinde yapay zekadan da 
faydalanılacaktır. YSA (Yapay Sinir Ağları) adı verilen yapay zeka destekli tahmin 
programı kullanılarak, yürütülen deneylerin güvenilirliği sorgulanacak, aynı zamanda 
tahmin sonuçlarıyla deney sonuçları da karşılaştırılarak yapay zekanın bu noktada ne 
kadar verimli çalıştığı görülebilecektir. Bu sayede bundan sonraki çalışmalarda yapay 
zeka yardımıyla belirli parametreler ile analizler yapılabilecek, belirli bir hata yüzdesi 
aralığında ne kadar kesme kuvveti ile karşı karşıya kalınabileceği kestirilebilecektir. 
Dolayısıyla hem malzeme, hem zaman hem de imalat maliyeti açısından avantajlı 
olunacaktır. 
Bu çalışmada AA5083 H111 alaşımı ile çalışılmış ve deneylerde 3 parametre 
belirlenmiş, her bir parametre 3 ayrı seviyeye bölünmüştür. Bu kombinasyonlarla 
deneyler yürütülmüş olup toplamda 27 adet delik açılmıştır. Deneylerde 3 farklı kesme 
hızı (80,100,120 m/dk), 3 farklı diş başına ilerleme miktarı (0.06, 0.09, 0.12 mm/diş), 
3 farklı soğutma tipi (kuru, hava soğutmalı ve sıvı soğutmalı) olmak üzere 3 parametre 
ile çalışılmıştır. TiAlN kaplamalı HSSE-Co5 matkap kullanılmıştır. Deneyler Taksan 
TMC-700 V marka ve model CNC dik işleme merkezi, ESİT AX3 yük hücresi, NI 
cDAQ-9188 veri toplama ünitesi ve FlexLogger yazılımı kullanılarak yapılmıştır. Delinen 
her deliğin Z ekseninde kesici takımı maruz bıraktığı direnç kuvveti dikkate alınmıştır. 
Yapay zeka tahminleri ise MATLAB paket programının NNTools modülü aracılığıyla 
yürütülmüştür. Nitekim yapay zeka tahminleri, yapılan 3 farklı deneyde ortalama 
%3.63 hata oranıyla gerçek deneylere yaklaşmıştır.Yapılan deneyler ve tahminler sonucu görülmüştür ki, çalışılan kesici takım ve 
malzeme şartlarında optimum parametreler 100 m/dk kesme hızı, 0,06 mm/diş, diş başı 
ilerleme ve sıvı soğutmalı olarak karşımıza çıkmaktadır.

Abstract:
Aluminum alloys have become frequently preferred alongside many new composite 
and alloy materials due to their lightness, durability and physical properties. These 
alloys can be used in the automotive, aircraft industry, maritime and construction 
sectors. With the use of aluminum alloys in these sectors, the need for drilling has 
increased considerably in terms of the use of fasteners encountered in the manufacturing phase of these sectors.
Aluminum 5083 H111 alloy is mainly preferred in the maritime and aerospace 
industries because it is light and highly corrosion-resistant. In the manufacturing 
processes in these sectors, it is inevitable to apply thousands of hole drilling processes. 
In addition, in these areas where care is taken to produce with high precision and 
quality, the quality of fasteners and holes becomes more important than anything else. 
Keeping the quality and efficiency in these processes at the maximum level can be 
achieved by optimizing the parameters in the drilling process.
A safe working environment and sustainability are of great importance in engineering 
studies. For the product or system to be manufactured to be reasonable in terms of 
length, quality and cost, all forces acting on cutting tools and workbenches should be 
examined in detail and measured well and sensitively. When the results obtained as a 
result of theoretical calculations and the data obtained as a result of the application are 
compared, it is seen that most of the time, they do not overlap. For this reason, 
experimental analysis and measurement are of vital importance.
Among the machining methods, drilling processes include 33-40% of the methods. 
Hole quality in drilling processes varies depending on parameters such as circularity, 
axial misalignment, surface roughness, hole size and burr formation. The working 
conditions and the quality of the assembly processes depend on the hole quality. The 
higher the quality of the hole axis, the smoother the precision assembly process can 
be. Misalignment affects the stress distribution and causes the material to be subjected 
to excessive tensile forces. As a result of the roughness in the holes, abrasive 
deterioration occurs in the material. In designs with more than one sample, mandatory 
tolerances occur for the surface of the hole, its axes and the positions of these axes. In 
particular, tolerances such as hole axis, perpendicularity of the axis to the surface, 
eccentricity, roughness of the hole axis, and circularity are critical in sensitive 
industries such as aerospace.
In this study, AA5083 H111 alloy was studied and 3 parameters were determined in 
the experiments. Each parameter was divided into 3 different levels. Experiments were 
carried out with these combinations and 27 holes were drilled. In the experiments, 3 
different cutting speeds (80,100,120 m/min), 3 different feed rates per tooth (0.06, 0.09, 0.12 mm/tooth), 3 different cooling types (dry, air cooled and liquid cooled) were 
studied with 3 parameters. TiAlN coated HSSE-Co5 drill was used. Experiments were 
made using Taksan TMC-700 V brand and model CNC vertical machining center, 
ESIT AX3 load cell, NI cDAQ-9188 data acquisition unit and FlexLogger software. 
The resistance force exerted by the cutting tool in the Z axis of each drilled hole was 
considered. Artificial intelligence predictions were carried out through the NNTools 
module of the MATLAB package program.
The estimation data obtained from the ANN were included in the text through the 
program's graphical outputs, the parameters' effects on the process were examined, and 
the estimations and the actual test results were interpreted with the help of graphics.
In this study, HSS (high speed tool steel) was studied with a drill. The reason for this 
is that it is more economical than carbide drills. The working set consists of TiAlN 
coated HSSE-Co5 drills. All experiments were done with a single drill.
Force-Displacement graph was drawn for the experiment performed in each cooling 
type. Exponential smoothing method was used since it was seen that there were signal 
noises in the data.
In addition, this study will also use artificial intelligence, apart from hole drilling 
experiments. The reliability of the experiments will be questioned by using the 
artificial intelligence-supported prediction program called ANN (Artificial Neural 
Networks). At the same time, it will be possible to see how efficiently the artificial 
intelligence works by comparing the prediction results with the experimental results. 
In this way, in future studies, analyzes can be made with specific parameters with the 
help of artificial intelligence, and it will be possible to predict how much cutting force 
can be encountered in a specific error percentage range. Therefore, it will be 
advantageous regarding material, time and manufacturing costs. The artificial neural 
networks (ANN) method is an approximate method that contains input and output data 
sets and defines the complex relationships between these sets. The ANN method is 
frequently used in the industry to create a model with the experimental parameters and 
results and to model the estimation structure to eliminate the necessity of repeating 
high-cost experiments with the data obtained from the experiments. Identify complex 
process properties of problems using empirical equations.
ANN has a structure consisting of an input layer, an output layer and one or more 
hidden layers. The input and output layers are sets of neurons that define these layers. 
Although there is no specific amount for hidden layers; generally, 1 or 2 are used. The 
output layer receives all the responses from the hidden layer and creates an output 
vector. While ANN models with a single output layer give better results, the efficiency 
of the results is affected since models with multiple output layers are more complex.
The analysis will be performed with the help of the MATLAB software Neural Net 
Fitting application. With the help of artificial intelligence, the cutting forces will be 
estimated and compared with the experimental results.
In the study using MLP (Multi-Layer perception) method, 3 parameters were entered 
as input and cutting force was requested as output. While the results of the experiments 
performed during drilling with all three cooling types were analyzed, it was understood 
that machine learning and analysis were more efficient by using the LevenbergMarquardt backpropagation method, which is one of the methods available in the application, So it was studied with this method. 15% of the data is reserved for testing, 
15% for validation, and 70% for use in the training function. Working with 3 inputs, 1 
output and 3 hidden layers, 6 neurons are assigned to the first hidden layer, 8 neurons 
to the second hidden layer, and 10 neurons to the third hidden layer.
Learning was tried 3 times with layers with 2-4-6, 4-6-8, 8-6-10, 6-8-10, 3-5-7, 8-10, 
6-8, 6-10, 1-3-7 neuron arrays, respectively. However, no error rate below 10% could 
be obtained, and the least error rate and machine learning were obtained from the 6-8-
10 arrayed alternative. These sequence selections are based on guesswork and trial and
error based on previous studies. For each of these layers, logsig and tansig functions 
were tried with different combinations, and it was seen that the tansig function was 
more efficient for all layers.
Artificial intelligence predictions approached actual experiments with an average error 
rate of 3.63% in 3 different experiments. As a result of the experiments and 
estimations, it has been seen that the optimum parameters in the cutting tool and 
material conditions are 100 m/min cutting speed, 0.06 mm/tooth, feed per tooth and 
liquid cooling.