Milling System Research Articles

Structure analysis, ferroelectric and ferromagnetic properties in nanostructured Bi2O3 –Fe2O3 –PbTiO3 for magnetoelectric applications

The ternary 0.52Bi2O3 – 0.18Fe2O3 – 0.30PbTiO3 (BFPT) system was prepared by mechanosynthesis in a high-energy planetary mill. The mixed powder was milled for 1, 2, 5, and 10 h. After 5 h of milling, the partially crystalline phase was achieved, as investigated by the XRD analysis. HRTEM verified the nanoparticle formation, and the mean size of the particles is estimated to be ⁓ 2.95 nm. Dielectric permittivity, loss tangent, electric modulus, and ac conductivity were measured for the milled system for 5 h as a function of frequency and temperature. The nanostructured BFPT sample shows a high dielectric permittivity (ε′ ⁓ 1388 at 500 Hz) obtained at Tc ⁓ 633 K (Curie temperature), which corresponds to the structural phase transition. The experimental results of σac are acceptable with the CBH model. The measurements of the P–E hysteresis loop illustrated recoverable energy density (Wrec=4.33 mJ/cm3) and energy storage efficiency (η=60.56%) at room temperature. The M-H data represent remnant magnetization (Mr) of 12.41 × 10−3 emu/g and coercive field (Hc) of 497.23 Oe. These remarks suggest that the nanostructured BFPT sample might be a promising option for magnetoelectric applications.

The effect of the accumulator on oil pressure shock suppression based on mill screw-down system

This paper takes the servo valve front accumulator as the research object. It analyzes its effect in suppressing pressure shock and improving system pressure stability. Finally, it achieves the purpose of improving the control accuracy of the hydraulic screw-down system of the rolling mill. According to the actual operating requirements, the mathematical model of the servo valve front accumulator is established. The pressure shock suppression effect of the accumulator is simulated and analyzed with different parameters in the process of servo valve adjustment and reversal. Finally, it is verified by experiment. The optimal selection method of the parameters of the accumulator is obtained to provide theoretical and empirical support for the study of methods to enhance the system pressure stability and improve the control accuracy.

A Dynamic Behavior Analysis of a Rolling Mill’s Main Drive System with Fractional Derivative and Stochastic Disturbance

Taking the random factors into account, a fractional main drive system of a rolling mill with Gaussian white noise is developed. First, the potential deterministic bifurcation is investigated by a linearized stability analysis. The results indicate that the fractional order changes the system from a stable point to a limit cycle with symmetric phase trajectories. Then, the stochastic response is obtained with the aid of the equivalent transformation of the fractional derivative and stochastic averaging methods. It is found that the joint stationary probability density function appears to have symmetric distribution. Finally, the influence of the fractional order and noise intensity on system dynamics behavior is discussed. The study is beneficial to understand the intrinsic mechanisms of vibration abatement.

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link. The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansion factor. The control performance is directly related to the contraction-expansion factor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Its simulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.

Effect of Fabrication Technology on the Accuracy of Surgical Guides for Dental-Implant Surgery.

The accuracy of surgical guides is a relevant factor in both surgical safety and prosthetic implications. The impact of widespread fabrication technologies (milling and 3D printing) was investigated. Surgical guides manufactured by means of two specific milling and 3D-printing systems were digitized and compared in a 3D analysis with the digital file of the designed guides. The surface mean 3D distance (at the surface where the teeth and mucosa made contact) and the axial and linear deviations of the sleeves' housings were measured by means of a metrological software program. Univariate and multivariate statistical analyses were used to investigate the effects of the fabrication technology, type of support, and arch type on the surgical guides' accuracy. The median deviations of the intaglio surface in contact with the mucosa were significantly (p < 0.001) lower for the milled surgical guides (0.05 mm) than for the 3D-printed guides (-0.07 mm), in comparison with the reference STL file. The generalized estimated equation models showed that the axial deviations of the sleeves' housings (a median of 0.82 degrees for the milling, and 1.37 degrees for the 3D printing) were significantly affected by the fabrication technology (p = 0.011) (the milling exhibited better results), the type of support (p < 0.001), and the combined effect of the fabrication technology and the sleeve-to-crest angle (p = 0.003). The linear deviation (medians of 0.12 mm for the milling and 0.21 mm for the 3D printing) of their center points was significantly affected by the type of support (p = 0.001), with the milling performing slightly better than the 3D printing. The magnitude of the difference might account for a limited clinical significance.

Development of a Bamboo Toothbrush Handle Machine with a Human–Machine Interactive Interface for Optimizing Process Conditions

Non-renewable materials like plastics are widely applied on toothbrush handles and bristles. Polypropylene (PP) or polyethylene (PE) is often used to fabricate the toothbrush handle, while nylon (PA) is used to form the bristle. These plastics are sourced from non-renewable fossil fuels. The primary greenhouse gases—nitrous oxide (N2O) and carbon dioxide (CO2)—in the Earth’s atmosphere are released during the production of these plastics. Bamboo can generate 30% more oxygen than most plants and trees, which absorbs twice as much carbon dioxide as trees. A comparison of the cradle-to-grave material requirements between bamboo and plastic toothbrushes reveals that bamboo toothbrushes entail hidden environmental costs. Nevertheless, bamboo toothbrushes can be completely decomposed in the environment, which makes them eco-friendly and sustainable green products. This research aims to develop a bamboo toothbrush handle machine with a human–machine interactive interface and production management for optimizing process conditions. The machine is designed as a double-group to stably mass-produce high-quality bamboo toothbrush handles under optimal process conditions. Although bamboo is a sustainable green material, the shaping process is difficult due to an extremely anisotropic property in the bamboo structures. An improper process condition will induce a rough or scorched surface, which may further cause a tearing crack. The bamboo toothbrush handle milling machine is usually designed by a profiler, which uses various molds to change the shapes and sizes of bamboo toothbrush handles. This machine cannot probe the accurate cutting force for optimizing the cutting operations, paths, and parameters. The proposed equipment with a double-group design includes two storage racks of raw materials, two feeding devices, two exchange clamping devices, and a dual-spindle milling system required to form the shaping process of bamboo toothbrush handles. The whole system is propelled by a computer numerical control (CNC) SYNTEC controller, which can fabricate the bamboo toothbrush handle with various shapes and dimensions. This controller is integrated with a LabVIEW human–machine interactive interface via a Modbus RTU communication protocol. The optimal milling paths, manufacturing methods, and feeding rates are verified by a surveillance system to detect the instant currents of both spindles via the trial-and-error method and mass production. The maximum output of the equipment can reach four bamboo toothbrush handles per minute and 1600 bamboo toothbrush handles per day.

Digital Twin Based on Historical Data and Simulation Results: Fault Detection and Estimation of the Remaining Useful Life of a Cyclone Bag Filter

This study deals with the development of a digital twin for monitoring the operating conditions of a cyclone bag filter installed on the suction system of a wheat mill. The model aims to be used for fault identification and real-time prediction of the remaining useful life (RUL). Computational fluid dynamics simulations were performed to characterize in detail the fluid-dynamic behavior of the airflow inside the system under different conditions of filter sleeve clogging. Furthermore, the simulation results were used to identify a location for the installation of a new velocity sensor that would allow, together with the pressure drop measured at the ends of the filter, monitoring of the systems’ conditions. A model able to assess the filter’s operating state, identify failure events or operating anomalies, and make a prediction of the RUL was then developed. A possible implementation of the developed model, based on the simulation results that aimed to optimize the management of the sleeve cleaning cycles was also proposed. The developed digital model was then tested on a working cycle lasting one year, in which a sleeve failure was simulated. It was shown how the simultaneous monitoring of the two identified quantities allows for the correct identification of the failure and the accurate prediction of the RUL.

PRODUCTION OF SPECIFIC FLOUR FOR FROZEN PRODUCTS BY FLOUR STREAMS BLENDING

The bakery industry requires specific quality flours to manufacture different products. For the production of frozen products, strong flour with specific quality indicators is required, which is caused by the deterioration of the structural and mechanical properties of the dough after defrosting, decrease in the gas–holding capacity and dimensional stability of the dough blanks. Based on the literary review and a priori information the following quality indicators are recommended for flour for frozen products: whiteness &gt;54 units, protein content &gt;12.0%, gluten content &gt;28.0%, GDI in the range of 70–80 units, starch damage in the range of 19–22 UCD, WAC &gt;57%, W &gt;260∙10–4 J and P/L ratio in the range of 0.8–1.2.&#x0D; Milling process of the wheat generates different flour streams that differ in composition, particle size, functional and baking properties. Furter all flour mill streams are blended in one straight grade (single grade milling) or multiple flour grades (split milling). Research in the article is to investigate the quality indicators of flour mill streams for further formation of specific flour for frozen products. For the research, samples of flour were taken from each milling system of the flow diagram during milling of the two hard red winter wheat samples (HW1 and HW2 with protein content 13.3 and 12.2% respectively).&#x0D; Based on the analysis of the quality indicators of flour milling streams and their comparison with the requirements for flour for frozen products, it was established that none of the flour milling streams meets all the flour quality indicators for frozen products – flour streams from systems B3 and B2 have the closest quality indicators. Selected individual flour milling streams that are of great interest as streams for blending for flour for frozen products.&#x0D; As a result of comparing the flour quality indicators obtained at various stages of the technological process with the requirements for flour quality indicators for frozen products flour, the optimal percentage of flour patent yield for frozen products составляет 33.2% for HW1 and 20.5% for HW2.

Experimental modal transferring of industrial robot with data augmentation through domain adaptation and transfer boosting

Pose-dependent modal properties of the robotic milling system determine the milling stability, which significantly affects the machining accuracy and surface quality. Experimental modal analysis is an effective way to obtain the actual modal properties, however, the full experimental modal measurement in the entire robot workspace is repetitive and time-consuming, and the modal properties of a measured workspace can hardly transfer to an unmeasured workspace because of domain discrepancy and data imbalance. In this paper, the mathematical formalization of modal properties transferring problem between different workspaces is recommended as heterogeneous transfer, and a transfer learning method of modal properties is proposed by integrating domain adaptation and transfer boosting to eliminate domain discrepancy and data imbalance. Firstly, a workspace is selected as the source workspace in which pose-dependent modal properties are obtained as the source data through a series of sufficient impact tests. For a new workspace, namely the target workspace, a few impact tests would be sufficient to obtain the modal properties as the target data. Then, both the inputs of source and target data are projected onto a reproducing kernel Hilbert space (RKHS) through transfer component analysis (TCA) to get the IN-Domain (IND) inputs. The source data is used to generate pseudo data with data augmentation through affine transformation, and the pseudo data is regarded as coarse data for the target workspace. Finally, the precise modal properties models are trained with TrAdaBoost.R2 in which the training data was obtained by mixing pseudo data and target data, and the unmeasured modal properties in the target space are predicted. The effectiveness of the proposed method is verified through experimental modal properties transferring between different workspaces and modal orders for the robotic milling system, and the comparisons with Gaussian process regression (GPR) demonstrate the outperformance of the proposed method.

Marginal and Internal Gaps, Surface Roughness and Fracture Resistance of Provisional Crowns Fabricated With 3D Printing and Milling Systems.

To compare the marginal and internal gap, surface roughness, fracture resistance and mode of failure of provisional crowns fabricated with 3D printing and CAD/CAM manufacturing techniques. A maxillary first premolar was prepared for an all-ceramic crown and reproduced in cobalt-chromium metal dies (n=30) following CAD/CAM technology. The die was digitally scanned and exocad software was used to design the missing crown, which was manufactured using a CAM milling machine and two types of 3D printing machines (SLA and DLP) to produce 10 provisional crowns per group. The crowns were cemented to their respective dies and stored in water (24 hours; 37°C) followed by 600 thermal cycles (5°C/55°C) and 100,000 mechanical cycles (at 50N). Marginal and internal gap measurements were carried out using a 3D superimposition technique. Surface roughness (Ra) was determined using a profilometer at 0.5 mm/second cross-head speed. The crowns were loaded to fracture at 0.5 mm/minute cross-head speed. Fracture forces and mode of failure were recorded, and data were analyzed using one-way analysis of variance (ANOVA) and multiple comparisons post hoc tests (p<0.05). The SLA group exhibited higher marginal and internal gaps than both the CAD/CAM and DLP groups (p<0.05). The DLP group showed higher surface roughness than the CAD/CAM and SLA groups (p<0.01). The CAD/CAM and SLA groups showed higher resistance to fracture than the DLP (p<0.05) group. Mode of failure I: Minimal crown fracture or crack, was predominate among DLP and SLA crowns (60%), while the Mode II: Less than half of the crown lost, was predominant among CAD/CAM crowns (60%). Crowns manufactured following CAD/CAM technique have better marginal and internal fit, surface roughness than SLA and DLP crowns. The CAD/CAM and SLA crowns showed higher resistance to fracture than the DLP crowns.

Reinforcement Learning With Model-Based Assistance for Shape Control in Sendzimir Rolling Mills

As one of the most popular tandem cold rolling mills, the Sendzimir rolling mill (ZRM) aims to obtain a flat steel strip shape by properly allocating the rolling pressure. To improve the performance of the ZRM, it is meaningful to adopt recently emerging deep reinforcement learning (DRL) that is powerful for difficult-to-solve and challenging problems. However, the direct application of DRL techniques may be impractical because of a serious singularity, partial observability, and even safety issues inherent in mill systems. In this brief, we propose an effective hybridization approach that integrates a model-based assistant into model-free DRL to resolve such practical issues. For the model-based assistant, a model-based optimization problem is first constructed and solved for the static part of the mill model. Then, the obtained static model-based coarse assistant, or controller, is improved by the proposed reinforcement learning, considering the remaining dynamic part of the mill model. The serious singularity can be resolved using the model-based approach, and the issue of partial observability is addressed by the long short-term memory (LSTM) state estimator in the proposed method. In simulation results, the proposed method successfully learns a highly performing policy for the ZRM, achieving a higher reward than pure model-free DRL. It is also observed that the proposed method can safely improve the shape controller of the mill system. The demonstration results strongly confirm the high applicability of DRL to other cold multiroll mills, such as four-high, six-high, and cluster mills.

Improvement of the cold rolling mill diagnostic system based on the data base of its electromechanical processes

Purpose. To improve the diagnostic system of the cold rolling mill based on the database of its electromechanical processes, by developing a fuzzy decision-making system as to the condition of the rolling mill electrical drives, which will increase the efficiency of the existing diagnostic system. Methodology. Mathematical and computer modeling. Findings.A fuzzy decision-making system about the state of two interconnected electric drives of the unwinding mechanism and the rolling mill has been developed to investigate and prevent the pre-emergency state associated with the break of the rolling metal strip. The specified decision-making system is built on the basis of a database of electromechanical processes of electric drives of the skin-threat single-celled state of cold rolling of the cold rolling shop No. 1 of PJSC "Zaporizhstal". At the input of this system, the voltage of the armature circuit of the unwinding mechanism motor, the armature circuit current of the unwinding mechanism motor, the tension of the rolled metal strip in the area between the unwinder and the rolling cage are set. At the output, the general state of the system is obtained, which depends on the values of the input data. Information on the value of the tensile strength of the rolled metal strip can be obtained on the basis of the pressure data and voltage sensors, which are installed on industrial equipment. Information about the value of the tension force of the rolled metal strip can be obtained both by installing additional measuring devices and indirectly, using mathematical models of the roll current radius and the linear speed of the rolled metal strip. Originality. The system for diagnosing the condition of cold rolling has been improved by introducing a fuzzy decision-making system into its composition based on a database of electromechanical processes of the electric drives of the unwinding mechanism and the cage, which will allow to prevent an emergency condition caused by a break in the rolled metal strip. Practical value. Prevention of breaking of the rolled metal strip on the basis of an improved system for diagnosing the state of cold rolling, which uses information from the database of its electromechanical processes in order to improve the efficiency of the product quality management process. Using complete organized information and experience of operating a cold rolling mill, you can form technical and technological solutions for the modernization of existing and development of new technological equipment and systems for automatic control of electric drives of rolling mills.

Dynamical responses for a vertical vibration model of a four-roller cold rolling mill under combined stochastic and harmonic excitations

Considering that random fluctuations can affect the rolling process of cold rolling mills, leading to abnormal cold mill performance and defects in the rolled product. Therefore, it is crucial to study the influence of random fluctuations on the system of cold rolling mills. In this paper, taking a model of the vertical vibration of a four-roller cold rolling mill under harmonic excitation as a prototype class for a real system, the effects of random fluctuations on the system response are analyzed. Firstly, based on the deterministic vertical vibration model of a four-roller cold rolling mill under harmonic excitation, a stochastic vertical vibration model of a four-roller cold rolling mill with random fluctuation as Gaussian white noise is introduced. Subsequently, the vertical vibration model of a four-roller rolling mill is theoretically analyzed by the averaging method and the stochastic averaging method, respectively. The effectiveness of the proposed theoretical method is verified by numerical simulation results, and the influences of harmonic excitation and random excitation on the system response are also investigated in detail. Finally, the results show that the noise induces the occurrence of stochastic transitions and bifurcations, and the steady-state probability density function and time history diagrams are given to further explain the existence of these dynamic phenomena. The related research can provide theoretical guidance for the realization of vibration control and reliability design in the four-roller cold rolling mill system.

Research on Continuous Machining Strategy for Five-Axis Machine Tool: Five-Axis Linkage to Four-Axis Linkage

The article presents a novel strategy for enhancing the efficiency of machines that are used for complex structure machining. It proposes a low-cost five-axis four-linkage milling system as an alternative to the more expensive five-axis five-linkage system. Kinematic analysis of the machine tool is conducted to establish a correlation between the tool location data and the displacement of kinematic axes. An interpolation algorithm is then devised to determine a four-axis linkage milling strategy. The theoretical errors of the interpolation trajectory are observed to be reduced following the transformation. The research employs impeller processing as a case study, wherein the five-axis linkage machining path is translated into a more efficient five-axis four-linkage path using the interpolation algorithm. The practical application of this novel milling strategy confirms its effectiveness in processing the integral impeller within acceptable efficiency parameters. The results provide a theoretical foundation for the practical application of the low-cost five-axis four-linkage machining strategy in high-precision five-axis five-linkage machine tools.

Dynamic reconstruction principal component analysis for process monitoring and fault detection in the cold rolling industry

An improved method for process monitoring and fault detection called dynamic reconstruction principal component analysis (DRPCA) is proposed. By extracting direct dynamic connections between samples, DRPCA utilizes the overall dynamic information of the training data set and improves the monitoring performance of dynamic industrial processes. In DRPCA, the optimal orthogonal transformation is used to reconstruct the past sub-matrix with the current sub-matrix. The reconstructed increment matrix preserves the raw data’s basic, incremental, and dynamic information as much as possible. In addition to the traditional T-squared and SPE statistics, a new statistic SPE-R is proposed for DRPCA based on the reconstruction accuracy. We evaluate the performance of the proposed method on a cold rolling mill system, and the results show that DRPCA outperforms DPCA and its improved versions in terms of faster computation speed, more timely alerts, higher detection rates, and lower false alarm rates. Our study demonstrates that DRPCA is a superior method for monitoring dynamic processes.

Laser powder bed fusion of 316L stainless steel with 2 wt.% nanosized SiO2 additives: Powder processing and consolidation

Gas atomized 316L stainless steel powder was processed with 2 wt.% of nanosized SiO2 in a planetary ball milling (BM) system to produce feedstock material for laser powder bed fusion (L-PBF). X-ray diffraction (XRD) of powders revealed the development of micro-strains over increasing milling durations and the strain-induced ferrite formation. BM was limited in duration to preserve the austenitic phase and morphological characteristics analyzed by electron microscopy. In turn, 316L-2wt.% SiO2 feedstock powder was consolidated by L-PBF with varied process parameters and scanning strategies, that demonstrated a maximum density of 7.84 g/cm3 and relatively stable microhardness values close to 220 HV. A distinct (110) preferred orientation was observed in XRD pole figures of austenite for deeper melt pools over the building direction, whereas in-plane alignment reflected variations in scanning strategy. Furthermore, electron backscatter diffraction revealed columnar grains accompanied by the suspected depletion of SiO2, while electrochemical behavior displayed consistent characteristics.

On the global well-posedness of the Einstein–Yang–Mills system

In this paper, we present a partial result on the global well-posedness of the Cauchy problem for the Einstein–Yang–Mills system in constant mean extrinsic curvature spatial harmonic and generalized Coulomb gauges as introduced in the work of Mondal [arXiv:2112.14273 (2021)]. We give a small-data global existence theorem for a family of n + 1 dimensional spacetimes with n ≥ 4, utilizing energy arguments presented in the work of Andersson and Moncrief [J. Differ. Geom. 89, 1–47 (2009)]. We observe that these energy arguments will fail for n = 3 due to the conformal invariance of 3 + 1 Yang–Mills equations and present a gauge-covariant formulation of the Einstein–Yang–Mills system in 3 + 1 dimensions to show that an energy argument cannot be used to prove the global well-posedness result, regardless of the choice of gauge.

Open-architecture of CNC system and mirror milling technology for a 5-axis hybrid robot

This paper presents an open-architecture of CNC system and mirror milling technology for a new-type 5-axis hybrid robot named TriMule. The CNC system with dual CPUs is developed first to achieve human-computer interaction and motion control. Then, three key technologies are integrated in the system for improving the control quality, including singularity avoidance, feedforward control considering joint couplings and real-time error compensation by using externally mounted encoders. Based on these control technologies for single robot system, a collaborative machining strategy on the mirror milling system that consists of two TriMule robots is proposed to control the machining wall thickness of large thin-walled structural parts. Experiments on the TriMule robot and mirror milling system verify that the acceptable machining accuracy on the NAS test part and large thin-walled structural part can be ensured by using the developed CNC system and technologies. The root mean square of wall thickness error using the collaborative machining strategy can be 41.67% lower than the case without using the strategy.

Generation of Textured Surfaces by Vibration-Assisted Ball-End Milling

Textured surfaces have been widely applied in many fields due to their excellent functional performances. Although several micro-scale surface texturing techniques have been used to fabricate surface textures, most are either very expensive, have material limitations, or lack flexibility. In this study, a novel textured surface generation method using vibration-assisted ball-end milling with a non-resonant vibrator is proposed. Firstly, the configuration of the vibration-assisted ball-end milling system is introduced. Then, the trajectories of the cutting edges are modeled and analyzed. Furthermore, an analysis of a non-resonant vibrator is conducted. Finally, surface texture machining experiments are conducted, and the feasibility of the proposed vibration-assisted ball-end milling method for surface texture fabrication is verified.

Hydrothermal carbonization of olive oil industry waste into solid fuel: Fuel characteristics and combustion performance

A low-cost treatment of wet wastes generated by olive oil industry still remains a challenge for the industrial scale applications. In the concept of waste-to-energy, the hydrothermal carbonization (HTC) is suggested as a low-cost valorization way for the wet wastes. In this study, the HTC process was used to obtain clean solid fuel from the wet waste of the two-phase olive mill system. Variation of the fuel properties and combustion performance of obtained solid fuel, hydrochar, with process conditions (temperature and duration) was investigated by carrying out proximate analysis, elemental analysis and thermal analysis. The lignite-like hydrochars having calorific value of 28.4–33.9 MJ/kg were obtained at the temperatures higher than 200 °C. The combustibility index (S) revealed that all obtained hydrochars were suitable solid fuels having reasonable combustion performance and short combustion time. Alkali index values of hydrochars predicted that their combustion in a boiler would be safe. The calculated net energy generation of process was found as 10.2–14.1 MJ/kg dry biomass.