Superalloys are extensively used in various industries like aerospace, chemical and petrochemical industries due to their properties such as high strength at elevated temperature and good corrosion resistance. On the other hand, owing to these properties superalloys are classified as difficult to cut materials. In the present work, the effect of cutting parameters on TOOL life in turning of N-155 iron-nickel-base superalloy is investigated. Cutting speed and feed rate, each at five levels, were selected as cutting variables. Relationship between cutting parameters and output variable, i.e. TOOL life, was modeled using response surface methodology (RSM). The results showed that there was good agreement between the experimental results and the predicted values using the developed mathematical model. Additionally, analysis of variance was implemented to evaluate the adequacy of the regression model and respective variables. ANOVA results indicated that the cutting speed had more effect on TOOL life than feed rate. Moreover, WEAR MECHANISMS and failure modes of the cutting edges were analyzed by using the images of scanning electron microscope (SEM) at different cutting speeds and feed rates. It was observed that abrasion and adhesion were the most dominant WEAR MECHANISMS in this study. Finally, desirability function was used so as to predict optimum cutting parameters for achieving maximum TOOL life. The results of optimization process showed that 50 m/min cutting speed and 0.2 mm/rev feed rate are the optimum cutting parameters which result in 7.526 min TOOL life.

In industrial production systems, one of the most important factors in the economic field machining operations is to reduce the TOOL WEAR. Abrasive nature of the phenomenon is dependent on many factors, including temperature, machining conditions (cutting speed, forward speed cutting depth) and shortage of coolant. All of these parameters lead to TOOL WEAR. If we do not study the TOOL WEAR precisely, it will impose extra cost in manufacturing processes. These extra costs are due to the reproduction of goods which is due to the replacement of TOOLs and the sharpening operation of TOOLs. In this study, an in vitro study done on TOOL WEAR SNMM120404 and the effect of parameters such as rotation speed of the spindle chuck, feed rate and depth of TOOL WEAR is investigated.

Composites are combined with at least two materials,one is considered as the base material and later is used to improve the material properties. This study is also about one of the widely used composites so-called aluminium metal matrix composites (Al-MMC) and additional reinforcing particles, including silicon carbide (SiC). The main features of such composite are high hardness and strength, as well as lightweight. The main disadvantages are low machinability and high WEAR in cutting TOOLs. Several studies have been conducted to improve the machinability of MMCs. The literature review in this domain denotes that limited studies have yet been conducted on the effects of different cooling methods, especially cryogenic cooling and related strategies, on the WEAR and TOOL life in the machining of MMCs. Therefore, in this study, the effects of cutting parameters and cryogenic cooling on the TOOL WEAR in the machining of A356-10%SiC were studied. Based on the statistical analysis, it was observed that the effects of cutting parameters and cryogenic cooling parameters on the TOOL WEAR was significant (R2=0. 97, and a robust mathematical relationship exists between TOOL WEAR and the studied parameters. Moreover, compared with previous studies, it was observed that TOOL life under cryogenic conditions was increased by around 26% compared to readings made under dry conditions.

Controlling system state is a purpose in system automation. Monitoring of TOOL WEAR is the same in machining process. Dimensional accuracy, surface quality of work, machining economy, machine TOOLs life and machining forces were influenced by TOOL WEAR. So estimating the TOOL WEAR without stopping the operation can be a monitoring method. In this paper an on-line multisensoring approach for TOOL WEAR estimation in face milling proposed. Measuring of forces and motor current simultaneously is used. Many experimental tests have been done in different machining conditions and a back propagation neural network (bp) has been designed. This neural network can estimate the TOOL WEAR amount with a high accuracy by measuring of machining forces and motor current simultaneously. This method can be used in controlling and monitoring of machining process.

Statistical process control charts are generally designed assuming that when the process is in control the observations are independent and identically distributed (i.i.d.) over time. However, the assumption of independence is easily violated when a process inherently generates auto correlated observations. When traditional control charts are applied to such processes then the false alarm rate experienced would be higher than what is expected.Machining process, due to the TOOL WEAR out, usually generates auto correlated observations. If such phenomenon is not incorporated in the chart design then one should expect a pattern in the plotted observations that will eventually lead to false alarms from time to time. This paper discusses the application of logistic regression to model and eliminate patterns that appear on fraction nonconforming items chart because of TOOL WEAR.Numerical results indicate significant improvements.

Recently the metal-bonded diamond TOOLs have been found dramatic applications for the processing of natural stone in civil engineering. Although, significant improvement in TOOL performance have been made, however diamond TOOLs are still the main factor in order to achieve the economic and optimum sawing conditions.The influence of machining parameters on diamond TOOL WEAR during sawing a kind of granite stone belongs to Kezab region of Yazd city in Iran was investigated. A machine TOOL was designed and made for this purpose. A series of tests were scheduled. The radial WEAR of the saw-blade was measured by using a micrometer at the end of experiments. The cutting forces during sawing were measured with using of mechanical dynamometer. The results show that with the increase of feed rate the TOOL WEAR reduces. This may be due to the reduction of cutting forces and/or the reduction of cutting temperature. On the other hand, with the increase of cutting speed the TOOL WEAR also increases. This may be because of rising the temperature and mechanical impacts during machining. These factors affect on the diamond grains and the cobalt binder of the segments.

Surface quality along with the low production cost, play significant role in today’s manufacturing market. Quality of a product can be described by various parameters. One of the most important parameters affecting the product quality is surface roughness of the machined parts. Good surface finish not only assures quality, but also reduces the product cost. Before starting any machining process, surface finish is predictable using cutting parameters and estimation methods. Establishing a surface prediction system on a machine TOOL, avoids the need for secondary operation and leads to overall cost reduction. On the other hand, creating a surface estimation system in a machining plant, plays an important role in computer integrated manufacturing systems (CIMS). In this study, the effect of cutting parameters, cutting TOOL vibration, TOOL WEAR and cutting forces on surface roughness are analyzed by conducting experiments using different machining parameters, vibration and dynamometers sensors to register the amount of TOOL vibration amplitude and cutting force during the machining process. For this, a number of 63 tests are conducted using of different cutting parameters. To predict the surface quality for different parameters and sensor variables, an ANN model is designed and verified using the test results. The results confirm the model accuracy in which the R2 value of the tests was obtained as 0.99 comparing with each other.

TOOL WEAR is a purpose in machining process. Dimensional accuracy, surface quality of work, machining forces, vibration, TOOL life, and machine TOOLs life are influenced by TOOL WEAR. In other hand estimating the TOOL WEAR without stopping the operation is an essential problem in system automation. In this paper an intelligent approach for TOOL WEAR estimation from machining forces is proposed. Many experimental tests have been done in different machining conditions and TOOL WEAR amount have been measured directly. With this data, a back propagation (bp) neural network have been designed and trained. This neural network can estimate the TOOL WEAR amount with a high accuracy receiving data of depth of cut, feed rate, revolution and machining forces in X, Y and Z coordinates.

Deep sub-zero treatment is a complementary operation performed on all types of TOOL steels, carbonized and high-speed steels to improve WEAR resistance and hardness. Among these TOOL steels, H13 is a hot work TOOL steel that have an extended application in industry as a hot deforming TOOL. This paper investigates the WEAR behavior of deep cryogenic treated H13 hot work steel at operating temperature. Two quench-tempered and quench-subzero-tempered samples are compared. The microstructures of the specimens were determined by scanning electron microscopy, and the structures were determined by X-ray diffraction. Vickers hardness used for determining hardness after each treatment. The WEAR test was carried out at 250° C (mold temperature on forging of copper base alloys). Finally, the WEAR surface was examined by scanning electron microscope equipped with EDS analyzer. The results show that the highest hardness was in quench-subzero-tempered condition which is about 26% higher than the quench-tempered in oil conditions. This is due to the formation of fine, dispersed and uniform precipitates and higher martensite percentage in quench-subzero-tempered sample compared to quench-tempered sample. Quench-subzero-temper operation reduced the residual austenite percentage by 10% and improved the WEAR properties by 36% at 250° C. Examination of WEAR surfaces indicates the presence of oxidized surfaces adhered to the WEAR surface in the form of abrasive particles. These oxide levels were lower in quench-subzero-tempered sample than quench-tempered sample.

In manufacturing industry, it has been acknowledged that TOOL WEAR prediction has an important role in higher quality of products and acceptable efficiency. Being an emergingarea of research in recent years, drilling TOOL WEAR is an important factor which directly affects quality parameters of machined hole suchas hole centring, roundness, burr formation and finished surface. In this paper, the genetic equation for prediction of drilling TOOL flank WEAR was developed using the experimentally measured WEAR values and genetic programming for two different materials, AISI1020 and AISI8620 steels. These equations couldbe used to compare the behaviour of WEAR in both mentioned materials and analyse the effect of materials characteristics on WEAR rate and WEAR pattern. The suggested equations have been shown to be corresponding well with experimental data obtained for flank WEAR when machining in various cutting conditions. The results of experiments and equations showed that properties of work material can affect drill bit flank WEAR drastically. It was concluded that greater toughness and strength of AISI8620, compared to AISI1020, lead to higher cutting stresses and temperatures, resulting more flank WEAR.