CNC Milling Research Articles

Five-axis parallel mechanism system (PMS) CNC partial link control system based on modified inverse kinematic of 6-DOF UPS parallel manipulator

This paper presents a control system algorithm for a five-axis parallel mechanism system (PMS) CNC milling machine based on a 6-DOF Stewart platform parallel manipulator with a universal-prismatic-spherical (UPS) configuration. The control system reads the G-Code commands as standard CNC machine language, then extract data points and interpolates them to generate the robot trajectory patterns as motion references. Then, the control system uses the modified inverse kinematic equation to determine the length of each link to move the end effector to track the trajectory patterns from the previous G-code extraction process. The inverse kinematic equation is modified especially for the five-axis PMS CNC milling machine by including machine-offset and tools-offset parameters so it will be easier for the control system to implement the kinematic equation. As expected, the system simulation results successfully followed the G-Code program moving commands. The average error of the length control system is 0,1 mm, while the average error of the length change rate control system is 1,8 mm/s. The maximum error is 26.9 mm was caused by the system's inability to follow the motion profile in transient. It can be concluded that 6-DOF Stewart platform parallel structures,which provide better performance than serial structures, can be implemented as a new concept for the motion mechanism of five-axis CNC milling machines. The five-axis PMS CNC milling machine also promises better performance than conventional five-axis gantry structures CNC.

On-line surface roughness classification for multiple CNC milling conditions based on transfer learning and neural network

On-line surface roughness classification for multiple CNC milling conditions based on transfer learning and neural network

Coupling of multilayer CAD-CAM prosthetic components: Computing the user interaction influence on the adhesion interface geometry

The fracture resistance of multilayer zirconia crowns has recently been proven to be improved by using lithium millable disilicate glass–ceramic blocks (D’Addazio in Materials, 2020). Accordingly, the framework and the ceramic coating are designed and milled using a CAD-CAM technology and the two separated prosthetic components are then manually assembled by the dental technician and glued with the fusion of a glass–ceramic material. It is essential, during the CAD phase, to design a gap between the framework and the decorative veneer that will later be filled by the fused ceramic.Since the act of gluing the two parts is manually performed by the dental technician, we aim at investigating the operator influence on the final gap with respect to the designed gap. For this purpose, an original geometrical investigation method was developed to enable the 3D digital analysis of the whole fusion interface. During the CAD design stage, two technicians input a different setting for the gap between the two components. The framework and veneering structure were designed, the milled components were produced, and the zirconia framework was sintered, then the two CAD-on prosthetic components were scanned before and after their fusion/crystallization to analyze the physical internal gap. The results show that manual assembly cancels out any effect of the precision settings adopted during CAD-CAM design of the components, as well as any benefit expected from machining on a CNC milling machine, thus requiring, as a last step, manually retouching the prosthesis to correctly fit in the mouth.

Prediction using multi-objective slime mould algorithm optimized support vector regression model

Different from the traditional neural network models based on empirical risk minimization principle, support vector regression (SVR) minimizes the upper limit of generalization error by the structural risk minimization principle. SVR is suitable for solving nonlinear, small-sample, high-dimensional modelling, and pattern recognition problems. However, the selection of hyperparameters of SVR can significantly affect the prediction accuracy and computational time. This paper proposes a method to optimize the SVR hyperparameters through an intelligent optimization algorithm, specifically multi-objective slime mould algorithm (MOSMA). MOSMA is a new multi-objective optimization algorithm with strong global search ability and fast convergence. The principle and calculation process of MOSMA are first described in detail. The analytical method for transforming the SVR hyperparameter optimization problem into a multi-objective problem is then presented. The process of optimizing two SVR hyperparameters (the penalty coefficient and radial basis function kernel parameter) based on MOSMA is described in steps. Meanwhile, the procedures of algorithm parameter setting, fitness function design, and population update are given. To investigate the characteristics and performance of MOSMA-SVR, two datasets are used to predict the vibration trend of the spindle of CNC milling machine in one and the maximum bending normal stress in the cutting process in the other. The performance of MOSMA-SVR is evaluated by multiple statistical indexes and compared with seven other prediction models (GA-SVR, PSO-SVR, ABC-SVR, GWO-SVR, SSA-SVR, SMA-SVR and MOWOA-SVR).

XRD analysis and surface roughness of AL2024 alloy produced through P/M route and machined by using CNC milling machine

The purpose of this analysis is to examine the surface roughness during machining of aluminum 2024 prepared through the powder metallurgical (P/M) route. Aluminium 2024 alloy is prepared with different compositions, such as pure Al, 1.5 W% of Mg, and 2–6% of Cu powders. Powders are blended with the ball milling machine according to the composition required, and green compacts are prepared in a square-shaped die (25*25mm) by applying a uniaxial load of 250 Mpa ±50. The sintering process was performed at a temperature of 594 °C for 60 minutes, and the heating profile consisted of a 10-minute isothermal keep at 594 °C followed by cooling in Furness. It presents and addresses compaction characteristics, green density, dimensional changes during sintering, sintering density, mechanical properties, and microstructures. Also determined were the surface roughness and MRR during machining with a CNC milling machine at different depths of cut.

The effects of different types and ratios of reinforcement, and machining processes on the machinability of Al2024 alloy nanocomposites

In the presented study, aluminum metal matrix nanocomposites reinforced with SiC and B4C, with different reinforcement ratios (0–4 wt%), have been prepared using the powder metallurgy method. The main objective of this study is to investigate the effect of different reinforcements and reinforcement amounts on the machinability of metal matrix nanocomposites using different machining processes (WEDM and CNC milling). The surface morphologies of the samples were investigated before and after machining using scanning electron microscopy and chemical analysis of the samples was obtained using EDX and X-ray diffraction. The surface hardness, roughness, and metal removal rate (MRR) before and after machining were examined to investigate the effect of the machining process on the surfaces of the samples. According to the results of the study, the hardness value of the nanocomposite containing 4% B4C by weight (195.6 HB) increased by approximately 90% compared to the hardness value of the pure Al2024 alloy (101.6 HB). After both machining processes, the hardness values of all nanocomposites decreased. In addition, the tensile strength values of the nanocomposites containing 2% B4C and SiC by weight are 392.8 MPa and 377.7 MPa, respectively, while the tensile strength value of the pure Al2024 alloy is 201.7 MPa. After both machining processes, the hardness values of all nanocomposite samples decreased. While the MRR values decreased with the increase in the reinforcement amount of the nanocomposites after machining, the SR values decreased.

Multi-criteria decision making in CBN grinding tool steel using TOPSIS method

This paper introduces the results of a multi-criteria decision making (MCDM) study when CBN grinding SKD11 tool steel in a CNC milling machine. In this work, the TOPSIS method was applied to solve the MCDM problem, and the Entropy method was used to find the weight of criteria. Also, two criteria were chosen: surface roughness (SR) and material removal speed (MRS). Furthermore, an experiment with design L18 (61+33) was carried out using the Taguchi method. In the experiment, four input parameters were investigated: the dressing cut depth aed (mm), the wheel speed Rpm (rpm), the feed rate Fe (mm/min), and the wheel diameter d (mm). From the results of this work, the best experimental setup was proposed.

Investigation of conventional and ANN-based feed rate scheduling methods in trochoidal milling with cutting force and acceleration constraints

In CNC milling, the feed rate scheduling is a frequently used method to increase machining quality and efficiency. Among the benefits of feed rate scheduling, this paper focuses on controlling the tool load and optimizing the machining time. Although the advantages of feed rate scheduling are undeniable, some areas remain still to be addressed. In order to control the tool load, geometric methods are often used, which are based on keeping a specific parameter, such as chip thickness or material removal rate (MRR) constant. However, a high level of tool load control can only be provided if cutting force models or experimental-based techniques are used. Besides traditional methods, this paper presents an artificial neural network (ANN)-based feed rate scheduling method to keep the tool load constant, using data gained by preliminary cutting experiments. A case study demonstrates that a significantly higher level of tool load control can be achieved with this method as compared to the geometric models. Besides controlling the tool load, the present feed rate scheduling method also addresses the consideration of acceleration limits which is of great importance for practical uses. The application of feed rate scheduling in trochoidal milling is also discussed in detail in this paper. This area has not received enough attention, as due to the limited fluctuation of cutter engagement, the tool load was considered as well-controlled. However, experiments have shown that in the case of trochoidal milling, the introduction of feed rate scheduling can still further increase the machining efficiency. Using the developed ANN-based feed rate scheduling method, significant progress could be made as compared to conventional technologies in controlling the cutting force and optimizing the machining time. In the present case study, a reduction of 50% in machining time was achievable by adjusting the feed rate without increasing the peak value of cutting force.

Farklı Frez Türlerinin Kullanımında Açığa Çıkan Sıcaklık Değerlerinin Değerlendirilmesi

Aim: In our study, we aimed to measure the amount of released head by recording it with a thermal camera during the osteotomies made utilizing round, fissure and lindemann burs to the synthetic bone blocks to simulate the mandible ramus region which is often preferred when obtaining autogenous bone from the mouth. Material and Methods: The burs in our study were used at rotational speeds of 10000 rpm and 15000 rpm and feed rates of 60 mm/min and 90 mm/min, and each osteotomy was made with a CNC milling machine in order to standardize the applied force. Results: According to the results of our study, the highest temperatures were observed in the fissure bur groups, and the round bur and lindemann bur groups gave similar results. In addition, when the feed rate is increased from 60 mm/min to 90 mm/min in all groups at constant rotational speed, the heat released increases significantly. When the groups are evaluated within themselves; the temperature values observed at 15000 rpm and 60 mm/min feed rate in the groups using round bur were found to be significantly lower than the group observed at 10000 rpm and 60 mm/min feed rates (p=0.028), in fissure bur groups, the temperature values observed at 10000 rpm and 60 mm/min feed rate were significantly lower than the values observed at 15000 rpm and 60 mm/min feed rates (p=0.028). No statistically significant difference was observed between the heat exchange averages of the 10000 rpm and 15000 rpm groups at a Lindemann bur 60 mm/min feed rate (p=0.182). Conclusion: This study has shown that while the generated heat in the bone is thought to increase when the bur speeds are increased, the head generated according to bur designs can decrease and it is necessary to operate according to the characteristic features of the preferred bur.

Influence of Cutting Process on the Flanging Formability of the Cut Edge for DP980 Steel

Flanging is a key process in the forming of automobile parts and is influenced by the cutting process before it. In this paper, a set of self-designed tools is adopted. The flanging height and flanging radius are specified as the indexes to investigate the influence of the cutting process on the flanging formability of DP980 steel. Microstructure, microhardness, and tensile properties are tested to evaluate the formability of cut edges. The results show that the flanging formability is positively correlated with an increase in flanging height and a decrease in flanging radius. The flanging formability of six cut edges can be ranked as: LC-L > LC-H > WEDM-HS > WEDM-LS > CNC milling > WJC. In the LC process, acicular martensite is formed and the microhardness of the cut edge significantly increases, with a heat-affected zone of about 150 μm. More and larger microvoids are found on the edge of WEDM-LS than WEDM-HS, which reduces the formability to a certain extent. Plastic deformation of the edges is the reason for the decrease in the formability of CNC milling. For WJC, the formation of irregular edge damage and the generation of burrs are the main reasons for the unstable performance. This research is helpful in guiding the selection of the cutting process, and it may contribute to a high part formability for DP980 steel.

МОДЕРНИЗАЦИЯ ТЕХНОЛОГИЧЕСКОГО ПРОЦЕССА ИЗГОТОВЛЕНИЯ ТОНКОСТЕННОЙ ЗАТВОРНОЙ РАМЫ ИЗ ЛИСТОШТАМПОВАННОЙ ЗАГОТОВКИ

A comparative analysis of the technological processes of machining a part from a stamped workpiece using universal equipment and a CNC milling machining center is considered. Route technological processes have been developed. A diagram of a special fixture for machining on a CNC milling center Tongtai TMV-1050QII is presented. Comparative analysis of labor input is made.

Design and Development of 3-Axis Benchtop CNC Milling Machine for Educational Purpose

The main factor in improving learning skills is providing students with hands-on laboratory experience, and the small-scale machine can accomplish academic programs requiring students tolearn machining skills. This paper aims to design and develop a 3-axis CNC milling machine with a PC-based open architecture controller in a vertical position open frame structure. Some technical specifications were randomly selected based on the capabilities of similarly sized machines reviewed in previous work. The designed machine consisted of inexpensive off-the-shelf hardware components capable of machining the sample block with high cutting speed and reasonable precision. The accepted percentage error of circular and straightness test readings is below the set requirements. This machine is not intended for series production and precise machining. It can still effectivelyreplace the high cost of commercial CNC machines and be used in any higher education institution offering technical courses.

Effects of cryogenic treatment on the performance of coated tungsten carbide inserts during milling of EN24 steel

Cryogenic treatment is one of the innovative techniques to improve the performance of cutting tools. The present study investigated the effects of cryogenic treatment on the performance of cutting tools, while machining (CNC milling) EN24 steel. The TiCN-coated tungsten carbide inserts (APKT09) were procured and exposed to shallow cryogenic treatment at −80 °C for 6 h and deep cryogenic treatment at −196 °C for 24 h independently. The experiments were designed using Taguchi’s L9 orthogonal array. Milling tests were performed by considering three cutting speeds (1900, 2200, and 2500 rpm), three feed rates (350, 440, and 530 mm/min), and three depths of cut (0.40, 0.65, and 0.90 mm) under dry cutting conditions. The machining performance of cryogenically treated and untreated inserts was evaluated in regard to the flank wear of the inserts and the surface roughness of the machined workpiece. Cryogenically treated inserts exhibited reduced flank wear and surface roughness than untreated inserts. The best flank wear and surface roughness performance were attained with the deep cryogenically treated inserts. Moreover, an improvement in the microhardness of the cryo-treated inserts was observed.

Do Sleeping Shepherds Dream of 3D-Printed Sheep: John Gibson, Oliver Laric, and Digital Neoclassicism

This article considers the relationship between John Gibson’s neoclassical sculpture The Sleeping Shepherd Boy and Oliver Laric’s installations for the 2016 Liverpool Biennial using 3D models and prints of the Shepherd. These bodies of work allow us to think about their similarities in attitude towards imitation, the significance of the “neoclassical” across different historic moments, and the cultures of copying or reproduction. It looks at the reproductive technologies of 3D scanning, printing, CNC milling, and digital remixing alongside historical reproductions such as casts and copies. These offer new potentially disruptive—but not destructive—opportunities within the legacy of neoclassical practices. The intellectual and artistic inheritance of neoclassical sculpture as an imitative practice after Greek and Roman antiquity informs Laric’s sculptural work. I draw on Alexander Nagel and Christopher Wood’s Anachronic Renaissance (2010) and George Kubler’s The Shape of Time (1968) to discuss Laric’s modular, large-scale 3D prints, which point towards issues of replacement, imitation, and wholeness. The open-source 3D models he produces as part of his practice are then used by other artists, including Zachary Eastwood-Bloom in his Divine Principles series, and the author, for making research objects.

Material extrusion-based additive manufacturing of zirconia toughened alumina: Machinability, mechanical properties and biocompatibility

Material extrusion-based additive manufacturing (MEAM) has gained significant attention due to its versatile flexibility for fabricating a broad range of materials. In this study, fabrication of dense zirconia-toughened alumina (ZTA) ceramics with micro/nano powders of excellent mechanical and biological properties was investigated. The ZTA paste rheology was engineered to achieve appropriate rheological behavior. A novel machining scheme on MEAM produced ZTA green parts using CNC milling and high precision micro-milling was studied to investigate their effect on surface integrity and geometric accuracy. The MEAM produced ZTA ceramics show the highest relative density, Vickers hardness, bending strength, and fracture toughness of 99 %, 17.7 GPa, 422.5 MPa, and 6.2 MPa*m1/2, respectively. Biocompatibility of fabricated ZTA ceramics with different size of powders was investigated including cell adhesion, cell toxicity and cell proliferation, showing a good potential in biomedical usage. This framework provides a unique pathway for MEAM of ZTA ceramics to combine design and manufacturing freedom, multifunctionality, stability, and economical simultaneously.

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

In order to guarantee machining quality and production efficiency and as well as to reduce downtime and cost, it is essential to receive information about the cutting tool condition and change it in time if necessary. To this end, tool condition monitoring systems are of great significance. Based on the data fusion technology of multi-sensor integration and the learning ability of deep residual network, a tool wear monitoring system is proposed in this paper. For data acquisition, multiple sensors are used to collect vibration, noise and acoustic emission signals in the machining process. For signal processing, the data fusion method of angular summation of matrices is used, so that the multi-source data are fused into two-dimensional pictures. Then, the feature extraction module of the proposed system uses the pictures as input and takes advantage of the deep residual convolutional network in extracting the deep features from the pictures, and finally the identification module completes the recognition of the tool wear type. To verify its feasibility, a testing bed is built on a CNC milling machine, and multi-sensor data are collected during the machining process of a simple workpiece with one of the two selected cutting tools each time. The proposed system is trained using the collected data and then is used to monitor the wear condition of a new cutting tool by collecting real-time data under similar condition. The results show that the accuracy of wear state recognition is as high as 90%, which verifies the feasibility of the proposed tool wear monitoring system.

A data-driven digital twin framework for key performance indicators in CNC machining processes

ABSTRACT This paper presents a data-driven digital twin (DT) framework that predicts key performance indicators (KPIs) in a CNC machining environment. The decision-makers can use these predicted KPIs in the CNC machining process flow to better choose cutting parameters to accomplish the required KPIs. Those beneficiaries would be the process planner in the process planning stage and the machine operator in the machining stage. The cutting parameters affect major performance KPIs such as machining time, quality, and energy consumption. So, correctly selected cutting parameters can improve KPIs in CNC machining operations. In this paper, the two KPIs considered for building predictive models, and their application in the proposed DT with experimental data are energy and surface roughness. The data for building the predictive models for a CNC milling process are obtained through experiments. This work also illustrates the choice of predictive modelling methods in both the stages of CNC machining and its outcomes.

Incremental sheet forming process of an AA3003-H12 part: Simulation study and experimental validation

A three-finite element model to simulate the single point incremental forming (SPIF) of AA3003-H12 parts is developed. Tool motion in the simulation environment and the CNC milling machine is controlled using new protocols developed especially for SPIF process of pyramidal parts. Numerical measured part thickness and geometry are found to best agree with the experimental ones. Due to the lack of tools, enabling a proper selection of numerical factors to overcome computation convergence problems and product conformity error, trial and error method is applied alternately and proves to be effective. Thickness distribution, equivalent stress distribution, finite element size and friction coefficient effect on the final thickness distribution, geometric profile and computation time are studied. In order to understand the failure and fracture mechanisms in the produced part, the stress state, thickness and equivalent stress history at the thinned finite element are equally studied. Results show a critical thinning occurs at the part corners more dramatic than in the walls. Equivalent stress presents great values first at the part corners and second at the fillets. Failure areas do not coincide with fracture ones due to different deformation mechanics. The increase of the finite element size increases the thickness and the geometry error, but reduces significantly the computation time. The friction coefficient does not significantly affect the thickness and geometry, leading to a significant computation time reduction.

Channel Selectivity of Satellite Transponders with the Antenna Combined with a Size-Reduced Metallic Waveguide Bandpass Filter Having Thin Metamaterial Resonators.

Global and intercontinental networking relies on satellite communication. Its wireless communication system always has antennas and their feed assembly comprising waveguides. This makes the satellite payload heavy and costly. In this paper, a novel method is proposed to effectively reduce the size of a waveguide bandpass filter (BPF). Because the metallic cavities make the conventional waveguide end up with a large geometry, especially for high-order BPFs, very compact waveguide-type resonators having metamaterial zeroth-order resonance (WG-ZOR) are designed on the cross-section of the waveguide and substituted for the cavities. While the cavities are half-wavelength resonators, the WG-ZOR is shorter than one eighth of a wavelength. A substantial reduction in size and weight of the waveguide filter is observed as the resonators are cascaded in series through coupling elements in the X-band much longer than K- or Ka-band. An X-band of 7.25~7.75 GHz is chosen to verify the method as the passband with attenuation of 40 dB at 7.00 GHz and 8.00 GHz as the roll-off in the stopband. The BPF is manufactured using the CNC milling technique. The design is carried out with geometrical parameters, not of the level of 10 μm, but the level of 100 μm, which is good for manufacturers but a big challenge for component designers. The measurement of the manufactured metal waveguide filter reveals that the passband has about ≤1 dB and ≤-15 dB as insertion loss and reflection coefficient and the stopband has ≤-40 dB as attenuation, which are in good agreement with the results of the circuit and simulation. The proposed filter has a length of 3.5 λg as the eighth-order BPF, but the conventional waveguide is 5 λg as the seventh-order BPF for the same area of the cross-section. This metamaterial BPF is combined with a horn antenna. The filter enables the wide-band antenna to distinguish the band of transmission from that of noise suppression. This channel selectivity is obviously observed by the filter integrated antenna test.

Prediction of Thermal Deformation and Real-Time Error Compensation of a CNC Milling Machine in Cutting Processes

This study constructs a prediction model of thermal deformation with an artificial neural network and implements the real-time error compensation for a three-axis vertical CNC milling machine in cutting processes to improve the thermal error of the workpiece. There are 32 PT-100 thermal sensors installed in key parts of the machine in order to measure the temperature of key machine parts in actual cutting conditions. Pearson’s correlation coefficients are used to select crucial temperature sensors for building the prediction model of thermal deformation. The reduced number of crucial temperature sensors in model construction can simplify the model complexity and speed up the response time of prediction. This study constructs a long short-term memory (LSTM) neural network model to predict the thermal error of the machine in cutting processes. This prediction model of thermal deformation can be further used in real-time error compensation of the workpiece in cutting processes. In an 8 h cutting experiment, the dimensions of the workpiece show that, with real-time error compensation, the thermal error in X-axis decreases from 7 µm to 3 µm, the thermal error in Y-axis decreases from 74 µm to 21 µm, and the thermal error in Z-axis decreases from −64 µm to −20 µm. The results indicate that the prediction model of thermal deformation and the real-time error compensation can significantly reduce the thermal error and improve the dimensional accuracy of the workpiece.