Cutting Force

Energy consumption model of peeling process is required to optimise main influenced factors as well as to limit peeling waste and consumed energy. Mechanical peeling process using an abrasive-cutter brush which applies both abrasive a nd cutting forces was modeled. Choosing the input and output variables which would be industria lly applicable was attempted. Three variables, namely, angular velocities of abrasive-c utter brush ( ωp), the degree of unevenness of produce surface ( φ), and the shape of the abrasive-cutter brush ( λ), were chosen as independent variables and the peel losses per unit time were ch osen as the output of the model. The developed model was verified using the experimental results o f peeling by abrasive-cutter brush for two varieties of pumpkin named Jarrahdale and Jap. The results showed correlation coefficients between predicted and experimental values of the Ja p (0.98) and Jarrahdale (0.96) were statistically significant. It is concluded that the relation...

Tool performance of conventional tools is poor and a major constraint when used in milling titanium alloys at elevated cutting speeds. At these high cutting speeds, the chemical and mechanical properties of Ti6Al4V cause complex wear mechanisms. In this paper, a fine-grain polycrystalline diamond (PCD) end mill tool was tested, and its wear behavior was studied. The performance of the PCD tool has been investigated in terms of tool life, cutting forces, and surface roughness. The PCD tool yielded longer tool life than a coated carbide tool at cutting speeds above 100 m/min. A slower wear progression was found with an increase in cutting speeds, whereas the norm is an exponential increase in tool wear at elevated speeds. Observations based on scanning electron microscope (SEM) and energy dispersive spectroscopy (EDAX) analysis suggest that adhesion of the workpiece is the wear main type, after which degradation of the tools accelerates probable due to the combined effect of high temperature degradation coupled with abrasion.

... Permissions & Reprints. A finite element analysis of orthogonal machining using different tool edge geometries. Yung-Chang Yen, Anurag Jain and Taylan Altan Corresponding Author Contact Information , E-mail The Corresponding Author. ...

The eternal goal achieving optimum gear manufacturing results in a quick and flexible way can be obtained by efficient machine tools and thorough processes know how. Gear manufacturing is associated with complicated generating kinematics, chip formation and tool wear mechanisms. To capture quantitatively the tool wear progress and the cutting loads, empirical, analytical, numerical as well as FEM-based methods describing the chip geometry and predicting the tool life and cutting forces have been developed. The application of innovative tool materials and coatings, optimized tool geometries and appropriate conduct of reconditioning procedures contribute to the significant reducing of the manufacturing cost.

Investigations into parameters affecting cutting forces in foods were undertaken to identify basic trends such as the relationship of cutting forces to cutting speeds and food temperatures. A simple plain blade was used to cut three typical foodstuffs (cheese, bacon and beef) at three feed speeds and three temperatures. After each cut the blade was passed through the product a second time to measure forces indicative of friction on the sides of the blade.Cutting forces for cheese decreased with increasing temperature and increased with cutting speed. The relatively homogeneous nature of the samples resulted in consistent and repeatable measurements. For bacon, variable salt content gave rise to different ice contents and thus hardnesses in samples at the same ‘frozen’ temperatures. Layers of fat and muscle boundaries also produced marked deviations from the average forces. Force results were therefore scattered but increased with decreasing temperature. The effect of cutting speed was not consistent for all forces, but higher speeds generally produced higher forces. For beef, there was a marked difference between frozen and unfrozen samples but little difference between samples at different unfrozen temperatures. In unfrozen samples, cutting speed had little effect on forces, whereas faster cutting speeds produced higher forces in frozen samples. The proportion of total cutting forces made up by friction was found to be consistent over all temperatures and speeds for cheese and bacon, but markedly higher in the frozen beef samples compared to the unfrozen samples.

In this paper, a wide literature review of soft computing methods in conventional machining processes of metal matrix composites is carried out. The tool wear, cutting force along with surface quality are presented in the different types of machining processes and examined thoroughly. Summary of the different particular soft computing approaches in machining such as turning, milling, drilling and grinding operations are thoroughly discussed. Furthermore, this work put emphases on the optimization and modeling of the machining process. The study will emphasis on the most general methods used by researchers in literature for developing the statistical and mathematical modeling using soft computing approaches including, genetic algorithm, response surface methodology, fuzzy logic, artificial neural network, Taguchi method and particle swarm optimization. In last section the comprehensive open issues and conclusion are presented for application of soft computing techniques in machining of metal matrix composite performance prediction and optimization.

Surface roughness prediction for the end-milling process, which is one of the major cutting processes, is a very important economical consideration in order to increase machine operation and decrease production cost in an automated manufacturing environment.
In this study; prediction of surface roughness (Ra) for Brass (60/40) material based on cutting parameters: cutting speed, feed rate, and depth of cut; was studied. 
Adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in the end milling process. Surface roughness was used as dependant variable while cutting speed of range (750 - 1750rpm), feed rate of range (50 - 250mm/min) and depth of cut of range (0.3 - 0.7mm) were used as predictor variables. Normal and feed forces were used as predictor variables to verify the ANFIS model. Different membership functions were adopted during the training process of ANFIS.
Surface roughness was measured in an off line manner using stylus based profile-meter (surtronic 3+). The normal and feed forces were measured in an on-line manner using two components dynamometer.
The effects of cutting parameters on the normal force, feed force and surface roughness were discussed.  Experimental test data were used to examine the ANFIS model by defining the reliability and percentage error of the model. Experimental results demonstrate the effectiveness of the proposed model. While the predicted surface roughness was compared with measured data; the mean square error has been found equal to 8.5 % hence the achieved accuracy is equal to 91.5 %.
Although this work focuses on prediction of surface roughness for end-milling operation, the concepts introduced are general; ie., prediction of surface roughness using ANFIS can be applied to many other cutting and machining processes.

Cutting force required to cut a stalk of fruit is determined experimentally and method of experimentation is discussed in the paper. Harvesting of fruits is carried out by various methods viz. jerking, by hands or by mechanization etc. Fruit harvesting by severing the stalk has its own advantage. The method presented generates the cutting force and cutting torque data for stalks which is useful in designing harvesting tools. Present experimentation carried out is limited to stalks of fruits mango, guava, custard apple and stalks of a vegetable drumstick. This experiment may be applied to generate the cutting force and cutting torque data required to sever branch tips and small stems which will be useful in developing optimum design of secateurs and trimming scissors etc. for garden tools.

Inappropriate machining conditions such as cutting forces cause tool failures, poor surface quality and worst of all machine breakdowns. This may be avoided by using optimal machining parameters, e.g. feed-rate, and continuing to monitor it throughout the machining process. To optimize feed-rate, we propose a system that consists of an optimisation module, a process control module and a knowledge based evaluation module. STEP-NC is the underlying data model for optimisation. Given the nominal powers, the cutting force can be estimated based on the higher-level production information such as workpiece properties, tool materials and geometries, and machine capabilities. The main function of the Process Control module is process monitoring and control. The output is the desired actual feed-rate. Finally, the actual feed-rate is recorded and evaluated in the Knowledge Based Evaluation module.