Tepki yüzey modeli ve genetik algoritma kullanılarak AISI 316’ nın delinmesinde oluşan çapak yüksekliğinin modellenmesi ve optimizasyonu

Year 2010, Volume: 1 Issue: 1, 71 - 80, 01.06.2010

Erol Kılıçkap Mesut Hüseyinoğlu

Abstract

İmalat sektöründe tam otomatik cihazların kullanımının yaygınlaşması ile birlikte üretim sürecinin
modellenmesi büyük önem kazanmıştır. Bununla birlikte optimum üretim şartlarının belirlenmesi, üretimin
gelişimi ve ürün kalitesi için önemli bir rol oynamaktadır. Bu çalışmanın amacı, minimum çapak yüksekliğini
belirlemede optimum delme parametrelerini bulmak için Tepki yüzey modeli ve Genetik algoritma
kullanarak sistematik bir prosedür ortaya koymaktır. Optimum üretim için, üç-düzeyli üç faktörlü tam
deneysel tasarım, Tepki Yüzey Yöntemi ve Genetik Algoritma kullanılmaktadır. Delme işlemleri üç ilerleme
hızı (0.1, 0.2 ve 0.3 mm/dev), üç kesme hızı (4, 8 ve 12 m/dak) ve farklı uç açısına (90°, 118° ve 135°) sahip
HSS matkap takımları kullanılarak yapıldı. Deneyler Box Behnken tasarımı dikkate alınarak yapıldı. Tepki
yüzey metodolojisi kullanılarak çapak yüksekliği için bir matematiksel tahmin modeli geliştirilmiştir. Bu
matematiksel tahmin modelinden faydalanılarak minimum çapak yüksekliği için optimum delme
parametrelerini belirlemede Genetik algoritma kullanıldı. Genetik algoritma optimizasyon sonuçlarında
minimum çapak yüksekliğinin 4 m/dak kesme hızı, 0.1 mm/dev ilerleme hızı ve 135° uç açısında oluştuğu
görüldü.

Keywords

Delme, Çapak yüksekliği, Tepki yüzey modeli, Genetik algoritma.

Optimization and modelling of burr height by using response surface methodology and genetic algorithm in drilling AISI 316

Abstract

Drilling is one of the most commonly used industrial
machining processes for production of holes in
mechanical components. It is also an important
machining process employed as finish step in the
fabrication of machine parts. Typical problems
associated with drilling include rapid tool/drill
wear, hole delamination, burr formation, hole
geometry, dimensional accuracy and hole surface
quality. Two machining parameters are effective in a
drill operation, cutting speed and feed rate.
Therefore it is vital to evaluate these two parameters
in order to achieve the desired hole shape and
dimension. Burr formation affects workpiece
accuracy and quality in several ways; dimensional
distortion on part edge, challenges to assembly and
handling caused by burrs in sensitive locations on
the workpiece and damage done to the work
subsurface from the deformation associated with
burr formation. Burr formation is however
important as it requires additional manufacturing
process like deburring which attracts additional
production time and cost. The drilling process
produces burrs on both the entrance and the exit
surface of a workpiece. An entrance burr forms
where the drill undergoes plastic flow. The exit burr
is the extension of the material off the exit surface of
the workpiece. Since the exit burr is much larger
than the entrance burr, most of the burr related
problems reported to be associated with the exit
burr (Kim, J., Dornfeld, D.A., 2002; Dornfeld, D,
2004).
Response surface methodology (RSM) is a collection
of mathematical and statistical techniques, which
are useful for the modelling and analyzing the
engineering problems and developing, improving,
and optimizing processes. It also has important
applications in the design, development, and
formulation of new products, as well as in the
improvement of existing product designs, and it is an
effective tool for constructing optimization models.
Genetic Algorithm (GA), which imitates the
evolution mechanism of nature, is used for finding a
particular data in a dataset. GA produces everimproving
solutions based on the rule ‘the best one
survives’. For this purpose, it uses a fitness function
that selects the best, and operators like regeneration
and mutation to produce new solutions. Another
feature of GA is that it involves a group solution. By
the way optimum solutions among other ones could
be picked and disqualified ones are eliminated.
Due to widespread use of highly automated devices
in manufacturing, the manufacturing process
modelling has gained importance. However,
determination of optimal manufacturing conditions
has an important role for production development
and product quality.
The purpose of this study is to demonstrate a
systematic procedure by using RSM and GA to find a
combination of optimal drilling parameters to obtain
low burr height. The three-factor three-level full
experimental design, Response surface methodology
and Genetic algorithm are used for optimum
production. Drillings are performed by using three
feed rates (0.1, 0.2 and 0.3 mm/rev), three cutting
speeds (5, 10 and 15 m/min) and different point
angles (90°, 118° and 135°). The experiments were
conducted based on Box-Behnken design. A
mathematical prediction model was developed using
Response Surface Methodology (RSM) for the burr
height. Genetic algorithm is used for selection
optimum drilling parameters to obtain minimum
burr height by utilizing the mathematical prediction
model. The GA optimization results have reveal that
the minimum burr height was obtained at 4 m/min
cutting speed, 0.1 mm/rev feed rate and 135° point
angle.

Keywords

Drilling, Burr height, Response surface methodology, Genetic algorithm.