Abstract:
In manufacturing industries, hybrid manufacturing (HM) emerged as a paradigm shift since it
provides numerous benefits which include lead time reduction, cost savings and waste
minimization. In this thesis, the concept of HM as an integration of Fused Deposition Modelling
(FDM)-an Additive manufacturing (AM) process, and milling being a traditional process was
utilized. To maximize the benefits of HM, it is essential to predict the behaviour of process
parameters effectively. This thesis proposes the framework of predictive analysis in HM. Firstly
the experiments were conducted by considering AM parameters: layer thickness, print speed, and
extrusion temperature. Three levels of each parameter were considered. Surface roughness was
measured. Then milling was carried out with two levels of feed rate and again surface roughness
was measured to observe the effects of milling. After this, compressive testing was performed.
Then experimental data was fed to Microsoft Azure Machine Learning Studio to get numerical
results. In this software, neural network regression was performed to explore the behaviour of
process parameters. Hyperparameter tuning was also done to increase the efficiency of the
model. The actual, predicted, and hyper scored outcomes were compared. It was observed that
experimental and numerical results agreed. It was concluded that for HM, to improve the surface
finish, layer speed and extrusion temperature need to be kept low while print speed needs to be
increased. For compressive strength, layer thickness should be kept maximum while print speed
and extrusion temperature should be kept low.
