Numerical simulation of non-linear chatter vibration in turning

In this paper, basic relationships and algorithms for numerical simulation of non-linear, self-excited vibrations in single degree-of-freedom cutting systems are presented. Non-linearities due to the tool leaving the cut, as well as interference between the cutting tool clearance face and cutting surface waviness, were taken into consideration. Examples of vibration simulation results are shown.     

Chip Form Monitoring through Advanced Processing of Cutting Force Sensor Signals

This paper draws on the activities of the CIRP Collaborative Work on “Round Robin on Chip Form Monitoring” carried out within the Scientific-Technical Committee Cutting (STC-C). This collaborative work involved the following main round robin activities: (a) generation, detection, storage and exchange of cutting force sensor signals obtained at different Laboratories during sensor-based monitoring of machining processes with variable cutting conditions yielding diverse chip forms, and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies, both aimed at comparing chip form monitoring results achieved on the basis of innovative analysis paradigms.     

Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network

Cutting forces and acoustic emission measures as a function of tool wear are presented for different cutting parameters and their applicability for tool condition monitoring is evaluated. The best of them, together with cutting parameters, were chosen as inputs to a feedforward, back propagation (FFBP) neural network; some training techniques were applied and their effectiveness is also evaluated. Conventional training of FFBP neural networks very soon leads to overtraining, hence to deterioration in the net response. Training of these nets depends very much on the initial weight values. A good way of finding satisfactory results is to introduce random distortions to the weight system, which efficiently push the net out of a local minimum of testing errors. An even more effective method may be to employ temporary shifts in the weights, alternately negative and positive. This has two advantages: (1) it brings the net to balance between training and testing errors and (2) it enables a great reduction in the number of hidden nodes.     

Modelling of dynamic cutting coefficients in three-dimensional cutting

A new theory to determine the dynamic cutting coefficients from steady state cutting data for three dimensional cutting has been developed. It is based on direct measurements of cutting forces, without any hypothesis relating to the steady state cutting. The experimental results show fairly good coincidence with the theoretical prediction of the stability limit.     

Characterization on White Etching Layer Formed During Ceramic Milling of Inconel 718

A comprehensive characterization of the near surface formed during the interrupted high-speed dry ceramic milling of IN718 was performed using light imaging, SEM/EDX, TEM and nano-hardness methods. It was found out that even an initial cut by a fresh tool creates a sub-surface alteration roughly 20 µm deep. The depth of altered sub-surface progressively changed to a roughly 40 µm when the tool reached an approximately half of its life, and almost 60 µm at the tool’s end of the life. In the last two cases, the visible WEL (utilizing a light microscope) of the thickness roughly 6 and 15 µm was created, respectively. The outermost layer of the deformed subsurface was found to be for all three cases approximately 1.5 µm thick and composed of dynamically recrystallized γ phase grains with the average diameter of approximately 150 nm. This layer was free of δ phase and γ′ or γ″ precipitates. It was followed by a plastically deformed zone.     

Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling

Prediction of cutting forces is very important for the design of cutting tools and for process planning. This paper presents a fuzzy modelling method of cutting forces based on subtractive clustering. The subtractive clustering combined with the least-square algorithm identifies the fuzzy prediction model directly from the information obtained from the sensors. In the micro-milling experimental case study, four sets of cutting force data are used to generate the learning systems. The systems are tested against each other to choose the best model. The obtained results prove that the proposed solution has the capability to model the cutting force in spite of uncertainties in the micromilling process.     

Tool condition monitoring in micromilling based on hierarchical integration of signal measures

This paper presents a tool wear monitoring strategy in micromilling of cold-work tool steel, 50 HRC with a ball endmill d = 0.8 mm. The strategy is based on a large number of AE and cutting forces signal features and a hierarchical algorithm. In the first stage of the algorithm, the tool wear is estimated separately for each signal feature. In the second stage, the results obtained in the first stage, are integrated into the final tool condition evaluation. The obtained results prove that the proposed algorithm enables reliable evaluation of tool wear in spite of strongly disturbed signal features.     

Cutting tool wear detection using multiclass logical analysis of data

This article presents a new tool wear multiclass detection method. Based on the experimental data, tool wear classes are defined using the Douglas–Peucker algorithm. Logical analysis of data (LAD) is then used as machine learning, pattern recognition technique for double objectives of detecting the present tool wear class based on the recent sensors' readings of the time-dependent machining variables, and deriving new information about the intercorrelation between the tool wear and the machining variables, by doing pattern analysis. LAD is a data-driven technique which relies on combinatorial optimization and pattern recognition. The accuracy of LAD is compared to that of an artificial neural network (ANN) technique, since ANN is the most familiar machine learning technique. The proposed method is applied to experimental data those are gathered under various machining conditions. The results show that the proposed method detects the tool wear class correctly and with high accuracy.