No abstract

In this study, the correlation between chip surface chromaticity and wear of cutting tools is established through experiments, and a system for judging and predicting tool wear by observing chip color is proposed. At present, the life prediction of cutting tools is indirectly measured and predicted by using vibration and current. In this study, chip color change is used to predict tool wear, and back-propagation Artificial Neural Networks (ANN) is used to predict and verify. The average error percentage between the predicted value and the actual value of tool wear is only 1.73% and 1.66%, respectively, which was confirmed by cutting test and verification experiments. This study uses Taylor’s tool life model and chip color to analyze, and after repeated tests and experimental analysis, the average error of repeatability is 4.5%. In the verification of stainless steel cutting hard-cutting materials, the equipment accuracy is between 0.5 and 3.0 color difference values of grade 2 to 3. Therefore, the measurement and model establishment of the system can accurately and quickly predict tool wear. In prediction experiment and analysis, the back neural network is used for test, the maximum error ranges are 0.0012 mm and 0.0097 mm, the mean error percentages are only 1.73% and 1.66%.

An airplane has about 6-million components and parts, mainly the engine, undercarriage, constructions and so on. Among them, nickel base materials are widely used, including engine, cartridge receiver, compressor drum and other industries application, such as energy, petrochemical industry, mould, etc. Nickel base alloy, with anti-corrosion and thermo stability, has good mechanical properties under high temperature. Since 1950s, due to the development of precision casting technology, a series of cast nickel base super-alloy with high strength has been developed, namely the super-alloy complying with the above conditions. In recent years, with the changing of military and civil aerospace, the usage amount of nickel base super-alloy also increases along with the date. This research mainly used Waspaloy of nickel base material to do cutting research and uses regression analysis to find significant factor of cutting tool's life, and performs the optimization experiment. The cutting research mainly uses TiAlN to coat cutting tools. In the conclusion, it discusses main factors that influence the tool life, such as the cutting speed, depth, feed rate and so on. Finally it establishes tool life formula with regression analysis method, in this case when cutting speed V = 30.77 m/min and cutting depth dp = 0.0367mm, the minimum wear prediction can be obtained.

Nickel base and titanium base materials have been widely applied to engines in aerospace industry, and these engines are essential components of airplanes. The machining characteristics of aerospace materials may cause machining cutters to be worn down in a short time and thus reduce the accuracy of processing. The plasma-assisted machining adopted in the research is a kind of the complex machining method. In the cases of nickel base and titanium base alloys, the method can heat workpieces in an extremely short duration to soften the materials for the ease of cutting so that the cutting force, cutter wear, and machining cost will all be reduced. The research adopted plasma heating to soften parts of the materials and aimed to explore the heating of nickel base alloy. The temperature variation of the materials was investigated and measured by adjusting the current and feed velocity. Moreover, Inconel-718 superalloy was adopted for the comparison with nickel base alloy for the observation of the influence and change brought by heat, and the method of exponential smoothing was adopted to conduct the prediction and analysis of thermal diffusion for understanding the influence and change brought by electric current on nickel base materials. Finally, given the current from 20 A to 80 A and feed velocity from 1,000 mm/min to 3,000 mm/min, the influence of thermal diffusion was investigated and the related model was built.

Due to the enormous engineering advancement in modern industries, the competition in manufacturing technologies has been increasingly intense, as can be seen in automobile and aerospace industries. Nickel-based superalloys are widely in the manufacture of components for aircraft turbine engines for cryogenic tankage, in liquid rockets, reciprocating engines, space vehicles, heat-treating equipment, chemical and petrochemical industries, because of their ability to retain high-strength at elevated temperatures. But, because of its characteristics of highstrength, poor thermal diffusion and work hardening, the cutting of nickel-based superalloys results in decreased tool life and poor efficiency of works. This is much more prominent than in other materials. AISI4340 are widely used in the manufacture of component parts for gear, pistons, and automobiles.

The nickel alloy has good mechanical strength and corrosion resistance at high temperature; it is extensively used in aerospace and biomedical and energy industries, as well as alloy designs of different chemical compositions to achieve different mechanical properties. However, for high mechanical strength, low thermal conductivity, and surface hardening property, the nickel alloy has worse cutting tool life and machining efficiency than general materials. Therefore, how to select the optimum machining parameters will influence the workpiece quality, cost, and machining time. This research will be using a new experimental design methodology to the cutting parameter planning for nickel-based alloy cutting test, and used the uniform design methodology to cutting test to reduce the number of experiments. Three independent variable parameters are set up, including cutting speed, feed rate, and cutting depth, and four dependent variable parameters are set up, including cutting tool wear, surface roughness, machining time, and cutting force. A nickel alloy turning parameter model is built by using regression analysis to further predict the I/O relationship among various combinations of variables. The errors between actual values and prediction values are validated. When the cutting tool wear (VB) is 2.72~6.18%, the surface roughness (Ra) is 4.10~7.72%, the machining time (T) is 3.75~8.82%, and the cutting force (N) is 1.54~7.42%; the errors of various dependent variables are approximately less than 10%, so a high precision estimation model is obtained through a few experiments of uniform design method.

With the development of the tool machine industry, the precision and quality demand for processing is more exquisite , from the traditional industrial manufacturing equipment in the past, to the current development of high-speed, high-precision , high-efficiency intelligent automation tool machine equipment , in the face of today's globalization,customerization andenvironmental awareness trend. Taking the vertical milling machine as an example, the feed system drives the machining in the direction of the tool machining to complete the cutting process.The rail contact surface of the feed system is grinding. It produces noise, bad vibration, processing rigidity is reduced, processing accuracy is reduced, friction almost thermal energy is generated, etc. Finally it reduces the service life of the servo motor. In this study, the contact between the slide rail and the slide seat was obtained by using the homemade device to obtain the influence of the contact area of the rail on the thickness of the lubricant. When the same contact area, the oil film thickness of the lower oil injection amount is small. The thickness of the oil film with higher oil injection amount is thicker. When the same oil injection volume, the thickness of the oil film of the larger contact area is small.

A hybrid method is proposed for optimizing rigid tapping parameters and reducing synchronization errors in Computer Numerical Control (CNC) machines. The proposed method integrates uniform design (UD), regression analysis, Taguchi method, and fractional-order particle swarm optimizer (FPSO) to optimize rigid tapping parameters. Rigid tapping parameters were laid out in a 28-level uniform layout for the experiments in this study. Since the UD method provided a layout with uniform dispersion in the experimental space, the UD method’s uniform layout provided iconic experimental points. Next, the 28-level uniform layout results and regression analysis results were used to obtain significant parameters and a regression function. To obtain the parameter values from the regression function, FPSO was selected because its diversity and algorithmic effectiveness are enhanced compared with PSO. The experimental results indicated that the proposed method could obtain suitable parameter values. The best parameter combination in FPSO yielded the best results in comparisons of the non-systematic method. Next, the best parameter combination was used to optimize actual CNC machining tools during the factory commissioning process. From the commissioning process perspective, the proposed method rapidly and accurately minimizes synchronization error from 23 pulses to 18 pulses and processing time from 20.8 s to 20 s. In conclusion, the proposed method reduced the time needed to tune factory parameters for CNC machining tools and increased machining precision and decreased synchronization errors.