Cutting force measurement is of great importance in machining processes. Hence, various methods of measuring the cutting force have been proposed by many researchers. In this work, a novel integrated ...rotating dynamometer based on fiber Bragg grating (FBG) was designed, constructed, and tested to measure four-component cutting force. The dynamometer consists of FBGs that are pasted on the newly designed elastic structure which is then mounted on the rotating spindle. The elastic structure is designed as two mutual-perpendicular semi-octagonal rings. The signals of the FBGs are transmitted to FBG interrogator via fiber optic rotary joints and optical fiber, and the wavelength values are displayed on a computer. In order to determine the static and dynamic characteristics, many tests have been done. The results show that it is suitable for measuring cutting force.
The titanium alloy Ti6Al4V has several applications and is considered as a difficult-to-machine material. This study evaluates some tribological interactions in finish turning of Ti6Al4V titanium ...alloy, including the tool wear, and coefficient of friction in tool-chip interface. Subsequently, the machinability of Ti6Al4V titanium alloy has been investigated, considering the chip geometry, chip thickness ratio, as well as changes in the total cutting force components. The turning process was carried out over a wide range of the feed rate (f) and depths of cut (ap) using hard carbide, GC1115 grade, inserts with double-layer PVD coating. The finish turning tests were conducted under the dry, flood, and MQL (minimum quantity lubrication) cutting conditions. It was found that favorable chip shapes (arc loose) were obtained in the range ap = 0.9–1.2 mm and f = 0.25–0.4 mm/rev. Increasing the f clearly affects the formation of a serrated chip. In addition, the intensity of tool wear, as well as the variations of cutting forces and chip thickness ratios were strongly affected by the applied cooling/lubricating condition. Compared to wet conditions, the Kh values decrease by ∼22 % with dry machining and by ∼12 % with MQL. Compared to wet conditions, for dry machining the cutting forces decrease to 70 % and with MQL to 8 %. The cumulative wear rate under dry machining increases by 18 % compared to wet conditions, and with MQL reduces by 19 %.
When integrating a dynamometer into a machining system, it is necessary to identify the dynamic relationship between the effective input forces and the measured output signals (i.e., its ...transmissibility) through dedicated experimental modal analysis. Subsequently, a filter can be derived and applied to reconstruct the effective input forces from the measured signals. Unfortunately this identification phase can be complex, posing challenges to the device’s applicability in both laboratory and industrial conditions. Here this challenge is addressed by introducing a novel dynamometer concept based on both load cells and accelerometers, along with a Universal Inverse Filter. Notably, this filter is independent of the dynamic behavior of the mechanical system where the device is installed. A single calibration suffices, ideally conducted by the device manufacturer or by an expert, allowing the dynamometer’s integration by a non-expert user into any machining system without the need for repeating the identification phase and the filter generation. Furthermore, this new concept offers another significant advantage: it attenuates all inertial disturbances affecting the measured signals, including those arising from the cutting process and those originating from exogenous sources such as spindle rotation, linear axes’ movements, and other vibrations propagating through the machine tool structure. To illustrate, a simplified model is introduced initially, followed by an overview of the novel dynamometer design, innovative identification phase, and filter construction algorithm. The outstanding performance of the novel (non-parametric) Universal Inverse Filter – about 5 kHz of usable frequency bandwidth along direct directions and 4.5 kHz along cross dir. – was experimentally assessed through modal analysis and actual cutting tests, compared against state of the art filters. The efficacy of the new filter, which is even simpler than its predecessors, was successfully demonstrated for both commercial and taylor-made dynamometers, thus showing its great versatility.
Display omitted
•Universal dynamometer is developed for accurate cutting force estimation in milling.•The new method relies on the combination of load cells plus accelerometers signals.•Only a single preliminary calibration by endogenous and exogenous training required.•Successfully cross-validated on another machine tool without further identification.•Outstanding capability of cross-talk and exogenous noise attenuation.
•Chipping condition with the progressive wear of a coated micro-mill is established.•Wear proceeds sequentially through abrasion, delamination, adhesion, and chipping.•Face wear reaches up to 23 μm ...in dry cutting, and can be reduced by 37% with MQL.•Usage of higher feed per flute (2 – 3 times of fresh edge radius) is recommended.•MQL increases the ploughing-stresses making the tool-tip more prone to chipping.
Micro-milling tools suffer high wear rate and early edge-chipping owing to their small sizes. Mechanism of various wear modes of a coated tool with the progression of micro-milling along with the conditions for their onset was not systematically explored in literature. To understand the edge-chipping scenario with the progression of wear, a comprehensive tribological analysis is presented in this article considering the cumulative effects of process mechanics, material deformation mechanism, tool geometry, lubrication, and process parameters during micro-milling of Ti-6Al-4V using 500 μm TiAlN-coated WC/6Co end-mills. The apparent friction coefficient at the chip-tool-workpiece interface remains very high, in the range of 0.97 – 0.84 in dry micro-milling that reduces to 0.60 – 0.50 under sustainable minimum quantity lubrication (MQL). When a fresh tool is engaged, it undergoes rapid wear for initial 15 mm length of cut. Thereafter, the tool undergoes gradual non-adhesive wear for another 40 – 70 mm cut. As the edge radius increases with machining time, the corresponding minimum uncut chip thickness (hmin) also increases proportionally. When hmin exceeds 12% (for MQL) or 34% (for dry) of the set feed per flute, strong adhesion occurs at the cutting edge, and the process is dominated by the non-cutting passes. Normal stresses within the ploughing-dominant region also remain reasonably high (10 – 18 GPa). Initially the coating, and thereafter the adhered layer, helps sustaining such high normal stresses. Once the adhered layer dislodges, the exposed substrate fails to sustain high stresses leading to edge-chipping. As compared to dry micro-milling, application of MQL helps decreasing abrasion rate, assisting in chip-evacuation, discouraging adhesion, and extending the tool-life; however, the same unfavourably increases the intensity of stresses within the ploughing-dominant region making the tool-tip more vulnerable to chipping.
Display omitted
This paper presents the development of a numerical model for predicting and studying the effects of tool nose geometries and its interactions with cutting parameters during orthogonal cutting of AISI ...1045 steel. The process performance characteristics studied were cutting temperature, effective stress, cutting forces and tool wear. The cutting simulations were done using the commercial DEFORM-2D
V 11.3 software, based on the finite element method (FEM). The cutting tool used had a round nose with various nose radii (0.01-0.9 mm), while the machining parameters tested were the feed rate (0.1-0.3 mm/rev), the cutting speed (100-500 m/min) and the rake angle (-5° to +10°). The interactions between the tool nose radius and the cutting parameters (speed, feed) were found to affect mostly the cutting stress and, slightly, the tool wear rate. These interactions did not much influence the cutting temperature, that was found to be high when the tool nose radius and/or the cutting speed were high. The maximum temperature was found to occur at the middle of the tool-chip contact length and at the interaction of nose radius and flank face of the tool. Except for some fluctuations, there was no significant difference in tool wear rate between small and large nose radius scales.
Tungsten is extensively used as a plasma facing material in fusion energy reactors. A finite element model was created to simulate the machining of tungsten for the first time by estimating the ...cutting forces and observing the impact of the variation in tool rake angle. The model was validated through machining experiments involving a specially designed single flute fly cutter which indicated errors of 6% – 34%, depending on the rake angle. This investigation is the first step in understanding the impact of cutting parameters on machining of tungsten. However, the model is affected by the unpredictable impact of tungsten's deformation behaviour and especially the effects of its brittle nature and low fracture toughness.
•Exp. (experimental) and num. (numerical) cutting forces were obtained by the exp. studies and FEM analysis.•The best convergence between exp. and num. cutting forces was provided.•Num. cutting ...temperatures were predicted by ANN within very low error interval.•Exp. cutting temperatures were obtained using exp. cutting forces.•Exp. temperature results were quite satisfactory.
In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN+Al2O3+TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.
Micro-milling through mechanical chip removal process has attracted much attention among researchers due to its flexibility and productivity that allow an extensive application for manufacturing ...several types of micro-components for the modern-day world. Its potential is continuously growing as the market demands continuous innovation in the manufacturing of high precision products with progressively smaller dimensions. Its global research and available literature increased very fast in recent years. Relevant topics like size effect, burr formation, surface quality, cutting forces, tool wear, vibrations, and process optimization as highlighted by a systematic bibliometric study during the period 2000–2019 must be properly addressed. The review work on such a scale was not attempted earlier by considering many parameters at a time. Hence, this study may provide a current view and future prospects of mainstream research on micro-milling worldwide.
Martensitic steels are widely used in many areas such as automotive, mining, and agriculture mostly thanks to their thermal loading ability property. On the other hand, these special steels exhibit ...extreme tool wear tendency and low surface quality which can be associated with abrasive resistance. This situation makes this steel hard-to-cut and requires further investigation with several approaches. Sustainable machining environments are highly effective as modern strategies to improve the machinability index. Also, machine learning models have pivotal role on decreasing the total consumption in the way of lean manufacturing. In the light of above-mentioned information, this work focuses on the machining performances and optimization of dry, flood, and MQL conditions during the milling of Hardox 400 martensitic stainless steel. A novel approach was applied with using several cutting environments and machine learning models to enhance machinability of Hardox which is an industrially important material. Results were analyzed with different machine learning models using heat map and decision trees. Seemingly, cutting fluid assistance in the milling of Hardox steel is critical where flood and MQL provided a considerable effect on the tool wear for reducing it under some level. Also, this technology was found useful in determining the best conditions of machinability in terms of surface roughness, chip morphology, energy consumption, and cutting temperatures. Machine learning models provided hopeful results in analyzing the correlations between parameters used in the model. In machine learning, the heat map being close to 1 and the MSE and MAE values being close to 0 indicated that the model was suitable. This study is expected to observe the contributions of different types of cutting environments to the machinability criteria during milling of industrially important materials.
PDF
