الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Thermal drilling (TD) or Thermal friction drilling (TFD) process is a nontraditional, hole shaping process that produces bushing without the formation of
chips. In TD process the heat developed due to the friction between the drilling tool
and the workpiece is used for drilling the holes and forms a bushing on the workpiece
due to the plastic deformation of the workpiece material. The bushing in TD process
is formed in situ from the workpiece. The TD is clean, no-chip and dry
manufacturing process, in which no coolants or cutting fluids are necessary.
There are several TD important process parameters that influencing the final
hole dimensional characteristics as well as the hole quality. These parameters,
include, the tool rotational speed (TRS), tool feed rate (TFR), the drilling tool
geometry and size, the hole diameter and sheet thickness and the type of the
workpiece material. Due to these large number of factors, the optimization of the
process parameters is very important to obtain the optimal hole dimensions
characteristics and quality. Moreover, the development of models to correlate these
parameters with the obtained hole dimensions and quality is very crucial. These
models can be also used to predict the hole dimensions and quality, which may
reduce the tedious experimental work and can be used in developing expert systems.
There are several modelling data-driven techniques such as regression analysis,
support vector machine (SVM), fuzzy logic (FL), and artificial neural networks
(ANN) have been taking more footprints in the industry as they can solve complex
problems.
In the present investigation, holes were manufactured in AA6082 aluminum
alloy sheets using TD process. The TD process was carried using computer
numerically controlled (CNC) machine. The effect of the TD process parameters,
namely, the TRS, TFR as well as the tool’s conical angle (TCA) on the generated
hole dimensional characteristics, typically, hole diameter (HD), bushing height (BH)
and bushing thickness (BT) were studied. The analysis of variance (ANOVA) was
used to evaluate the significance of the investigated TD process parameters. Optimization of the TD process parameters was carried out using grey relational
analysis (GRA) analysis technique. Models were developed using regression
analysis and ANN to predict the hole dimensional characteristics as a function of the
TD process parameters.
The results revealed that the interaction effect of TRS, TFR and TCA found
to have significant influence on the hole dimensional characteristics, like, HD, BH
and BT. The ANOVA calculations showed that the TFR is the most influential
parameter that affects the HD, followed by TRS and then TCA. While for the BH
and BT, the TRS is the most influential parameter that affects the BH and BT,
followed by TFR and then TCA. The TFD process parameters is the least parameter
that influence on the hole dimensional characteristics. The GRA optimization
showed that the optimum parameters achieved are TCA at level 3 (50
o
), TRS at level
1 (2000 rpm), and TFR at level 2 (200 mm/min) for achieving better HD, BT and
BH. The developed ANN models were successfully used to predict the hole
dimensional characteristics. While the regression models failed to the hole
dimensional characteristics with an acceptable accuracy.