The precision of part machining is influenced by the tool life. Tools gradually wear out during the cutting process, which reduces the machining accuracy. Many studies have used machining parameters and sensor signals to predict flank wear; however, these methods have many limitations related to sensor installation, which is not only time-consuming and costly but also impractical in industry. This paper proposes an interval type-2 fuzzy neural network (IT2FNN) based on the dynamic-group cooperative differential evolution algorithm for flank wear prediction. Moreover, the Taguchi method is used to design cutting experiments for collecting experimental data and reducing the number of experiments. The CIE-xy color chromaticity values, spindle speed, feed per tooth, cutting depth, and cutting time are used as inputs of the IT2FNN, and the output is the flank wear value. The experimental results indicate that the proposed method can effectively predict flank wear with higher efficiency than other algorithms. INDEX TERMS Flank wear, chip surface, color calibration, interval type-2 fuzzy neural network, differential evolution

As prosperous in automobile and aerospace industries, the competition in machining industry is getting fierce. The requirements for product lie on not only the improvement of the quality, but also the processing speed. The Nickel based superalloy can maintain a very good strength under highly elevated temperature, so it is wildly applied to the components and parts which need to resist to high temperature, such as blades of aircraft turbine engines, rotors, turbine parts for nuclear power plants, heat exchangers, reciprocating engines, heat-treating equipments, liquid rockets, space vehicles and equipments for chemical and petro chemical industries. Nevertheless, Nickel based superalloy’s high strength, low thermal conductivity and working hardening will lead to a short cutting tool life and low efficiency in machining. At the same time, AISI4340 is also widely applied to processing parts for gears, piston, automobiles and machines.

Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scans of the brain. Manual segmentation is extremely time-consuming and tedious. Therefore, fully automated MS detection methods are being developed which can classify large amounts of MR data, and do not suffer from inter observer variability. In this paper we use standard fuzzy c-means algorithm (FCM) for multi-spectral images to segment patient MRI data. Geodesic Active Contours of Caselles level set is another method we implement to do the brain image segmentation jobs. And then we implement anther modified Fuzzy C-Means algorithm, where we call Bias-Corrected FCM as BCFCM, for bias field estimation for the same thing. Experimental results show the success of all these intelligent techniques for brain medical image segmentation.

Abstract. Nickel-base superalloy is a special super heat resistant alloy developed by U.S in 1970s. It is mainly applied to turbine parts as well as high-temperature components. Nickel-base superalloys exhibit an excellent high strength, low thermal conductivity and creep resistance as well as work hardening. It is the most difficult to be machined with high-speed cutting among different sorts of high-temperature superalloys and is a material presenting multifold challenges for machining. The purpose of this study aims at the machinability of Nickel-base alloys. Engineering statistical analysis was employed to observe the cutting speeds, feed rates and surface roughness at first place. The researcher further applied the half-normal probability plot (HNPP), Pareto analysis and ANOVA to identify the cross effects and probed into the characteristics of Nibase alloy.

Swivel spindle head is a key component used in gantry type five-axis machine tool of high performance and is of great importance in its application and design. Nowadays, more and more components are manufactured by high precision CNC machine tools, such as components of spaceflight, renewable energy and automobile, etc. Therefore, high precision machine tools of multiple axes are more and more urgently demanded, while dual axis rotary head is one of the most important components for a multi-axis machine tool. Hence, it will be a key to develop dual axis spindle head that meets high precision needs. The study explores the highly responsive direct-driving motor able to drive the spindle head to rotate with multi-driving rotary technology. The dual-driving motor rotates via multi-driving units, generates torsion that magnifies and eliminates its clearance, and then drives the spindle head to rotate. Results of the test show that the completed machine tool can meet the standards of dual axis rotary head with high precision in, no matter, speed, distance, positional accuracy, repeated accuracy or maximum torque, etc.