Machine Vibration Research Articles

Influence of Cold Work on the Efficiency of Vibratory Machining

The aim of the article was to determine the impact of crushed condition (work hardening) on the effectiveness of the vibratory machining. The vibratory machining processing was carried out in two steps. The first step consisted of mechanical abrasion and remove oxides from the surface of the workpieces with abrasive media. While in the second step, smoothing - polishing with metal media was performed. Vibratory polishing also strengthened the treated surfaces. The test results were compared for samples in the crushed state (work hardening, plastic processing) and samples subjected to recrystallization annealing heat treatment. Mass losses, changes in the geometric structure of the surface and changes in the hardness of the machining surfaces were analyzed. Samples subjected to recrystallization, as compared to the samples in the state after work hardening-plastic working, are characterized by a slightly higher arithmetic mean surface roughness and lower surface hardness than for analogous processes for samples not subjected to heat treatment. Heat treatment of annealing allows to remove the effects of crushing and thus it is possible to obtain larger mass losses. Smaller burrs dimensions were obtained for samples after the heat treatment – annealing than after work hardening.

Theoretical and experimental study on the fatigue property of material extrusion products and improvement by using vibration machining during manufacturing process

Nowadays, material extrusion (ME) is one of the most popular additive manufacturing technology, which offers a new way of manufacturing parts with specific complex geometry and mechanical properties. However, with the expansion of the application range of ME products, it is urgent to investigate their fatigue property so as to ensure the reliability and safety. In this paper, the damage mechanical fatigue life prediction model of ME products considering void defects was established. Fatigue test specimens built with different processing parameters were then fabricated, and tensile fatigue tests were carried out under different stress levels. In addition, a novel method, utilizing vibration during ME process, was proposed to improve the built products’ fatigue property, and corresponding theoretical and experimental investigations were performed to reveal the influencing mechanism of the vibration utilized. The results showed that the proposed model was validated by comparing the theoretical predictions with the experimental measurements. As layer height and printing speed were increased, the fatigue life of ME specimens dropped, while it rised with the increasing extrusion temperature. The built products’ fatigue life could be improved obviously by using vibration.

Analysis of the particle characteristics of solid-liquid turbulent flow by triaxial vibration statistic and machine learning methods

The real-time monitoring of particle information in turbulent flow is critical for safe and efficient production. A new method based on the fusion of particle-wall free collision and triaxial vibration method are proposed to measure particle information in turbulent flow. The characteristic frequency band of the triaxial vibration response excited by the particle-wall collision is successfully characterized by time-domain statistical, Zoom-FFT and STFT analysis, etc. Further, SVM and CNN were used to evaluate the performance of the particle characterization and analysis methods, high recognition accuracy were obtained for whether particles were present (98.48%), the flow velocities (85%), and the particle sizes (96.67%), which also proved the validity of the triaxial vibration response analysis method. Finally, a particle-wall collision energy model expressed as Energycollision=a1∙m+a2∙p2+b1∙m+b2∙p+c1∙m+c2 was established for solid-liquid flow, and the average error rates for each vibration direction were 11.40% (x-direction), 3.17% (y-direction), 13.92% (z-direction) and 7.90% (multidirectional joint).

Recursive signal denoising method for predictive maintenance of equipment by using deep learning based temporal masking

In the data driven predictive maintenance, high quality data is the premise of high accuracy diagnosis and prediction. In the industrial practice, reducing the noise is of great significance to ensure data quality. This paper proposes a recursive denoising method for manufacturing equipment signals in the data driven predictive maintenance. First, in signal decomposition method, equipment mixed signal is decomposed by temporal masking with dilated convolution neural network to generate a noise mask, which realizes signal decomposition of using recursive operation of temporal masking model. Second, in signal components recognition method, signal component features similarities are calculated, which act on the parameter regulation of signal recognition meta-learning model. The experimental results demonstrated that the proposed method effectively solves the noise reduction problem of the equipment signal. Further engineering tests of a chemical winding machine vibration signal decomposition and recognition show that the proposed method has strong adaptive performance for noise reduction.

Solid-state production of uniform metal powders for additive manufacturing by ultrasonic vibration machining

This work presents a new technique to generate uniform and micron-sized metal powders for additive manufacturing. By collecting discrete chips resulting from ultrasonic vibration machining, we demonstrate the feasibility of all solid-state production consistent powders with tight dimensional tolerance, the ability to control powder geometry, and good efficiency. The technique offers a new route for sustainable and low-cost manufacturing of high-quality metal powders. The powder generation mechanism is analyzed with a special tool path design to ensure consistent dimensions over multiple cuts. An analytical model to predict the dimensions of produced powders under different cutting parameters is introduced. Aluminum and brass powders of different dimensions are produced, and the overall shear ratio that governs the deformation during the machining process is calibrated with the experimental results. The morphology consistency of produced powders is investigated over multiple hours of production, illuminating the role of tool wear on final powder shape. A high-efficiency powder collection system and a scalable solution for parallel production are proposed for the introduced technique. Additive manufacturing experiments (laser powder bed fusion) are conducted using produced A356 aluminum powders, demonstrating the printability of produced powders in additive manufacturing. The microhardness of the printed parts for five different process parameters is measured to be 45% higher than the raw material on average.

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Gearbox failures have a detrimental effect on the machine performance that affects the production capacity and economic benefits of a manufacturing industry in an adverse manner. Early detection of faults in gears helps to prevent sudden machine failures. Previously, many signal processing and artificial intelligence techniques have been successfully applied to diagnose gear faults by analyzing the machine vibrations. These techniques have repeatedly emphasized on the need to extract quality features from gear vibration signals and using artificial intelligence techniques that can identify multiple types of faults simultaneously. As such, in this article, new features called as visibility graph features based on complex network theory have been proposed to extract fault information from vibration signals. In addition, a new artificial intelligence framework for multifault classification known as error-correcting output codes-multiclass support vector machine is proposed to detect gear faults. To the best of authors’ knowledge, these features along with the error-correcting output codes-artificial intelligence framework have been rarely utilized for fault diagnosis purpose. The proposed approach has been applied on experimental gear vibration data to validate its effectiveness. First, the vibration signals are transformed to graphs, and various graph properties are computed to serve as fault features. Then, the fault features are supplied to train the error-correcting output codes-multiclass support vector machine model and learn the fault patterns. Finally, the gear fault classification accuracies are determined for various gear test conditions and compared with those of existing methods. It is observed that the proposed approach provides an average improvement of 4.22%, 9.93% and 11.8% over the time-domain features, time–frequency domain features and voting-based multiclass support vector machine classifier, respectively, for different operating conditions. Furthermore, the suggested technique augments the classification accuracies by 10%, 7.5% and 6.7%, respectively, as compared with the deep-learning models, namely standard stacked sparse autoencoder, deep neural network and convolution neural network.

Study on the effect of rolling mill dynamic stiffness on coupled vibration of hydraulic machine

The productivity and capacity release of hot-strip rolling is severely limited by pendular vibration. Previous studies on mill vibration have investigated the influence of rolling parameters on vertical vibration; however, the influence of dynamic stiffness compensation factors on vertical vibration has not yet been considered owing to the limitations of modelling methods. Herein, we develop a simulation model of mill liquid–machine coupling with dynamic stiffness compensation using AMESim software. The established simulation model is used to investigate the effect of this dynamic stiffness compensation on mill vertical vibration under conditions of downfeed thickness difference excitation frequency, thickness difference excitation amplitude, thickness difference excitation phase, and dynamic stiffness compensation signal generation hysteresis. Evidently, the impact of the mill stiffness compensation on the mill vertical vibration differs with the thickness of the excitation frequency, and the impact should be discussed according to the excitation frequency segment. The amplitudes of the roll system vibration and incoming thickness difference excitation are essentially linear, whereas the excitation phase does not affect the frequency or amplitude of the mill vibration. The rolling machine stiffness compensation signal hysteresis also has a significant impact on the mill vibration amplitude. Our findings will improve the mechanism of the hot rolling process and suppress droop vibrations.

Chatter Stability Prediction for Deep-Cavity Turning of a Bent-Blade Cutter.

The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the influence of bending and torsional vibration, the change of tool profile and the complex machining geometry, so it cannot be directly used to reveal the underlying cause of the chatter phenomena in the deep inner cavity part turning process. This paper attempts to investigate the dynamic problem of the bent-blade cutter turning process. The dynamic model of a bent-blade cutter is proposed by considering the regenerative chatter effect. Based on the extended Timoshenko beam element (E-TBM) theory and finite element method (FEM), the coupling between the bending vibrations and the torsional vibrations, as well as the dynamic cutting forces, are modeled along the turning path. The vibration characteristics of the bending-torsion combination of cutter board and cutter bar, together with the dynamical governing equation, were analyzed theoretically. The chatter stability of a bent-blade cutter with a bending and torsion combination effect is predicted in the turning process. A series of turning experiments are carried out to verify the accuracy and efficiency of the presented model. Furthermore, the influence of cutting parameters on the cutting process is analyzed, and the results can be used to optimize the cutting parameters for suppressing machining vibration and improving machining process stability.

Computational and Theoretical Investigation of Acoustical and Vibrational Properties of Rigid Thin Material

A computational and theoretical investigation of acoustical and vibrational properties of rigid thin fiberglass material was carried out for different boundary conditions. Fiberglass materials could be applied in industries varying from the aircraft and automotive sectors to the built environment and construction sectors. Plate vibration and acoustic radiation were applied to predict the deflection of the thin fiberglass material and sound radiation efficiency at different locations on its surface, while a study-controlled equation of motion known as the Kirchhoff thin plate theory was applied for a COMSOL simulation of the thin material to determine the deflection of the plate and to obtain stress distribution, velocity contour, displacement, and acoustic pressure at the first resonance of the material. The results of this paper show that thin fiberglass material could be applied to sandwich building elements to form panels for reducing airborne noise and to lessen the sound transmission of structural borne noise, to cover noise barriers to make them more sustainable and weather resistant, to dampen the vibration of machines, and to reduce the structural vibration of buildings.

Biomimetic leaves with immobilized catalase for machine learning-enabled validating fresh produce sanitation processes

Washing and sanitation are vital steps during the postharvest processing of fresh produce to reduce the microbial load on the produce surface. Although current process control and validation tools effectively predict sanitizer concentrations in wash water, they have significant limitations in assessing sanitizer effectiveness for reducing microbial counts on produce surfaces. These challenges highlight the urgent need to improve the validation of sanitation processes, especially considering the presence of dynamic organic contaminants and complex surface topographies. This study aims to provide the fresh produce industry with a novel, reliable, and highly accurate method for validating the sanitation efficacy on the produce surface. Our results demonstrate the feasibility of using a food-grade, catalase (CAT)-immobilized biomimetic leaf in combination with vibrational spectroscopy and machine learning to predict microbial inactivation on microgreen surfaces. This was tested using two sanitizers: sodium hypochlorite (NaClO) and hydrogen peroxide (H2O2). The developed CAT-immobilized leaf-replicated PDMS (CAT@L-PDMS) effectively mimics the microscale topographies and bacterial distribution on the leaf surface. Alterations in the FTIR spectra of CAT@L-PDMS, following simulated sanitation processes, indicate chemical changes due to CAT oxidation induced by NaClO or H2O2 treatments, facilitating the subsequent machine learning modeling. Among the five algorithms tested, the competitive adaptive reweighted sampling partial least squares discriminant analysis (CARS-PLSDA) algorithm was the most effective for classifying the inactivation efficacy of E. coli on microgreen leaf surfaces. It predicted bacterial reduction on microgreen surfaces with 100% accuracy in both training and prediction sets for NaClO, and 95% in the training set and 86% in the prediction set for H2O2. This approach can improve the validation of fresh produce sanitation processes and pave the way for future research.

Force servo nano-precision diamond cutting of freeform surfaces on a micro-precision machine

This paper presents force servo single point diamond cutting of freeform surfaces with nano-precision on a micro-precision machine. For nanometric machining accuracy, traditional diamond cutting based on position servo, requires accurate machine axial motions by ultra-precision machine with high cost, or utilizes a fast tool servo to compensate for machine slide vibration and motion error of common machines that is a complicated procedure. In this paper, nanometric machining accuracy was achieved on a micro-precision machine by force servo diamond cutting, in which the tool can be driven to trace the workpiece surface to compensate for the machine slide vibration, machine motion errors and workpiece incline errors accurately and automatically. For highly sensitive and stable force controlling, force servo nanocutting mechanism (FSNM) based on flexible structures was designed with force sensing resolution down to submillinewton level, and a damping controller was developed to suppress the resonant vibration of the FSNM. For extending application of force servo cutting from fabrication of only discontinuous microstructures to continuous freeform surfaces, referring force maps were developed by considering the real material removal volume of each cutting point. Force servo cutting was then validated by fabricating freeform surfaces with surface form errors of less than 10 nm on silicon spherical surfaces based on the micro-precision machine. The works discussed in this paper provide an effective technique for the fabrication of complex and continuous microstructured and freeform surfaces with nano-precision by common machines only with micron even tens of microns precision.

Novel Framework for Quality Control in Vibration Monitoring of CNC Machining.

Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.

Vibration Research on Centrifugal Loop Dryer Machines Used in Plastic Recycling Processes

This study investigates the vibrations of centrifugal loop dryer machines used in plastic recycling processes. These machines are characterized by large uncertainties, high vibration, and unmodeled dynamics, making the design and maintenance of real-time state estimators for their operational conditions difficult. The present study includes an analysis of the centrifugal loop dryer machines’ vibration characteristics and their influence on operation results based on vibration analysis, frequency response analysis, and expert advice. Two identical loop dryers installed and operated in parallel in a single recycling line were investigated. Measurements were performed using a two-sample measurement design and based on a one-sample statistical method for estimating uncertainty in repeated measurements of data processing. Additionally, a problem connected with incorrect machine operation during high vibration, resulting in insufficient drying of loaded material, was investigated. This was defined as a situation in which some melted plastic is still too wet after mechanical drying, caused by the incorrect installation of damper elements of the holding elements. Finaly, it is recommended that a correction of the machine installation and a control measurement are carried out to determine whether the vibration in the base of the machine still exists. A simplified theoretical vibration analysis of the rotating machine was also carried out in the present paper.

Influence of Concentric and Unequal-Turn Windings on Vibration of Induction Machines Considering Supply With Low-Order Harmonics

Influence of Concentric and Unequal-Turn Windings on Vibration of Induction Machines Considering Supply With Low-Order Harmonics

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time.

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first real-time, reagent-free, portable analysis platform for viral detection in liquid saliva, using COVID-19 as a proof-of-concept. We show that vibrational molecular spectroscopy and machine learning (ML) detect biomolecular changes consistent with the presence of viral infection. Saliva samples were collected from 470 individuals, including 65 that were infected with COVID-19 (28 from hospitalized patients and 37 from a walk-in testing clinic) and 251 that had a negative polymerase chain reaction (PCR) test. A further 154 were collected from healthy volunteers. Saliva measurements were achieved in 6 minutes or less and led to machine learning models predicting COVID-19 infection with sensitivity and specificity reaching 90%, depending on volunteer symptoms and disease severity. Machine learning models were based on linear support vector machines (SVM). This platform could be deployed to manage future pandemics using the same hardware but using a tunable machine learning model that could be rapidly updated as new viral strains emerge.

Enhancing functionality of two-rotor vibration machine by automatic control

The demand for the development of cyber-physical systems remains high across various production sectors. While some industries have made significant strides, others still require ongoing research and development efforts. This article delves into the application of feedback control for equipment in the processing industry, using a two-rotor vibration machine equipped with induction motors as a case study. Conventionally, such equipment is manually operated with the help of affordable constructive facilities, which does not align with the principles of Industry 4.0. In response, this paper explores an architecture for an automatic control system capable of handling various technological tasks, including mixing and transporting processed media. This architecture encompasses several components, such as real-time regulator tuning, control of the rotor speed loop, and/or adjustment of the phase shift between the rotors based on machine sensor measurements. A notable advantage of this control system lies in its complete software-based implementation. The article also presents the findings from full-scale experiments, demonstrating the capabilities and effectiveness of the proposed approach.

DETERMINATION OF ELASTIC CHARACTERISTICS OF BUILDING INDUSTRY VIBRATION SYSTEMS

The design of the difficult dynamic systems is examined with removable descriptions ofresilient elements and analytical dependences are got for realization of multimode resonances and the card ofstability of vibration of shock machines is offered.

Experimental verification of additively manufactured stacked multi-wedge acoustic black holes in beams for low frequency

Noise from machine vibrations and oscillations is a growing problem in today’s society. The use of acoustic black holes (ABH) in the area of passive vibration damping as an absorbing metamaterial is an active research field. Previous work has been successful mainly in the higher frequency range above 1500 Hz. This work aims at vibration damping in the lower frequency range below 1500 Hz. Here, additively manufactured multi-wedge ABH with two, three, four and ten wedges were welded to a beam structure and measured to estimate which number of wedges produces the best damping for a specific frequency range. The manufactured wedges largely absorbed a vast amount of the vibration energy induced into the structure and showed promising results. It was found that the more wedges were welded to the beam, the more natural frequencies occurred in the low frequency range. In the case of the ABH with ten wedges, ten eigenmodes were detected in this range, all of which absorbed the induced vibration energy effectively in the low frequency range.

Variasi Frekuensi pada Electric Rotary Table Berbasis Inverter Guna Menurunkan Cycle Time dan Cacat Produk pada Pengelasan Pipa Kapiler

<p><strong><em>Abstract</em></strong><strong></strong></p><p> </p><em>Cycle Time is a matter that must be considered in production time, a good Cycle Time will</em> <em>support the targets that must be met, even if Cycle Time decreases, production will also increase and company turnover will also Increase. In the case study of PT Arisamandiri Pratama, one of its productions is in the form of AC parts, namely capillary pipes which still use a manual rotating table to rotate the pipe to be welded so that it is unable to reach the target of 800 parts / hour with an average Not Good product of 93 parts / day. The design of an inverter-based electric rotary table can reduce cycle time, but how much frequency will affect the results of a welding, not that the faster the welding process the better, the faster the welding will affect the vibration of the table machine which will have an impact on the welding results. By experiment the inverter frequency setting is expected to get a frequency value with maximum speed but no vibration on the rotation table, the frequency used is between 700 Hz to 1500 Hz with an interval of 100 Hz. The experimental results show that the best frequency is 1000 Hz, with a rotation speed of 16 rpm with a production capacity of 935 parts / hour with Not Good products of 59 parts / day or a decrease in Not Good products by 42.1% per day</em>

Vibration and Slope Conditions during Harvesting Affect Radish Mass Measurements for Yield Monitoring: An Experimental Study Using a Laboratory Test Bench.

Site-specific measurements of the crop yield during harvesting are essential for successfully implementing precision management techniques. This study aimed to estimate the mass of radish tubers using the impact principle under simulated vibration and sloped-field harvesting conditions with a laboratory test bench. These conditions included the conveyor speed (CS), impact plate layout (IP), falling height onto the impact plate (FH), the plate angle relative to the horizontal (PH), the field slope, and the vibration of the harvesting machine. Two layouts of impact-type sensors were fabricated and tested, one with a single load cell (SL) and the other with two load cells (DL). An adjustable slope platform and a vibration table equipped with vibration blades were utilized to simulate the slope and vibration effects, respectively. Calibrations were conducted to verify the accuracy of the sensor outputs, processed with the finite impulse response and moving average filters. Radish mass was estimated using an asymmetrically trimmed mean method. The relative percentage error (RE), standard error (SE), coefficient of determination (R²), and analysis of variance (ANOVA) were used to assess the impact plate performance. The results indicated that the SE for both impact plates was less than 4 g in the absence of vibration and slope conditions. The R2 for the single and double impact plates ranged from 0.58 to 0.89 and 0.69 to 0.81, respectively. The FH had no significant impact, while the PH significantly affected the mass measurements for both impact plates. On the other hand, the CS significantly affected the plate performance, except for the double-load-cell impact plate. Both vibration and slope affected the mass measurements, with RE values of 9.89% and 13.92%, respectively. The RE for filtered radish signals was reduced from 9.13% to 5.42%. The tests demonstrated the feasibility of utilizing the impact principle to assess the mass of radishes, opening up possibilities for the development of yield-monitoring systems for crops harvested in a similar manner.