Explore the vast range of titles available.

In 5-axis machining, the existing tool’s axis vector optimization methods are limited since they only consider the global collision between the tool and the workpiece while aiming at the ball-nosed cutter. A multi-factor vector optimization method for the face milling cutter shaft is proposed to solve this problem. This method comprehensively considers machining global collision, cutting force, the angular displacement of a rotating shaft, and angular speed. An improved global collision detection method of cutter axis vector based on the NURBS surface principle is developed, and a global collision detection algorithm is employed to determine the cutter machining global collision. The relationship model between the end-milling cutter axis vector and cutting force variation is established to optimize the cutting force. In addition, an optimization model of angular displacement and velocity of the machine tool’s rotating axis is proposed based on Dijkstra optimal path algorithm. The CAM software simulation and experimental validation are conducted using a large propeller with a complex surface. The tool’s axis vector optimization algorithm is applied to the propeller results. Comparing the tool’s axis vector optimization results to those obtained without optimization, it is discovered that the surface workpiece’s machining quality has significantly increased.

In this paper, a series of milling tests were carried out in order to identify the effects of cutting speed on cutting forces and tool wear when high-speed face milling Inconel 718 with Sialon ceramic tools. Both down-milling and up-milling operations were conducted. The cutting forces, tool wear morphologies, and the tool failure mechanisms in a wide range of cutting speeds (600–3,000 m/min) were discussed. Results showed that the resultant cutting forces firstly decrease and then increase with the increase of cutting speed. Under relatively lower cutting speeds (600 and 1,000 m/min), the dominant wear patterns is notching. Further increasing the speed to more than 1,400 m/min, the notching decreases a lot and flank wear becomes the dominant wear pattern. In general, at the same cutting speed, flaking on the rake face and notching on the flank face are more serious in down-milling operation than that in up-milling operation with the same metal removal volume. However, the surface roughness values for down-milling are lower than that for up-milling.

Multi-blade face milling cutters are widely used in the finish machining of mechanical parts. The cutting force in the milling process is a crucial factor that promotes the chatter of the machine spindles, which can be used to predict the machined surface roughness. In this paper, a novel cutting force prediction model based on non-uniform rational basis splines (NURBS) and finite element method (FEM) is proposed. Single blade cutting forces under different parameters are simulated by FEM, and a cutting force model of the single blade is established by the NURBS interpolation method. Then, combined with the tool tip motion model, the cutting force of the multi-blade face milling cutter can be predicted. To verify the correctness of the cutting force predicted by the proposed method, the common coefficient-based cutting force mathematical prediction method is utilized as benchmark for comparison with the predicted results. The accuracy is verified by comparison with experimental data. According to the collected experimental data, the proposed model is proved to be an accurate and efficient method to predict the cutting force of the multi-blade face milling cutter in the milling process.

The fixed-setting face-milled curvilinear cylindrical gear features teeth that are arc-shaped along the longitudinal direction. Some researchers hypothesize that this arc-tooth may enhance the lubrication conditions of the gear. This study focuses on this type of gear, employing both finite element analysis (FEA) and analytical methods to determine the input parameters required for elastohydrodynamic lubrication (EHL) analysis. The effects of assembly errors, tooth surface modifications, load, and face-milling cutter radius on the lubrication performance of these gears are systematically investigated. The finite element model (FEM) of the gear pair is utilized to calculate the coordinates of contact points on the tooth surface and the corresponding contact pressures at the tooth surface nodes throughout a meshing cycle. Subsequently, the normal load on specific gear teeth is determined using a gradient-based approach. Entrainment speed, slip-to-roll ratio, and effective radius near the contact points on the tooth surface are derived through analytical methods. The data obtained from FEA serve as input parameters for EHL simulations. The lubrication performance of the curvilinear cylindrical gear is evaluated through example studies. The findings indicate that using FEA to provide input parameters for EHL simulations can reveal the occurrence of edge contact phenomena during gear meshing, allowing for a more accurate representation of the gear’s lubrication conditions. The lubrication performance of the curvilinear cylindrical gear is shown to be independent of the face-milling cutter radius but is significantly influenced by the size of the contact pattern on the tooth surface. Curvilinear gears with larger contact patterns demonstrate superior lubrication performance.

Purpose Recognition of tool failure is an everlasting problem for the manufacturing industry, which leads to diminishing productivity and quality of the product. Much research has been conducted on intelligent tool condition monitoring (TCM) techniques. Machine-learning-based techniques comprise pattern recognition of signals to obtain intelligent decision-making. However, dealing with diversified data distributions’ present and future moments is complex and needs robust models. This paper aims to develop a generalized statistical model of spindle vibrations trained using random forest. Methods The real-time vibration evolved during machining was collected by performing experimentation considering different tool wear conditions of the face milling cutter. A thorough statistical analysis was undertaken considering hyperparameter tuning of the random forest—a multitude of decision trees, thereby attempting to provide generalization and robustness to the model. Results Compared to various hyperparameters, the maximum depth of the tree showed a more significant influence on the performance of the vanilla model as it regulates its growth on a macro-level. While tuning the tree concerning minimum sample split, no notable splits were observed because the minimum splitting requirement of the node attained a peak and led to the underfitting of random forests. Thus, the allocation of the improvised value of minimum sample split eliminates the anomaly posed by underfitting. Conclusion The robustness analysis of the random forest tree consisting of hyperparameter tuning has successfully eliminated the underfitting and overfitting of the model by showcasing an improvement and downfall in the performance, respectively. Here, the downfall in the accuracy indicates the ability of the tuned model to access unknown instances, thereby showing the generalization of the model concerning blind data. The results obtained using the overall framework indicate that the tuned random forest tree algorithm is appropriate and proficient for fault classification. This ML-based classification approach shall offer effective and optimum cutting tool usage and save considerable tooling costs.

The increasing environmental and health concerns of conventional emulsion flood coolants have motivated the use of vegetable oil in the form of minimum quantity lubrication (MQL) in machining. This paper presents comparative evaluation of high oleic soybean oil (HOSO)–based MQL flow rates at 10, 30, 50, 70, and 90 ml/h with a mineral oil–based emulsion flood coolant as a benchmark in face milling of Inconel 718 using AlTiN/TiN-coated carbide inserts. Cutting forces, tool wear, and surface roughness were measured and analyzed. The results show that MQL oil flow rate at 70 ml/h gave the longest tool life comparable to that of mineral oil–based emulsion flood cooling, while 10 ml/h flow rate gave the shortest tool life. Also, 70 ml/h flow rate gave the lowest resultant cutting force among all MQL oil flow rates and conventional emulsion cooling at tool life. Increasing HOSO-based MQL flow rate improves surface roughness and reduces tool wear by providing enough thin lubrication film but also leads to an increase in chip affinity and formation of large built-up edges (BUEs) as the MQL flow rate reaches 90 ml/h. At lower HOSO-based MQL flow rate, tool wear mechanism is predominantly abrasion due to large surface friction, while at higher HOSO-based MQL flow rate, tool wear mechanism is adhesion leading to excessive BUEs. HOSO-based MQL flow rate of 70 ml/h and air pressure of 4.14 bar are recommended when face milling Inconel 718 and are demonstrated to be a potential replacement of mineral oil–based conventional emulsion flood cooling strategy for machining of difficult-to-cut metals.

Ti 2 AlNb intermetallic alloy is currently the most promising lightweight high-temperature resistant material in the aerospace industry, owing to its high specific strength, favourable room temperature plasticity, outstanding temperature strength, and creep resistance. High speed cutting technology is characterized by low cutting force, minimal thermal deformation, high material machining efficiency and precision, which is widely used in machining TiAl intermetallic alloys and associated key components to ensure superior machined surface quality and dimensional accuracy. However, there are serious tool wear during high speed cutting of Ti 2 AlNb intermetallic alloys because of its high specific strength and temperature strength, and the tool wear mechanism is unknown. In this paper, the high-speed face milling trials of Ti 2 AlNb intermetallic alloys are performed to investigate tool wear evolutions and wear mechanisms. More specifically, tool wear morphologies, tool tip breakage, machined surface roughness, and cutting forces are investigated in detail. Results indicate that high-speed face milling results in severe tool wears, leading to a rapid increase in cutting forces and machined surface roughness during the severe wear stages. The tool's service life is limited to 228 s due to tool tip breakage resulted from coating delamination, cracking, and mechanical impact. Tool wear morphologies encompass tool rake face and flank face wear, exhibiting typical characteristics of coating delamination and microchipping. Adhesive wear and oxidation wear are the primary mechanisms of tool wear, occurring on both the rake face and flank face. The experimental results can provide a reference for high speed machining of TiAl material and promote the application of the material.

In face milling processes, the surface roughness of the machined part reflects the cutting performance of face milling cutter. Surface roughness depends on different factors including feed direction, axial and radial run-out errors, and cutting tool geometry. In this paper, an algorithm considering the effects of static and dynamic factors on surface roughness for predicting the surface roughness is proposed. This work is focusing on straight-edged square insert. The dynamic characteristics of the milling process are also introduced. An electronic impact hammer is used to identify the dynamic parameters of the cutting system. Milling experiments are conducted to validate the prediction model. Results show that the prediction model can estimate the surface roughness of the machined parts after face milling. This paper provides an in-depth understanding of the relationship between machined surface roughness and process conditions especially for axial and radial run-out errors induced by static deformation and Z -axial relative displacement induced by forced vibration. The outcome of this research will lead to methodologies for cost-effective monitoring and surface roughness control.

Ceramic matrix composites of type C/SiC got paramount importance due to their special properties like high specific strength, high specific rigidity, high-temperature strength, and high wear resistance. Their applications are increasing rapidly for space, military, and aerospace industries. However, due to inhomogeneous, anisotropic and varying thermal properties of these composites, there are issues to achieve desired quality, high efficiency, and cost-effective processing in machining. In this regard, the cutting force is the most critical parameter which is required to be minimized for such composites to achieve better quality and minimum defects, especially in milling processes. In this research, brittle fracture approach was adopted and a cutting force model was developed from C/SiC composites for rotary ultrasonic face milling (RUFM) process. The experimental RUFM was carried out on C/SiC material and found that the cutting force decreased significantly with the increase of cutting speed, whereas the same was found increased with the increase of feed rate and cutting depth. By comparison of the experimental and simulation data of the cutting force, it was found that the errors are below than 10 % in most of the sets of parameters. The variation found is due to the heterogeneity and other complex properties of C/SiC composites. The developed cutting force model then further validated through another set of experiments, and the results were almost the same as before experiments. So, the cutting force model developed in this paper is robust and it can be applied to predict the cutting force and optimization of the process.

Aiming at the problems of uneven illumination at the edge of the surface damage image of the face milling cutter, the blade line at the damage cannot be accurately identified, and the slow recognition rate of the traditional image processing technology, an on-machine accurate measurement method for the surface damage of the face milling cutter, is proposed. The industrial camera, lens, and adjustable LED ring lighting are placed on the camera support, and they are used to collect the image of the flank face of the face milling cutter. This method first divides the tool damage area into the tool wear bright zone area and the damaged edge missing area and selects the specific tool position area for template matching. Next, the tool damage area is extracted, the arc fitting method is used to fit the missing area of the broken edge, the edge curve is reshaped, and the improved sub-pixel edge detection method is used to extract the lower boundary of the tool wear bright zone area. The cutting edge curve of the damaged area and the lower boundary edge curve of the wear area are spliced to obtain the tool damage area, and finally, the maximum damage width value of the tool flank is calculated. The on-machine detection platform is built to carry out the milling experiment of the face milling cutter. The damage value extracted by this method is compared with the damage value measured by the super depth of field microscope. The average difference in the tool damage value is within 3%. The results show that the proposed method can effectively detect tool damage under the premise of ensuring efficiency on-line and in-process and provides an effective solution for the condition monitoring of face milling cutter in actual machining.