System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing

The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values.

Experimental Study of Tool Wear when Milling Tropical Wood with Various Tool Materials

The paper presents a study on the performance of cutter tip for wood milling process. The tests were performed with the tropical wood samples which were milled in the double sided wood planer, the measured micro-geometrical parameters encompassing the linear wear and tooltip radius. The study primarily contributes to developing a far better understanding of the physical nature of cutting tool wear in response with the growing concern of many researchers. Given this basis, it does not only assist the selection of reasonable cutting tool but also enable the detection of the patterns in the cutting tool wear process. In terms of tool wear and bluntness, there has been a number of researches taking account into the physical nature of cutting tools, - providing basis for selection of cutting tools apart from clarification of the current pattern of tool wear and bluntness. The load applied to the cutter during wood milling is the load that changes marks periodically. When starting to work after tool sharpening and finishing, the first stage changes the microscopic geometry - tool run-in process (rapid initial wear), followed by constant conditions of wear before a rapid wear which leads to failure at last. The objective of this study is to determine the influence of the cutting path to the tooltip radius and linear wear of the cutting edge. The paper employs method of least squares and variance analysis in application of the Minitab software to determine regression equations for relation of the tooltip radius and linear wear to the relative cutting length. The ultimate goal is to predict the life of cutting tool when milling tropical wood.

Analysis of The Tool-Chip Interface Temperature In Cryogenic Hard Turning of Aisi D6 Tool Steel

Hard turning is considered a strong candidate to partially replace grinding in finishing operations. However, as in the grinding operation, hard turning produces high temperatures that contributes to accelerate the cutting tool wear. In order to minimize this effect, cutting fluids can be applied as an alternative, even when PCBN inserts are used as cutting tools. However, there are many drawbacks associated with the use of cutting fluids, particularly those of mineral base, as they are hazardous to the environment. In this context, the need for more eco-friendly cutting fluids is growing and liquid nitrogen (LN2) offers a promising alternative. Previous studies have shown that LN2 can significantly reduce the cutting tool wear rate in comparison with other cooling strategies, and this is normally attributed to a reduction in the tool-chip interface temperature. However, investigations on the tool-chip interface temperature in cryogenic machining are scarce in the literature, particularly with regard to the turning of tool steels, and this study was performed to partially fill this gap. The tool-chip interface temperature during the turning of quenched and tempered AISI D6 tool steel, under dry conditions and using LN2, was investigated. A tool-workpiece thermocouple system was developed for this purpose and calibrated using a data acquisition system based on the low-cost Arduino Uno platform. In the turning tests, liquid nitrogen was delivered at the tool flank face of PCBN inserts at three cutting speeds with a constant feed rate and depth of cut. The results showed that LN2 was effective in reducing the tool-chip interface temperature at the lowest cutting speed; however, when this cutting parameter was increased, the reduction in the interface temperature was minimal as compared with the dry condition.

Milling Inconel 718 Workpiece With Cryogenically Treated And Untreated Cutting Tools

Although Inconel 718 is an important material for modern aircraft and aerospace, it is a kind material, which is known to have low machinability. Especially, while these types of materials are machined, high cutting temperatures, BUE on cutting tool, high cutting forces and work hardening occur. Therefore, in recent years, instead of producing new cutting tools that can withstand these difficult conditions, cryogenic process, which is a heat treatment method to increase the wear resistance and hardness of the cutting tool, has been applied. In this experimental study, feed force, surface roughness, vibration, cutting tool wear, hardness and abrasive wear values that occurred as a result of milling of Inconel 718 material by means of cryogenically treated and untreated cutting tools were investigated. Three different cutting speeds (35-45-55 m/min) and three different feed rates (0.02-0.03-0.04 mm/tooth) at constant depth of cut (0.2 mm) were used as cutting parameters in the experiments. As a result of the experiments, lower feed forces, surface roughness, vibration and cutting tool wear were obtained with cryogenically treated cutting tools. As the feed rate and cutting speed were increased, it was seen that surface roughness, vibration and feed force values increased. At the end of the experiments, it was established that there was a significant relation between vibration and surface roughness. However, there appeared an inverse proportion between abrasive wear and hardness values. While BUE did not occur during cryogenically treated cutting tools, it was observed that BUE occurred in cutting tools which were not cryogenically treated.

An accurate cutting tool wear prediction method under different cutting conditions based on continual learning

Cutting tool wear prediction plays an important role in the machining of complex aerospace parts, and it is still a challenge under varying cutting conditions. To overcome the limitations of the existing methods in generalization ability when dealing with cutting conditions changing largely, this paper proposed a novel cutting tool wear prediction method based on continual learning. A meta-LSTM model is firstly trained for specific cutting conditions and can be easily fine-tuned with very small number of samples to adapt to new cutting conditions. Specifically, the meta-model could be continuously updated as machining data increase by using an orthogonal weights modification method. The experiment results show that the proposed method can realize accurate prediction of tool wear under different cutting conditions. Compared with existing methods including meta-learning methods, the range of adapted cutting conditions could be expanded as the task distribution of new cutting conditions is continuously learned by the prediction model.