High-speed Dry Milling Research Articles

Investigation on the Cutting Performance of High‐Speed Dry Milling for 30CrMnSiNi2A Steel

High‐speed dry milling (HSDM) technology is one of the most attractive solutions for improving the cutting performance of difficult‐to‐machine materials. However, the study of HSDM performance for 30CrMnSiNi2A steel has not been reported. In this article, HSDM for 30CrMnSiNi2A steel is investigated using TiN‐coated and AlTiN‐coated tools, focusing on cutting force, vibration, surface roughness, residual stress, and tool wear. The effects of cutting parameters on cutting force and vibration are remarkably similar. At a spindle speed of 7500 r min−1, both cutting force and vibration are minimized. TiN‐coated and AlTiN‐coated tools exhibit distinct performance differences. But, HSDM of 30CrMnSiNi2A steel using TiN‐coated and AlTiN‐coated tools can achieve low surface roughness. The depth of cut significantly affects surface roughness, which is lowest at the depth of cut (0.8 mm). Most of the workpiece surface shows residual compressive stress. The main wear mechanisms for both coated tools are abrasive wear, adhesive wear, and oxidative wear. Furthermore, the AlTiN‐coated tool is more wear resistant on the flank face compared to the TiN‐coated tool. Chipping due to crater wear is the leading cause of tool failure for the AlTiN‐coated tool. Upon various comparisons, the AlTiN‐coated tool is more suitable for HSDM of 30CrMnSiNi2A steel.

Modeling of workpiece temperature suppression in high-speed dry milling of UD-CF/PEEK considering heat partition and jet heat transfer

Modeling of workpiece temperature suppression in high-speed dry milling of UD-CF/PEEK considering heat partition and jet heat transfer

Wear behaviors of PcBN milling insert in high-speed dry milling nodular cast iron

Wear behaviors of PcBN milling insert in high-speed dry milling nodular cast iron

Sustainable high-speed milling enhancement of GnP-reinforced titanium nanocomposites under dry environment

No studies have been done on using graphene nanostructure reinforcement material to improve titanium alloy's dry high-speed machining performance, which is crucial for green manufacturing. This study seeks to understand the impact of graphene on improving the high-speed machining of Ti6Al4V (Ti64) nanocomposites. High-density Ti64-based nanocomposite samples with varying amounts of GnPs (0 wt%, 0.6 wt%, 1.2 wt%, and 2.4 wt%) were fabricated using ball milling and HFIHS sustainable technologies. Subsequently, a detailed investigation was conducted to examine the impact of the GnP contents on high-speed milling performance, namely, cutting force components, surface quality, and machined surface and subsurface microhardness. The variable milling parameters include the cutting speed and feed rate. The results showed that the inclusion of GnPs significantly affected the dry high-speed milling of the nanocomposites. The nanocomposites containing 1.2 wt% and 2.4 wt% of GnP-Ti64 exhibited lower cutting forces than the base-Ti64 without GnP (0 wt% Gnp). At a 100 m/min cutting speed, the cutting and feed forces decreased by 35% and 44%, respectively, compared to the base-Ti64 specimens. This is attributed to lower machining friction due to the presence of graphene and TiC (formed due to the reaction of Ti and GnP particles during sintering) at grain boundaries, facilitating material removal and reducing cutting force. Although the nanocomposites had a higher microhardness, they all showed improved surface quality in comparison to the base-Ti64 specimens. For example, at a feed rate of 270 mm/min the nanocomposites contain 0.6 wt%, 1.2 wt%, and 2.4 wt.% GnPs showed roughness reductions of 6%, 27%, and 38%, respectively, compared to the base-Ti64. Regarding surface and sub-surface hardness after machining, the 2.4 wt.% GnP samples showed superior performance in terms of minimal post-milling hardness variations compared to the base hardness of the material. All GnP-Ti64 milled specimens showed considerably improved surface morphology compared to the base-Ti64 machined specimens. Overall, the nanocomposites containing 1.2 wt% and 2.4 wt% GnPs are strong contenders for reducing cutting forces, enhancing surface roughness, and lowering the fluctuations in microhardness, leading to green manufacturing.

Investigation on high-speed dry milling stability of high strength steel by compound Simpson prediction method

Investigation on high-speed dry milling stability of high strength steel by compound Simpson prediction method

Surface quality investigation in high-speed dry milling of Ti-6Al-4V by using 2D ultrasonic-vibration-assisted milling platform

Surface quality investigation in high-speed dry milling of Ti-6Al-4V by using 2D ultrasonic-vibration-assisted milling platform

Thermal-field analytical modeling of machined surface layer in high-speed-dry milling UD-CF/PEEK considering thermal anisotropy and nonlinear thermal conductivity

The high-speed-dry (HSD) machining is now recognized as a strong potential dry-cutting technique to tackle the rapidly growing productivity demand of carbon-fiber-reinforced-polyetheretherketone (CF/PEEK). The thermal-field is an essential breakthrough for eliminating the severe thermal effect of cutting temperature beyond CF/PEEK glass transition temperature Tg. However, the anisotropy and temperature-sensitive thermal conductivity of CF/PEEK lead to great challenges in thermal analytical modeling. Addressing this issue, a novel thermal-field analytical model that incorporates fiber orientation-dominated thermal anisotropy is developed to investigate the thermal field of machined surface layer for unidirectional (UD) CF/PEEK HSD milling, where the nonlinear thermal conductivity of CF/PEEK are imported into this model. With experiment verification, the thermal-field model can forecast the spatio-temporal distribution of workpiece temperature. Then, the thermal mechanisms of fiber orientations and milling parameters are clarified to restrict workpiece temperature within the Tg for limited thermal damage and reveal the feasibility essence of CF/PEEK HSD milling.

Multi-objective optimization of surface morphology using fractal and multi-fractal analysis for dry milling of AISI 4340

Machining of difficult-to-cut metals is typically associated with inconsistent machining quality, high machining costs, and significant environmental pollution. This work proposes a novel method that utilizes fractal and multi-fractal features of the machined surface morphology to evaluate surface quality in high-speed dry milling of 4340 steel. The results demonstrate that the fractal dimension can be used as an evaluation index for machined surface defects. It accurately characterizes the contour information of machined surface defects, including voids, tears, and material side flow. A smaller fractal dimension indicates fewer surface defects and represents a higher-quality machined surface. However, the surface defects cannot be described using the traditional evaluation method based solely on surface roughness (Ra). Moreover, multi-objective optimization of high-speed milling of 4340 steel is achieved by using the combined indices Ra and fractal features as responses. The Ra generated using the optimized parameters can reach 97 nm, which represents a reduction of 4.9 % compared to the non-optimization state. And typical defects on machined surfaces virtually disappear. Meanwhile, the machining cost has been reduced by 2.52 % and the machining efficiency has increased by 31.45 %. The results demonstrate that the proposed surface quality evaluation method is possible for practical engineering applications to achieve high-quality, high-efficiency and low-cost high-speed dry milling of high-strength steels.

Prediction and optimization of surface roughness in high-speed dry milling of 30CrMnSiNiA using GPR and MOHHO algorithm

Prediction and optimization of surface roughness in high-speed dry milling of 30CrMnSiNiA using GPR and MOHHO algorithm

Revealing dynamic-mechanical properties of precipitates in a nanostructured thin film using micromechanical spectroscopy

Abstract Nanostructured materials with their remarkable properties are key enablers in many modern applications. For example, industrial dry-milling processes would not be as widely spread without the use of hard, wear-resistant metal nitride coatings to protect the cutting tools. However, improving these nanostructured thin films with regard to dynamical properties is demanding as probing respective parameters of (sub-)micron layers without any substrate influence is still challenging. To extend the scientific toolbox for such spatially confined systems, a novel methodological approach based on resonance peak measurements of a cantilever-transducer system termed micromechanical spectroscopy (µMS) is developed and applied to a Al0.8Cr0.2N model system. The mainly wurtzite type supersaturated Al0.8Cr0.2N system showed precipitation of cubic CrN at grain boundaries and local Cr variations upon annealing at 1050°C. This was accompanied by an increase in the previously unknown damping capability of 63 percent and an increase in Young’s modulus by 36 percent. Impact statement There is a wide variety of applications for nano- to micrometer-sized thin films in today’s engineering technology, from thermal barrier- and wear-resistant coatings in turbines and bearings, over diffusion barriers and heatsinks in microelectronic devices, to optically active layers in lasers or mirrors. The mechanical properties of such thin films are oftentimes governed by their thermal history, leading to either intentional or undesired changes in the microstructure (e.g., the formation of precipitates). While the investigation of such features is usually constricted to static analysis using high-resolution techniques, such as transmission electron microscopy, understanding their impact on dynamic properties of the film remains a challenge. However, these are highly relevant in many engineering applications where cyclic behavior is common, such as high-speed dry milling. In the present work, we investigate the change in mechanical damping capability upon annealing of a 6-µm thin AlCrN film, commonly used in demanding dry-milling applications, using micromechanical spectroscopy (µMS) of cantilever-shaped specimens. After a carefully adjusted heat treatment, the film exhibits the formation of cubic CrN precipitates in an otherwise wurtzite AlCrN matrix, which leads to a previously unknown beneficial increase in damping capability of the film. Graphical abstract

Impact effect-based dynamics force prediction model of high-speed dry milling UD-CFRP considering size effect

The impact and size effect are generally neglected in force prediction model of cutting Carbon Fiber Reinforced Polymer (CFRP). Since carbon fibers are frequently cut in and out, the main cutting mechanisms of CFRP are mixed by impact, shear and other mechanisms, which are difficult to be clarified. Thus, we establish an impact effect-based cutting force prediction model of CFRP considering size effect. In the model, cutting regions are effected by chipping, pressing, impacting and bouncing, respectively. The cutting force is calculated based on the representative volume element (RVE) and minimum potential energy principle (MPEP). After chip breakage, the free-damped vibration force prediction model is established. The dynamics force prediction model is proofed with high prediction accuracy. Besides, the influences of cutting parameters on impact, cutting and vibration are analyzed by nonlinear regression analysis. It demonstrates that the impact and size effect are of great significance in revealing the cutting mechanisms of CFRP.

Cutting performance and surface quality of Ti-6Al-4V by longitudinal ultrasonic vibration-assisted high-speed dry milling with coated carbide tools

Longitudinal ultrasonic vibration-assisted high-speed dry milling (LUVAHSDM) is adopted to improve the cutting performance and surface quality of Ti-6Al-4V (TC4). Multifaceted comparisons are presented in terms of milling force, milling temperature, tool wear, 3D surface roughness, and residual stress, utilizing AlCrN, AlCrSiN, AlTiN, and AlTiSiN-coated tools. The results show that the maximum milling forces in the feed and longitudinal directions both decreased at first and then increased with the increasing of Al element contents, during which the single edge and average flank wear increased. Therefore, the surface roughness of TC4 becomes more uniform using AlTiN-coated tools, and the residual stress is significantly reduced to 98.57 MPa. Combining the results of milling force, milling temperature, tool wear, 3D surface roughness and residual stress data, it is found that AITiN-coated tools achieve excellent cutting performance and surface quality. The excellent machining performance of coated carbide tools demonstrates the feasibility and effectiveness of applying LUVAHSDM methods.

Investigation on White Layer Formation in Dry High-Speed Milling of Nickel-Based Superalloy GH4169

To investigate the formation mechanism of the white layer on the machined surface during high-speed milling of nickel-based superalloy GH4169, several cutting parameters were selected for milling experiments. Energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and electron backscattered diffraction (EBSD) were employed to characterize element distribution, phase transformation, and microstructure changes in the machined surface of the superalloy and then reveal the formation mechanism of the white layer on the machined surface. The results show that the white layer appears on the machined surface of GH4169, which is dense and has no obvious structural features. The total amount of elements in the white layer remains unchanged, but the distribution of elements such as C, N, O, Fe, and Ni changes due to phase change. The formation mechanism of the white layer is due to the dynamic recovery and dynamic recrystallization caused by the heat–force coupling effect, which leads to the grain refinement of the material and thus forms the white layer. This investigation can provide theoretical support to improve the service life of the parts in actual machining.

Research on feasible region of specific cutting energy and surface roughness in high-speed dry milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts

Research on feasible region of specific cutting energy and surface roughness in high-speed dry milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts

Surface roughness prediction model in high-speed dry milling CFRP considering carbon fiber distribution

The surface roughness of carbon fiber-reinforced polymer (CFRP) components is extremely important because of the increasing demand for higher performance, reliability, and longer lifetime in aerospace and other manufacturing industries. However, the cutting mechanisms of CFRP are still unclear, which limits its formation mechanism prediction for surface roughness. Thus far, this study presents three action mechanisms in the CFRP machining process: accumulation bouncing on the matrix, impact effect on carbon fibers, and workpiece self-action. The workpiece self-action was reflected and calculated using the light rope model, which is new in CFRP machining. Furthermore, the formation mechanisms of surface roughness were first elucidated in the high-speed dry (HSD) milling of CFRP. An accurate surface roughness prediction model was theoretically formulated considering the kinematics, dynamics, and carbon fiber distribution. Surface roughness was expressed as the three-dimensional arithmetic mean height. The surface roughness prediction model was established and confirmed to have a high prediction accuracy of 90.05%, demonstrating that the distribution of carbon fibers was the main influencing factor of the surface roughness. Moreover, nonlinear regression analysis was used to clarify the effects of cutting parameters on the surface roughness, impact effect, and relationship between the surface roughness and impact effect. The study verified the feasibility of HSD milling CFRP, and it also provided guidance for breaking the low-speed machining limits by improving the machining process.

Optimization of High-speed Dry Milling Process Parameters Based on Improved ELM and Genetic Algorithm

High-speed dry grinding has the characteristics of high processing efficiency and clean environment. The high-speed dry grinding method meets the requirements of the green and efficient development of the national manufacturing industry. However, inappropriate cutting parameters seriously affect the surface quality of the workpiece and cause the workpiece to be scrapped. Therefore, this paper proposed an optimization method based on the combination of the improved extreme learning machine neural network (ELM) for high-speed dry milling surface roughness prediction model and genetic algorithm (GA). The Taguchi orthogonal experiment results show that the surface roughness of high-speed dry milling can be accurately predicted by the improved ELM and thereafter the optimal cutting parameter combination can be determined by GA.

Surface integrity optimization of high speed dry milling UD-CF/PEEK based on specific cutting energy distribution mechanisms effected by impact and size effect

Carbon fiber-reinforced polymer (CFRP) composites have been widely used in the aerospace industry due to their excellent mechanical properties. Cutting mechanisms of machining CFRP and its effect on machined surface integrity are still unclear due to inhomogeneity and anisotropy. To this end, this paper studied the cutting mechanisms of CFRP by establishing an impact-based Specific Cutting Energy (SCE) distribution prediction model of high speed dry (HSD) milling CFRP which is be an effective and eco-friendly cutting method. SCE is divided into five sub-SCEs affected by shearing, pressing, impacting, bouncing and delamination, respectively. Among them, sub-SCEs generated by pressing and impacting are influenced by carbon fiber distribution due to size effect and material properties, carbon fiber distribution model was introduced into the SCE prediction model. The proposed model was verified with maximum relative error 7.6%, demonstrating that impact and size effect are of great significance in revealing the cutting mechanism of CFRP. To clarify the effect of SCE distribution on surface integrity, three-dimensional (3D) arithmetic mean height and 3D fractal dimension were used to quantitative characterization of the surface integrity and sub-SCEs are diagnosed for obtaining the way of sub-SCE distribution effecting surface integrity by correlation analysis. According to SCE diagnosis, the milling parameters are optimized by removing sub-SCEs unrelated to surface integrity, founding that HSD milling can reduce machining defects.

Surface Finish Comparison of Dry and Coolant Fluid High-Speed Milling of JIS SDK61 Mould Steel

This paper investigates the influence of dry high-speed milling on the surface quality of JIS SKD61 hard steel, compared to the conventional coolant fluid method. This research was conducted in a Super MC 500 high-speed CNC milling machine with a Hitachi coated carbide 20mm in diameter. High-speed cutting parameters such as cutting speed V, cutting depth t, and spindle speed S were considered as variants. The experiment was designed based on Taguchi's L9. Surface quality, including Ra and Rq, was measured using the Mitutoyo Surftest SV-210. A mathematical regression model was found for the average values of surface roughness through regression analysis for dry and coolant fluid conditions. The chosen high-speed milling parameters and the respective Ra and Rq values were obtained by ANOVA. The grey relation scores for wet and dry milling surface quality for cut depth, feed rate, and cutting speed were 0.7527, 0.7869, 0.6302, and 0.8167, 0.7199, 0.6040, respectively. The results showed that the feed rate had the greatest influence on the surface quality during the high-speed coolant milling of hardened steel, while the depth of the cut had the greatest influence on the surface quality during the high-speed dry milling process.

A force model of high-speed dry milling CF/PEEK considering fiber distribution characteristics

Carbon fiber reinforced polyetheretherketone (CF/PEEK) is an emerging material that is widely used in the automotive and aviation industry due to its high shock resistance, thermostability, and recyclability. However, its dry-machining requirement with an unclear dynamic material removal process limits the machining efficiency and surface quality. To this end, we firstly established a two-dimensional joint probability distribution model of cutting carbon fibers based on the microstructure of CF/PEEK. Compared with previous brittle models, the plastic model is verified to be more consistent with experimental results. A high-speed dry (HSD) milling force model considering carbon fiber distribution is then developed and is proofed with a high prediction accuracy of about 92.6%. With the proposed model, the milling force can be separated into forces of cutting carbon fibers and PEEK matrix, by which the influences of high-speed dry milling process on carbon fibers and PEEK matrix can be analyzed. The results show that carbon fiber distribution exhibits a significant impact on milling force and is more easily affected by milling parameters compared with the PEEK matrix. It is also verified feasible to HSD milling CF/PEEK, and the work gives the guidance of breaking the low-speed machining limits by improving the machining process.

Process optimization of high-speed dry milling UD-CF/PEEK laminates using GA-BP neural network

High-performance carbon fiber-reinforced polyetheretherketone (CF/PEEK) is widely used in aerospace and premium-end medical fields due to its high strength-weight ratio, shock resistance, and reusability. However, its dry machining requirement is a significant limit to improving machining efficiency and machining quality using a traditional process. Addressing this issue, the high-speed dry (HSD) machining technique is imported in this paper. Multi-level mixed orthogonal experiments of dry milling unidirectional (UD) CF/PEEK laminates with the fiber orientation of 0° and 90° are designed. Aiming at quantitative characterizing surface quality, three-dimensional (3D) surface roughness Sq and 3D fractal dimension Ds are used to present surface roughness and surface defects, respectively. A characterization system for surface defects generated in milling CRF/PEEK is proposed. A prediction model of surface quality considering fiber orientation, cutting speed, feed per tooth, and cutting width is then established using the genetic algorithm optimized BP (GA-BP) neural network. The prediction results show that the model is of acceptable generalization capability with a prediction accuracy of over 90.39%. Based on the analysis of surface qualities and cutting temperatures, the HSD machining technique is verified to be feasible in milling UD-CF/PEEK, and the recommended cutting speed in the HSD milling boundary is 1500–1600 m/min. The 3D fractal dimension is verified feasible to evaluate the size of complex surface defects of the machined UD-CF/PEEK. It has a non-rigid negative correlation with Sq in general. Besides, cutting speed and fiber orientation are the key factors affecting the machined surface microstructural characteristics. The present study gives technical references for improving surface quality in HSD milling CF/PEEK.