Error Compensation of Thin Plate-shape Part with Prebending Method in Face Milling

Low weight and good toughness thin plate parts are widely used in modem industry, but its flexibility seriously impacts the machinability. Plenty of studies locus on the influence of machine tool and cutting tool on the machining errors. However, few researches focus on compensating machining errors through the fixture. In order to improve the machining accuracy of thin plate-shape part in face milling, this paper presents a novel method for compensating the surfacc errors by prebending the workpiece during the milling process. First, a machining error prediction model using finite element method is formulated, which simplifies the contacts between the workpiece and fixture with spring constraints. Milling fbrces calculated by the micro-unit cutting force model arc loaded on the error prediction model to predict the machining error. The error prediction results are substituted into the given formulas to obtain the prebending clamping forces and clamping positions. Consequently, the workpiece is prebent in terms of the calculated clamping forces and positions during the face milling operation to reduce the machining error. Finally, simulation and experimental tests are carried out to validate the correctness and efficiency of the proposed error compensation method. The experimental measured flatness results show that the flatness improves by approximately 30 percent through this error compensation method. The proposed mcthod not only predicts the machining errors in face milling thin plate-shape parts but also reduces the machining errors by taking full advantage of the workpiece prebending caused by fixture, meanwhile, it provides a novel idea and theoretical basis for reducing milling errors and improving the milling accuracy.

Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.

Wear Patterns and Mechanisms of Cutting Tools in High Speed Face Milling

High speed machining has received an important interest because it leads to an increase of productivity and a better workpiece surface quality. However, at high cutting speeds, the tool wear increases dramatically due to the high temperature at the tool-workpiece interface. Tool wear impairs the surface finish and hence the tool life is reduced. That is why an important objective of metal cutting research has been the assessment of tool wear patterns and mechanisms. In this paper, wear performances of PCBN tool, ceramic tool, coated carbide tool and fine-grained carbide tool in high speed face milling were presented when cutting cast iron, 45# tempered carbon steel and 45# hardened carbon steel. Tool wear patterns were examined through a tool-making microscope. The research results showed that tool wear types differed in various matching of materials between cutting tool and workpiece. The dominant wear patterns observed were rake face wear, flank wear, chipping, fracture and breakage. The main wear mechanisms were mechanical friction, adhesion, diffusion and chemical wear promoted by cutting forces and high cutting temperature. Hence, the important considerations of high speed cutting tool materials are high heat-resistance and wear-resistance, chemical stability as well as resistance to failure of coatings. The research results will be great benefit to the design and the selection of tool materials and control of tool wear in high-speed machining processes.

Dry face milling of titanium alloys

In machining titanium alloys, cutting tools generally wear out very rapidlybecause of the high cutting temperature resulted from the low thermal conductivity and density ofthe work material. In order to increase the tool life, it is necessary to suppress the cutting heatas much as possible by applying an abundant amount of coolant, but this will entail serioustechno-environmental and biological problems. To study the performance and avoid these limitations,a PVD-coated insert was used to the dry face mill of (alpha + beta) titanium alloys. As a result itwas found that the inserts exhibit an excellent cutting performance at low cutting speeds and feedrates, and there is no significant difference in the dominant insert failure mode between the wetand dry cutting in discontinuous cutting.