EXPERIMENTAL MILLING TESTS OF CUTTING TOOLS AT STAINLESS STEEL
The article is focused on the issue of cutting tools for group of materials M, according to ISO 513, more specifically the area of stainless steel machining. The main objective is to do a practical comparison of selected milling cutters and their inserts at machining of the world's leading manufacturers as Pramet Tools, Mitsubishi, Sandvik and Seco. Experimental testing is focused on functional cutting tests at machining which aim is to test and check significant methods as technique of chip formation during cutting, trajectory of chips leaving, shape and size of chip, tool’s operation in cut (noisiness, vibration) or tool’s operation in cut at various axial depths of cut. Selected tests will be performed on the milling centre Kovosvit MAS MCV 1270 Power, and stainless steel X2CrNiMo1712-2 will be selected as machined material.
Testing of Greenleaf ceramic cutting tools with an interrupted cutting
Ovaj se rad baviti rjesenjem dostupnosti u prekinutoj obradi. Ispitivanja e - prekinut rez. Ovo 
o na Zavodu za strojnu obradu i montažu, VS 
udara. Testni materijal reznog alata i. Brzina rezanja (408 m/min i 580 m/min) i posmak rezanja (0,15 mm, 0,2 
m e e a dubina ( = 1 mm). ih 
reznih materijala u prekinutoj strojnoj obradi.Udanasnje vrijeme ove materijale uju neki proizvo a i kao rezne umetke za prekinutu strojnu obradu.
Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG Signals
Parkinson’s disease (PD) is a common neurodegenerative disease that causes involuntary muscle movements and tremor among other symptoms. One approach to diagnosing this disease is by analyzing the electroencephalography (EEG) signals of the patients. However, due to the complexity of this type of signal, more advanced feature extraction and classification methods are required. The goal of this study is to combine six well-known features in EEG analysis with eight higher order statistical features and use them for classification of early stage PD (1st and 2nd stage) from a healthy control group. After extracting the required features, eight classifiers are employed to classify the signals.