Wasit Journal of Engineering Sciences

The aim of the work is to study the effect of cutting condition on cutting bit of the
turning machine while working on different metal ,Through using a special type of
commonly used cutting tool bit in (HSS) due to the high qualifications it is
characterized by cutting ,and its endurance of high temperature .Two types of metal are
used in cutting (Aluminum, Mild steel),relying on the working conditions used in the
machine(feed, cutting speed، depth of cut),while conducting working processes via
using cooling liquid ,and without it .The wear test results shown that there are two types
of wear measured by the (tool micker microscope) :Flank wear and Greater wear, are
formed on the cutting edge of the tool bits as a result of the great effect of cutting
conditions on the tool bit and the high temperature of the chips ;in addition to the
occurrence of resulting edge on the cutting edge of the tool bits in the process of cutting
aluminum , with the use of cooling liquids which prolong the of cutting tool and
decrease the periods of re-grinding the cutting tool bit.

The effects of heat treatment on the properties of rolled aluminum zinc alloy type 7072-T6 are study in this work. Representative samples of aluminum zinc alloys were subjected to heat treatment processes which are; Solution heat treatment follow by Artificial Ageing in the different order. The aluminum zinc alloys were heated to the initial temperature of 600 ºC and water quenched. The quenched aluminum zinc alloys were subjected to(lamellae formation) by reheating it to the dual-phase region at a temperature of 150ºC and then rapidly quenched in water. The (lamellae formation) samples was take an Artificial Ageing at 400 ºC to provide an alloy containing strong, tough and lath α –β phase in a soft and ductile α - matrix. Mechanical tests were carried out on the samples and the results shows that the aluminum zinc alloys developed has excellent combination of tensile strength, hardness and impact strength which is very good for structural applications.

This paper examines the results of performance of a single cylinder spark- ignition engine fuelled with 20% methanol +80% gasoline (M20), compared to gasoline. The experiments were conducted at stoichiometric air–fuel ratio at wide open throttle and variable speed conditions, over the range of 1000 to 2600 rpm. The tests were conducted at higher useful compression ratio using optimum spark timings and adding recirculated exhaust gas with 20% to suction manifold.

The test results show that the higher compression ratio for the tested gasoline was 7:1, 9.5:1 for M20 and 9:1 for M20 with added EGR. M20 at higher useful compression ratio (HUCR) and optimum spark timing (OST) characteristics are significantly different from gasoline. Within the tested speed range, M20 consistently produces higher brake thermal efficiency by about 6%. Also it resulted in approximately 3.06% lower brake specific fuel consumption compared with gasoline. Adding EGR to M20 caused reduction in HUCR and advancing the OST. This addition increased brake specific fuel consumption (BSFC), reduced brake thermal energy, volumetric efficiency and exhaust gas temperatures.

Speaker recognition (identification/verification) is the computing task of validating a user’s claimed identity using speaker specific information included in speech waves: that is, it enables access control of various services by voice. Discrete Wavelet Transform (DWT) based systems for speaker recognition have shown robust results for several years and are widely used in speaker recognition applications. This paper is based on text independent speaker recognition system that makes use of Discrete Wavelet Transform (DWT) as a feature extraction and kernel Support Vector Machine (SVM) approach as a classification tool for taking the decision through applying simplified-Class Support Vector Machine approach. The proposed SVM approach can convert local Euclidean distances between frame vectors to angles by projecting these -dimensional vectors together, and get the minimum global distance from the non-linear aligned speech path in order to address audio classification, and hence, sound recognition.The DWT for each frame of the spoken word are taken as a tool for extracting the main feature as a data code vectors, next these data is normalized utilizing the normalized power algorithm that is used to reduce the number of feature vector coefficients then these data is scaled and tested with those stored of the training spoken words to achieve the speaker identification tasks, also the DWT gives fixed amount of data that can be utilized modesty by SVM.Finally, the proposed method is tested and trained upon a very large data base with results limited to ten speakers only (5 males and 5 females) with words of maximally 17 phenomena and its performance gives an accurate and stable results which rises the algorithm efficiency and reduce the execution time with 97% overall accuracy.