Research on the prediction of wear distribution of ball-end mill based on milling GH4169 nickel-based superalloy

Nowadays, the trend of milling technique is high speed machining. Plunge milling is one of the most efficient machining with high metal removal rate. The online monitoring of tool wear has been the key of plunge milling process. In this paper, the technique of tool wear monitoring in plunge milling process was studied. An online monitoring and faults diagnosis system based on PC104 bus and Lab VIEW was established. The system can acquire and analyze the vibration signals by acceleration sensors that were put in the milling tool holder. The model between the tool wear and vibration signals in plunge milling 45# steel was built by fitting of multivariable linear regression on the base of experiment. The model was realized on the open NC system, monitoring the tool wear degree quantificational. Through the verification to experiment data, the model was effective and reliable, and the error of forecast tool wear value was within 10% by the tool wear model. The model provided an important reference for the research of tool wear on the plunge milling.

In this paper, the carbide tool wear in micro milling is investigated and a new tool wear measurement method is proposed. Al7075 alloy and C45 steel is used as the work-piece materials due to their common application. The cutting tools with different machining time are obtained by experiments and the states of tool wear with different work-piece materials are compared. The influence of tool wear on micro milling forces and surface topographies is studied through experiments. The traditional tool wear measurement methods based on the tool edge radius, tool diameter and flank wear width are introduced. For a more complete explanation of the tool wear, a new tool wear measurement method based on the wear area is proposed. The carbide tool wear values based on the tool edge radius, tool diameter, and flank wear width and wear area are measured and the tool wear process is obtained. Compared with the traditional methods, the new tool wear measurement method based on the wear area can explain the tool wear process more reasonable.

In this paper, the carbide tool wear in micro milling is investigated and a new tool wear measurement method is proposed. Al7075 alloy and C45 steel is used as the work-piece materials due to their common application. The cutting tools with different machining time are obtained by experiments and the states of tool wear with different work-piece materials are compared. The influence of tool wear on micro milling forces and surface topographies is studied through experiments. The traditional tool wear measurement methods based on the tool edge radius, tool diameter and flank wear width are introduced. For a more complete explanation of the tool wear, a new tool wear measurement method based on the wear area is proposed. The carbide tool wear values based on the tool edge radius, tool diameter, and flank wear width and wear area are measured and the tool wear process is obtained. Compared with the traditional methods, the new tool wear measurement method based on the wear area can explain the tool wear process more reasonable.

Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques

High-volume fraction silicon particle-reinforced aluminium matrix composites (Si/Al) are increasingly applied in aerospace, radar communications, and large-scale integrated circuits because of their superior thermal conductivity, wear resistance, and low thermal expansion coefficient. However, the abrasive and adhesive wear caused by the hard silicon reinforcement and the ductile aluminium matrix leads to significant tool wear, decreased machining efficiency, and compromised surface quality. This study combines theoretical analysis and cutting experiments to investigate polycrystalline diamond (PCD) tool wear during milling of 70 vol% Si/Al composite. A key contribution of this work is the development of a tool wear model that incorporates reinforcement particle characteristics, treating them as ellipsoidal structures, which enhances the accuracy of predicting abrasive and adhesive wear mechanisms. The model is based on abrasive and adhesive wear mechanisms, and can analyze the interaction between silicon particles, aluminium matrix, and tool components, thus providing deeper insights into PCD tool wear processes. Experimental validation of the model shows a good agreement with the results, with a mean deviation of approximately 10%. The findings on the tool wear mechanism reveal that, as tool wear progresses, the proportion of abrasive wear increases from 40% in the running-in stage to 75% in the rapid wear stage, while adhesive wear decreases. The optimal machining parameters of 120 m·min–1 cutting speed (v c) and 0.04 mm·z–1 feed rate (f z), result in tool life of 33 min and surface roughness (S a) of 2.2 μm. The study uncovers the variation patterns of abrasive and adhesive wear during the tool wear process, and the proposed model offers a robust framework for predicting tool wear during the machining of high-volume fraction Si/Al composites. The research findings also offer key insights for optimizing tool selection and machining parameters, advancing both the theoretical understanding and practical application of PCD tool wear.

Effect of tool stiffness upon tool wear in high spindle speed milling using small ball end mill

Most of the existing energy-consumption models of machine tools are related to specific machine components and hence cannot be applied to other machine tools with different specifications. In order to help operators optimize machining parameters for improving energy efficiency, the tool tip cutting specific energy prediction model based on machining parameters and tool wear in milling is developed, which is independent of the standby power of machine tools and the spindle no-load power. Then, the prediction accuracy of the proposed model is verified with dry milling AISI 1045 steel experiments. Finally, the influence of machining parameters and tool wear on tool tip cutting specific energy is studied. The developed model is independent of machine components, so it can reveal the influence of machining parameters and tool wear on tool tip cutting specific energy. The tool tip cutting specific energy reduces with the increase in the cutting depth, side cutting depth, feed rate, and cutting speed, while increases linearly as the tool wears gradually. The research results are helpful to formulate efficient and energy-saving processing schemes on various milling machines.

The effect of feed rate on tool wear in milling of B4C p reinforced aluminum metal matrix composites (MMCs), produced by liquid phase sintering method, was investigated. Milling experiments on these composites were conducted with three different types of cementide carbide tools (uncoated, double coated (TiN + TiAlN) and triple coated (TiCN + Al2O3 + TiN)) for three different feed rates of 0.15 mm/z, 0.20 mm/z, and 0.25 mm/z. After milling experiments, an optical microscope was used to measure the magnitude of flank wear on the tools. Flank wear limit value, (V B ) = 0.3 mm was selected as a reference value for comparison of these tools. On the other hand, tool wear mechanisms were examined with the help of a scanning electron microscope (SEM). Experimental results indicated that higher feed rates led to lower tool wear for all type of tools and coated tools exhibited better performance than uncoated tool with respect to the flank wear.

Investigation of surface roughness and tool wear in milling of AISI 4340 steel has been conducted with a 4-flutes endmill. The relationships between cutting force, surface roughness and cutting parameter were investigated To determine the tool life of the tool. It is required to research the wear of the tool and the roughness of the machining results on the material tested. In this research, the test material used is AISI 4340 steel with a cooling method in the cutting process. The cutting model is slot mill cutting. To determine the wear of the cutting, a variation of the cutting thickness was performed where the selected cutting thickness was 0.5mm, 1mm, and 1.75mm. The tests that have been done are surface roughness testing, tool wear, and tool photos after cutting. The result obtained from the study is the highest level of roughness on the material cutting poses with a cutting thickness of 1.75 mm. In addition, the most optimal wear results on cutting with a cutting depth of 1 mm.

Effects of Oil Mist and Air Jet Flushing on Tool Wear in Milling of Ti6Al4V at High Speed

Micro components have been demanded increasingly due to the global trend of miniaturization of products and devices. Micro milling is one of the most promising processes for micro-scale production and differs from conventional milling due to the size effect introducing phenomena like the minimum chip thickness, making the prediction of micro milling process hard. Among challenges in micro milling, tool life and tool wear can be highlighted. Understanding tool wear and modelling in micro milling is challenging and essential to maintaining the quality and geometric tolerances of workpieces. This work investigates how to model the diameter reduction of a tool caused by tool wear for micro milling of H13 tool steel. Machining experiments were carried out in order to obtain cutting parameters affecting tool wear by considering the diameter reduction. Dry full slot milling with TiAlN (titanium aluminium nitride)-coated micro tools of diameter d = 400 μm was performed. Three levels of feed per tooth (fz = 2 μm, 4 μm and 5 μm) and two spindle speed levels (n = 30,000 rpm and 46,000 rpm) were used and evaluated over a cutting length of lc = 1182 mm. The results show that lower levels of feed per tooth and spindle speed lead to higher tool wear with a total diameter reduction over 22%. The magnitude of the cutting parameters affecting tool wear was determined by ANOVA (analysis of variance), and the model validation meets the statistical requirements with a coefficient of determination R2 = 83.5% showing the feasibility of the approach to predict tool wear using diameter reduction modelling in micro milling.

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process.

The tool geometry is generally of great significance in metal cutting performance. The response surface method was used to optimize chamfer geometry to achieve reliable and minimum tool wear in slot milling. Models were developed for edge chipping, rake wear, and flank wear. The adequacy of the models was verified using analysis of variance at a 95% confidence level. Each response was optimized individually, and the multiple responses were optimized simultaneously using the desirability function approach. The Monte Carlo simulation method was applied to tolerance analysis. All milling tests were conducted at dry conditions; the chamfer width and the chamfer angle varied between 0.1 and 0.3 mm, and 10 and 30°, respectively. Optimal chamfer geometry for minimizing chipping and rake wear was small chamfer width and chamfer angle. The flank wear reached the minimum value for the tool with 0.18 mm chamfer width and 10° chamfer angle. The obtained composite model predicted good edge strength and minimum overall wear when the chamfer was 0.1 mm wide at a 10° angle. Thermal cracks were observed on the tools. They were small on the edges with the finest and least negative chamfer but were more significant on the more negative and greater chamfer. A great chamfer width and chamfer angle also resulted in insufficient chip evacuation. The results show how the edge geometry affects the tool’s reliability and wear and may help manufacturers minimize tool cost and downtime.

Titanium alloys are widely used in the manufacture of aircraft and aeroengine components. However, tool wear is a serious concern in milling titanium alloys, which are known as hard-to-cut materials. Trochoidal milling is a promising technology for the high-efficiency machining of hard-to-cut materials. Aiming to realize green machining titanium alloy, this paper investigates the effects of undeformed chip thickness on tool wear and chip morphology in the dry trochoidal milling of titanium alloy Ti–6Al–4V. A tool wear model related to the radial depth of cut based on the volume of material removed (VMR) is established for trochoidal milling, and optimized cutting parameters in terms of cutting speed and axial depth of cut are selected to improve machining efficiency through reduced tool wear. The investigation enables the environmentally clean rough machining of Ti–6Al–4V.

The measure mode of wear is proposed for ball-end milling cutter and tool wear model is built. Tool wear model coeffi-cients are decided to use tool wear data measured by mapping mode in ball-end milling cutter. Various experimental works have been performed to verify the validity of the proposed tool wear model. It is shown that the proposed model is capable of accurate prediction tool wear of ball-end milling cutter. At the same time, the algorithm of off-line simulation error compensation is proposed for wear-induced error of ball-end milling cutter. The algorithm predicts the machining error for one tool move using the proposed tool wear model in the NC mill machining and acquires one tool move of over tolerance. The NC programs are modified prior to practical machining operations for one tool move of over tolerance. The error compensation experimental results verify the validity of the proposed algorithm of off-line simulation error compensation and show that the error compensation algorithm is satisfying.