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A cost-effective stereolithography for medium-scale components is developed to fabricate 3D components with high build speed and resolution from photo-curable resin. The developed SLA utilizes a focused light beam of wavelength range (300 nm–700 nm) coming from the DLP projector and passes through the objective lens and finally imposed on the platform containing a layer of photo-curable resin. After focusing the light beam on the liquid resin layer, the photo-polymerization reaction occurs and the liquid resin becomes solid. Thus, the 3D object is fabricated layer by layer curing of liquid resin. The photopolymer used in this experimentation is polyethylene glycol di-acrylate and Irgacure 784 as photo-initiator. The Creo 3.0 software is used for modelling of 3D objects. A special MATLAB code is developed for slicing of the 3D model and displaying the sliced image one by one through DLP projector. The Arduino microcontroller with stepper motor and ball screw is used to control the motion of Z-stage platform. The Creation workshop software is also used to control motion of the Z-stage and period to display the sliced images through DLP projector. The medium-scale 3D objects with rectangular, square, and circular cross-sections are obtained by curing the aforementioned photo-curable resin. It is observed that the 3D objects are best cured for two seconds curing time with 0.1 mm curing depth along Z-axis.

In this study, a 5-degrees-of-freedom dynamic model of variable lead preloaded single nut ball screw is proposed. The proposed model considers the effect of excitation amplitude, deflection angle, the number of balls, and the preload into consideration. Moreover, to study the effect of working parameters on nonlinear dynamics, bifurcation diagram, 3-D frequency spectrum, phase diagram and Poincaré section with different system parameters are shown in discussion section. The numerical analysis indicates that the system can exhibits different motion states, and the continuous frequency component can be observed. Moreover, a series of experiments are conducted to estimate the dynamic parameters and validate the proposed dynamic model.

Data-based methods are capable to monitor machine components. Approaches for semi-supervised anomaly detection are trained using sensor data that describe the normal state of machine components. Thus, such approaches are interesting for industrial practice, since sensor data do not have to be labeled in a time-consuming and costly way. In this work, an ensemble approach for semi-supervised anomaly detection is used to detect anomalies. It is shown that the ensemble approach is suitable for condition monitoring of ball screws. For the evaluation of the approach, a data set of a regular test cycle of a ball screw from automotive industry is used.

Purpose For ball screw feed system, a sudden start or stop has a great influence on the transmission stiffness, so the axial stiffness mutation of feed system will occur. The purpose of this paper is to study the influence of acceleration on the transmission stiffness and dynamic characteristics of the ball screw feed system. Design/methodology/approach Taking the ball screw feed system as a research object, on the basis of the Hertz contact theory and the mixed element method, axial stiffness model and dynamic model are established. And the system stability was analyzed by the time history diagram and Phase-plane portrait diagram. The feed system was analyzed theoretically and experimentally, the experimental results are in good agreement with the model results. Findings Lead screw lead angle, preload, load and start acceleration affected ball-screw pair, bearing and transmission stiffness. And the load, nut contact stiffness, bearing contact stiffness, preload have a large effect on the transmission stiffness. The results show that a certain acceleration value will make the axial stiffness abrupt change. Originality/value This research provides a useful theoretical support for ensuring a good dynamic for the ball screw feed system.

The positioning precision of a half-closed-loop feed system of machine tools is usually affected by the frictional heat of the system. The axial thermal errors of the working table exceeding the axial allowed tolerance is a type of failure mode of the ball screw system in positioning precision. At present, thermal error prediction and compensation methods have poor accuracy retention because they do not consider the randomness of the parameters. In order to solve the problem, we present a new random moving difference method of studying a tree-like heat transfer with thermally excited moving branches. Furthermore, based on the random moving difference method, a dynamic reliability prediction model (DRPM) of thermally deduced positioning precision is proposed, which was used in the study for predicting the dynamic variation performance of the heat boundaries in the ball screw feed drive system and the dynamic reliability of positioning precision of the system. Finally, the prediction performance of the presented method is verified under a CNC lathe. Our method provides the theoretical and practical foundation for a real-time monitoring positioning precision reliability index while describing the positioning precision accurately because it effectively takes into account the random parameters.

Load distribution in ball screws is a representation of the ball contact stress, and it is fundamental to understanding the behavior of these machine elements. This work aims to conduct a multi-variable analysis of the load distribution in ball screws. For this purpose, a numerical tool is developed for the generation and calculation of ball screw finite element (FE) models, which has been validated against the state of the art. This tool is based on the combination of an analytical contact model and the use of high-order FE models for the analysis of the load distribution of ball screws and stands out for its accuracy (less than 1% error against high-order FE models), adaptability, versatility (models are generated with more than 20 design variables and they can be introduced as components in larger models) and efficiency (being the computational time 1.25% of that of a high-order FE models) with respect to other existing models. Many different design variables (number of start threads, pitch, contact angle, ball size, slenderness and load arrangement) are studied in order to obtain a general characterization of the morphology of the load distribution in ball screws. Among them, the most influential variables on the load distribution and therefore on the structural behavior of ball screws are, load arrangement (with ratio r variations of up to 25% on the same ball screw) and slenderness (with ratio variations of up to 13% on ball screws with two turns of difference). The two most characteristic features, the non-uniformity at a local and global level are identified, along with as the possible causes of their appearance and the consequences that they may cause.

The implementation of real-time fault detection technology for key components of high-speed train traction electromechanical system is of great significance for improving train motor reliability and reducing guarantee costs. It has become an inevitable trend. Machine learning is a new and powerful means of researching fault detection technology. Based on machine learning, this paper conducts real-time fault detection technology research on the electromechanical actuators of the key components of electromechanical systems. A model of the electromechanical actuator is established, and the mechanism, influence and fault injection method of the three faults of the electromechanical actuator motor shaft jamming, gear broken tooth, excessive ball screw clearance and two faults of internal leakage are analyzed. On this basis, a fault simulation model of the traction motor of a high-speed train was built to obtain simulation fault data. At the same time, based on wavelet packet decomposition and reconstruction, the fault simulation data of electromechanical actuators and hydraulic pumps are analyzed, the wavelet packet energy distribution is calculated, and time-domain statistics are combined to extract energy feature vectors that can reflect component fault characteristics. This paper proposes a fault diagnosis method for electromechanical actuators based on machine learning, designs, and improves the neural network learning algorithm and network parameters, and improves the classification effect of the neural network; proposes a fault diagnosis method based on the GA-SVM algorithm, using real values. The coded genetic algorithm improves the parameter optimization of the support vector machine, and improves the classification speed of the support vector machine. Finally, the effectiveness and superiority of the two fault diagnosis methods designed in this paper are verified on their respective objects.

The feed system of machine tool has complex nonlinear dynamic behaviors in the process of machining, which will affect the machining accuracy of parts. The two-degrees-of-freedom dynamical model of the feed system with clearance and friction is established, where the torsion and load disturbance are considered. By using the averaging method, the approximate analytical solution of the feed system of the machine tool is solved. The approximate analytical solutions are verified by the numerical solutions, when the worktable moves at a constant speed or the worktable is at the working point. And the approximate analytical solutions are in good agreement with the numerical solutions. According to the Lyapunov stability analysis, the stability of the steady-state solutions of system is judged by the maximum real part of the eigenvalues. The analysis results show that, the amplitude of the amplitude-frequency response of system decreases when the friction force increases. As the clearance increases, the resonance frequency decreases gradually. When the lead of the ball screw increases, the resonance peak and resonance frequency increase gradually. The numerical simulation analysis of the system shows that when the worktable is at the working point, the angle displacement of the lead screw chatters while it reaches the maximum or minimum value of the vibration angle displacement.

Thermal errors affect the accuracy of computer numerical control machine tools and are produced by the thermal deformation of machine components due to temperature difference between heat source and ambient temperature of the machine tools. At present, most of the literature does not consider the randomness of the influencing factors of thermal error, leading to inaccurate predictions of machine tool thermal error. In this paper, a new inverse random model is proposed through the combination of the stochastic theory, genetic algorithm, and radial basis function neural network (RBFNN), to predict thermal error while considering the randomness of influencing factors. The randomness index of influencing factors can be identified using the inverse random RBFNN (IR-RBFNN). Furthermore, through the combination of the stochastic theory, RBFNN, and the improved exponential moving average method with abnormal data elimination, a new forward random radial basis function neural network (FR-RBFNN) is established according to the identified influencing factor random index. The models are verified through experimental results on a ball screw system. Compared with the traditional methods, the experimental data show that the proposed method provides a more accurate description of thermal errors while incorporating the randomness of factors affecting thermal error.

Ball screws, the most frequently used mechanical components to transform rotary motion into linear motion, can directly affect the precision and service life of engineering machines. Once the efficiency and accuracy of ball screws degrades, the performance and safety of machines are hard to guarantee. Conventional fault diagnosis researches of ball screws are mainly focused on ordinary faults such as preload loss and wear, and lack of the researches on early faults such as lubrication degradation which may progress into the ordinary faults. Additionally, the fault diagnosis models proposed in previous studies divide the fault diagnosis into two separated stages: feature extraction and fault classification, which prevents the usage for real-time applications. The specifically designed algorithm in features extraction stage may be also not workable on other objects. To tackle these drawbacks, this paper proposes a highly accurate early fault diagnosis model of ball screws based on a state-of-the-art deep learning technique, called One-Dimensional Convolutional Neural Network (1-D CNN). Experiments simulating the lubrication degradation of ball screws are specially designed for the early fault diagnosis of the ball screws. Moreover, a concise and efficient approach based on orthogonal design is exploited to scientifically obtain the optimal parameters of the 1-D CNN. The results of a case study verify the superiority of the proposed method in establishing a highly accurate 1-D CNN based fault diagnosis model.

In accordance with the vibration characteristics of ball screw feed systems, a hybrid modeling method is proposed to study its dynamic behavior. Partially, the ball screw is modeled as a continuous body, and the remaining components are considered lumped masses, allowing for a realistic description of the dynamics of the feed system. The axial, torsional, transverse, and bending vibration models of a ball screw carriage system are established via the Rayleigh-Ritz series method based on the Timoshenko beam assumption. The established model that added the Timoshenko beam assumption obtains the coupling vibration displacement between the transverse and bending vibrations of the lead screw, which is close to real situations. Numerical simulations are conducted to investigate the changes of the natural frequency and modal shapes of ball screw systems with carriage positions. Results show that the carriage position has significant influence on the amplitude and direction of axial and transverse vibrations, substantial influence on the direction of the bending vibration, and minimal influence on the amplitude and direction of torsional vibration. These results indicate that the proposed hybrid model performs well to predict the vibration characteristics of the feed system. Moreover, the carriage position and carriage load also have a remarkable effect on the frequency response of the feed system. These results, along with the modeling approach, provide an important basis for the further study of in-machining monitoring and vibration controller design.

Recently, linear motion (LM) guides have been widely used in industrial processes, especially for precise positioning applications. A LM guide typically requires a custom design for specific characteristics of several industrial fields, which is time consuming for manufacturing process; additionally, and the production line with failed LM guides cannot be fixed rapidly. Therefore, to reduce production loss during such periods, it is important to prepare for maintenance and management in case of a fault through a real-time diagnosis of LM guide conditions. Currently, studies on condition diagnosis applying deep learning algorithms are actively being conducted, and actual measured signals in the industrial fields are being used as training data. In this study, the condition diagnosis of LM guides is conducted through a variational auto-encoder (VAE) with convolutional layers. Normal and fault data are measured using an LM guide unit that consists of four LM blocks and one ball screw. The measured signals are converted to spectrogram images through short-time Fourier transform, and only normal data are used to perform network learning. The trained model is applied to classify the normal and fault states, and the reconstruction error is utilized as the evaluation metrics for classification performance. In order to validate the performance, the results are compared to those of a restricted Boltzmann Machine (RBM), and a stacked auto-encoder and a VAE, which does not consist of a convolutional network. Through this study, it is shown that the LM guide diagnosis is possible with a network (VAE with convolution layers) learned with only normal states and the performance is shown to be superior to other networks.

Reliability of high precision linear motion system is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective linear drives and load bearings. A fault in these members severely affects the safe working of overall system. This paper gives a reliable intelligent approach to detect and classify faults for linear motion systems based on deep learning methods. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). This novel hybrid combination proved very effective in accurate faults identification. The performance of proposed methodology was also validated by IMS-UC (Intelligent Maintenance Systems – University of Cincinnati) public bearing dataset. The results confirm the effectiveness of proposed scheme in comparison to existing state of the art techniques.

Contact of balls in screw nuts and bearings causes the nonlinearity of a ball screw feed system. The identification of nonlinear stiffness helps to understand and control the feed system. The axial vibration of the ball screw feed system can be descripted by a single-degree-of-freedom (SDOF) system with polynomial nonlinear terms. Since generalized frequency response functions (GFRFs) can be expressed in terms of linear and nonlinear system parameters, one can estimate these parameters based on the measured GFRFs. In this effort, both single-tone and multitone harmonic inputs are employed to measure GFRFs, and a simple search algorithm is proposed to find the suitable frequency set of the multitone input. Parameter identification based on these two methods has been compared by numerical simulation and experiment. Since the response spectrum measured from vibration experiment of a ball screw system has even and odd harmonics, it is suitable to simplify the restoring force with square and cubic nonlinearity form. Static experiment is also conducted to verify the identified parameters, and the results show that the method based on single-tone inputs performs effectively in identifying axial stiffness of a ball screw feed system, but the method based on multitone inputs gets the incorrect results in practical application

This paper proposes a novel comprehensive modeling method for deformation errors of different types of rolling joints in machine tools to improving the analysis efficiency. This method comprehensively considers the deformation of three types of rolling joints, i.e., linear guideway, ball screw and bearing, firstly from the perspective of analytical modeling. Two comprehensive models for deformation errors of all rolling joints in two types of motion systems of machine tools are established respectively with the method. The nonlinear interfacial characteristics at rolling joints and the geometric and stiffness coupling characteristics are considered. In order to study characteristics of the synthetical deformation error of rolling joints, a synthetical error model for a five-axis machine tool is developed. Several results and optimizations of the joints are carried out. The proposed method in this paper can effectively avoid the tedious finite element modeling in the traditional analysis process and improve the efficiency.

Ball screws play a critical role in high-quality precision manufacturing. The use of machine learning and artificial intelligence for the diagnosis of machines’ health status is increasingly pertinent. The processing of big data originating from machine sensors is crucial. However, installing multiple sensors on the object requiring diagnosis may be costly. A sensorless strategy using built-in signals to determine the conditions of a hollow ball screw was deployed. Moreover, we evaluated the most discriminative parameters among fusion sensor signals by using Fisher’ criteria. A support vector machine (SVM) as diagnostic tool was used. In the absence of prominent characteristic features in data, the conventional SVM cannot classify the data well. To address this concern, we constructed a feature engineering for distinguishing features from the raw data to facilitate the SVM classification process well. In addition, we validated the physical phenomenon in realistic ball screw conditions through feature extraction. Experimental results demonstrated the average diagnostic accuracy levels for the ball screw preload, pretension, cooling system, and table payload were 98.91%, 94.08%, 91.69%, and 93.5%, respectively, after feature engineering was applied successfully.

The accurate model of the load state for all balls under multidirectional load is very helpful for the design process of ball screws. The contact deformation model of the ball screw without consideration of the stress difference of all balls is inaccurate. In this paper, a novel contact load model of the ball screw is established by considering coupled axial, radial load to study the elastic deformation displacement and position accuracy. The deviation and variation of axial elastic deformation with the dimension errors of all balls are investigated to obtain the influence of load state on the precision sustainability of the ball screw. The position precision including travel deviation and variation by considering load distribution of all balls is studied under the different load conditions. In addition, a new working bench is designed to study the position precision of the ball screw. The experimental study is carried out to obtain the relationship between the position precision and the contact load state of all balls, which is a reference to compensate for the precision loss of the ball screw.

In the real industrial application, the problem of ball screw health condition monitoring and fault diagnosis is still confronted many challenges. In some cases, the rotating machinery has long rotor, it need to arrange multiple sensors at different positions of the system and different faults are located at different positions of the system. The primary difficult issue involved in the task is to recognize the multiple faults at different positions of ball screw with high accuracy and feasibility. In order to overcome the problem, a novel method for ball screw fault diagnosis is proposed. The proposed method combines weighted data of the multiple sensors at different positions with convolutional neural network and it considers the sensitive index of different faults at different sensors for weight assignment. The proposed method mainly contains three steps. Firstly, a new data segmentation algorithm is proposed to obtain the uniform data of the vibration signals. Secondly, a sensitive sensor data selection criterion based on ball screw failure mechanism is developed to obtain the sensor importance factor. Finally, the weighted data is classified by the convolutional neural network. The effectiveness of the proposed method is verified by the experiment on the ball screw test-bed.

PurposeThis paper aims to provide thermal elastohydrodynamic lubrication (TEHL) contact model to study all balls’ lubrication performance of the ball screw when the multidirectional load is applied.Design/methodology/approachA new TEHL contact model combining the multidirectional load and the roughness surface texture is established to describe fatigue life of the ball screw. Meanwhile, the authors use the Reynolds equation to study the lubrication performance of the ball screw.FindingsWhen the multidirectional load is applied, contact load, slide-roll ratio and entrainment velocity of all balls have a periodic shape. The TEHL performance values at the ball-screw contact points including contact stress, shear stress, minimum film thickness and temperature rise are higher than that at the ball-nut contact points. The TEHL performance values increase with the increase of root mean square (RMS) except for the film thickness. In addition, the radial load of the ball screw has a significant effect on the fatigue life.Originality/valueThe results of the studies demonstrate the new TEHL contact model that provides the instructive significance to analyze the fatigue life of the ball screw under the multidirectional load.Peer reviewThe peer review history for this article is available at:https://publons.com/publon/10.1108/ILT-03-2020-0097/

The static stiffness of machine tools plays an important role in ensuring the performance of machine tools and improving the machining accuracy. In the design stage, stiffness matching provides a theoretical basis for determining the stiffness of each component. This study proposed a stiffness matching design method for machine tools. First, the deformation model of machine tools is established on the basis of the state space model. Through force analysis, the relationships between the deformations and stiffness of each part are determined. Second, the stiffness model of the machine tool is established by combining these relationships with the deformation model. Third, the objective function is determined by performing sensitivity analysis after obtaining the stiffness range. The stiffness matching design results are obtained through multi-objective linear programming. Lastly, the stiffness simulations, theoretical model, and experimental results are analyzed. The maximum errors among the experimental, simulation, and theoretical results (18.8 % and 19.9 %) are within the acceptable range. In conclusion, the proposed method is suitable for designing ball screw feed drive systems that requires rigidity.

Thermally induced elongation of a screw is the primary cause of deterioration in the positioning accuracy of half-closed-loop screw systems in machine tools. The failure mode of ball-screw feed-drive systems in relation to the positioning accuracy is defined as the response error of the carriage exceeding the axial tolerance. It is still challenging to accurately predict the thermal errors using conventional methods because the randomness of the influencing factors is not considered. This paper presents an improved random thermal network model subjected to dynamic thermal excitation for calculating real-time transient temperatures of ball-screw systems. Furthermore, a time-varying reliability model is proposed for estimating the gradual reliability of the thermally induced positioning accuracy of ball-screw systems with random thermal boundary parameters using the random thermal network. A ball-screw system of a computerized numerical controlled lathe is used as an example to show the practical application of the proposed model. Repeated experiments demonstrate the accuracy retaining ability and robustness of this method. This work provides the theoretical and practical method for real-time monitoring of the positioning accuracy reliability index for ball-screw systems and it has high accuracy considering the random parameters.

Ball-Screw is a mechanical actuator that transforms the rotational motion of the motor into a linear motion of drive. The ball-screw is commonly used in industrial precision machines and due to this very reason it had been a common topic of research for many scholars, as precision in motion renders to precision in work directly. In this paper, some of the work done by different research scholars on different parametric aspects of ball screw had been discussed alongside optimization of ball screw diameter. The aspects which had been reviewed are elastic deformation & stiffness in ball screw, feed velocity and preloading, preload control in ball screw and various other aspects of preloading such as loss detection. Furthermore, the Finite elemental method had been applied to find the suitable diameter of the ball screw to render minimum deflection and stress value possible with the chosen material.

As one of the most important part of the machine tool, thermal behaviors of the ball screw feed drive system has great effect on the positioning accuracy and machining accuracy. Finite element method is widely used for simulating the temperature of the ball screw under various working conditions. In this paper, in order to improve the simulation accuracy, the moving heat loads are considered in the finite element model, and the response surface methodology is proposed to optimize the boundary conditions. The correctness and effectiveness of the method are verified by experimental data. In addition, according to the temperature simulation results of the ball screw feed drive system, thermal equilibrium is defined and the factors which have influenced on it are analyzed.

Ball-screw drives are one of the common wear parts in machine tools. The wear will affect the positioning accuracy of motion axes and can cause a high rejection rate in production. Therefore, it is interesting to develop a method, which can detect the wear condition of ball screws at an early stage. Stiffness loss and backlash in ball-screw drives are often associated with wear. This article proposes a novel method to detect stiffness loss and backlash for ball-screw drives without preload based only on machine internal data. To simulate the wear of a ball screw, a spacer sleeve has been designed, which effectively reduces the axial stiffness of the feed axes. This design allows a quick modification of the wear status of the machine axes and can be utilized to test if the wear status is detectable by analyzing the machine internal data. A multimass model is developed and validated by experimental data, which is used to simulate the dynamic behavior of the machine axis dependent on wear.

A dynamic performance analysis and optimization method of the horizontal machining center based on contact theory was presented in this paper. The contact stiffness models of ball screw joints and guide joints were proposed based on contact theory, and the predicted values are in good agreement with the experimental results. In particular, the prediction errors of the ball screw joints are within 2.8% compared with the 13 sets of experimental results of THK Company for 26 types. The analysis principle of dynamic performance was given by modal theory. With the goal of reducing the sum of modal flexibility and strengthening the weak modes, static flexibility analysis, modal flexibility analysis, and potential energy distribution analysis were carried out. By studying the effects of geometric parameters, material parameters, surface parameters, and assembly parameters on weak parts, the most effective parameters for optimization were determined. The sensitive parameters were improved from type selection, surface technology, assembly process, and structural improvement. The optimization results showed that the modal flexibility of the weak modes has been reduced by 51.80%, 38.44%, 46.58%, and 27.21%, respectively, the modal flexibility ratios have been reduced by 17.27%, 5.19%, 27.11%, and 2.45%, respectively, and the 1st, 3rd, 4th, and 7th natural frequencies of the horizontal machining center have increased by 26.64%, 11.38%, 4.14%, and 4.55%, respectively. The effects of various parameters on the horizontal machining center were also discussed, which can provide reference for further optimization and research.

Structural health monitoring (SHM) of the ball screw of machine tools relies on the repeated observation of the damage-sensitive features. The natural frequency is a characteristic inherent to the structure that is more robust than other signal features as a SHM index. However, a major problem is that regular changes in the operating conditions, such as the feed speeds, the worktable positions and other uncertain complex boundary conditions, also affect the natural frequencies, and these factors may mask the subtle variations induced by structural damage. Therefore, it is necessary to adopt an effective method to eliminate those effects to avoid false alarms during structural damage monitoring. In this article, Bayesian ridge regression modelling methods are developed to eliminate the effects of the worktable different positions and feed speeds on the natural frequencies. The proposed method can model the natural frequencies that are simultaneously affected by two dimensional operational factors and provide a rigorous quantitative assessment of the uncertainties associated with the complex boundary conditions of the ball screw drive system and the inevitable estimation errors. First, the different worktable positions and feed speeds were tested on the experimental bench to verify the proposed method. Then, two novel criteria were used as damage warning signals, which can reduce false alarm. Finally, this method was applied to monitor the wear of a ball screw of a CNC machining centre in an automobile factory. The validity of the method was provend using actual monitoring results.

This paper presents a novel electromechanical brake (EMB) with automatic wear adjustment function, which has been ignored in most previous studies. With the aim of investigating the parameter characteristics of the EMB system and analyzing the proposed function, the mathematical models of the DC motor, motor friction, worm drive, ball screw and load are established in MATLAB/Simulink. The differences between the simulation and the experiment under step, triangular wave, square wave and sinusoidal input signals are discussed. The parameter characteristics of clamping force-motor current and clamping force-ball screw displacement are obtained, which also verifies that the established model is correct. The results demonstrate that the automatic wear adjustment function of the new EMB could guarantee that the brake stroke and response time remain consistent across each braking manoeuvre.

Ziel der Instandhaltung von Werkzeugmaschinen ist die Verfügbarkeitsabsicherung, um hohe Bauteilqualität und Produktivität zu gewährleisten. Die Kenntnis des Vorschubachsenzustandes, im Besonderen des Kugelgewindetriebs, ist daher von enormer Wichtigkeit. Das „Guard-Plus“-System, eine Entwicklung der August Steinmeyer GmbH und der ifm diagnostics GmbH, ermöglicht dies durch Detektion des Vorspannungsverlustes aufgrund von Verschleiß. Untersuchungen zur Systemfunktion wurden in diesem Beitrag durch das wbk Institut für Produktionstechnik durchgeführt. The aim of machine tool maintenance is to ensure availability in order to guarantee high product quality and productivity. Knowledge of the wear condition of feed axes involved, especially the ball screw, is therefore of enormous importance. The Guard Plus system, a joint development of August Steinmeyer GmbH and ifm diagnostics GmbH, enables this by detecting the preload loss due to wear. Investigation of the system’s function are presented through wbk Institute of Production Science in the following article.

Die Zustandsüberwachung von Anlagen, Maschinen und deren Bauteilen ist eine zentrale Thematik von Industrie 4.0. Unvorhergesehene Ausfälle von Werkzeugmaschinen sind häufig auf den Verschleiß und das daraus resultierende Versagen von Kugelgewindetrieben zurückzuführen. Aufgabe dieser Arbeit ist die frühzeitige Detektion von Oberflächenschäden auf der Kugelgewindetriebspindel mit einem elektromechanischen Kamerasystem in Kombination mit Deep-Learning-basierten Modellen, um entsprechende Wartungsmaßnahmen abzuleiten. Condition monitoring of plants, machines and their components is a central topic of Industry 4.0. Unforeseeable failures of machine tools are often caused by wear, resulting in failure of ball screws and subsequent surface disruptions. This article describes how image-based monitoring of ball screws by an electronic camera system in combination with deep learning-based models enable the early detection of surface disruptions and to derive appropriate and preventive maintenance measures.

This paper deals with the precision modelling of the ball screw unit’s thermal behaviour in the turning centre and its impact on the tool head positioning error. The error components along controllable axes X and Z are described in detail using an FE model integrating the changes in thermal and force loads and deformations occurring during the motion of the nut as a heat source. The impact of the nut work cycle on the thermal deformations of the ball screw and the displacements of the slideways and the screw points along both the axes and on carriage positioning precision is demonstrated.

Automated Guided Vehicle (AGV) is the material handling equipment that is utilized broadly in a maximum manufacturing company nowadays as it gives more flexibility to the system. The fundamental perception of AGV includes driverless and battery-powered vehicles with programming abilities for path selection and locating. They are equipped to navigate a flexible guide-path network, which might be simply changed and extended. This project may be concentrating on model, unloading and loading mechanism for AGV have been required to define specific criteria, which is automatic operated, lightweight and capable of transporting acrylic. This unloading and loading system worked utilizing ball screw as a development system and suction cup with a vacuum pump as adhering instrument. The outline and fundamental material to manufacture this AGV model would utilize aluminum to decrease the AGV weight. The AGV application in a real-world application is supported human and decreasing price in repetitive movement transportation actions. This manuscript suggests a survey on the control and design of AGV frameworks. We locate many key-related problems incorporating guide-path design, vehicle scheduling, assessing the number of vehicles, battery management, conflict determination, idle-vehicle positioning, and vehicle routing. Furthermore, we suggest a decision system for the design and execution of AGV frameworks, and propose few fruitful study directions. It will be designed for an importantly less price and exorbitant efficiency. The expenditure of the overall build is Rs. 90,000/- which is cost worthy.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board, particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, a clustering algorithm using a deep neural network core is developed. Time series are encoded into pictures to be fed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihoods, without prior knowledge. It was then compared to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As the state-of-the-art indexes were not producing relevant results, a new indicator was built to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

Being a critical factor affecting the motion accuracy of precision machine tools, structural thermal elongation of precision ball screw unit is generally caused by the comprehensive influence from heat generations of screw-nut pair / bearings and time-varying ambient temperature. To resist 2 thermal disturbances above to guarantee precisely the original length of screw shaft, an active coolant control strategy is proposed in this paper. This strategy is based on an empirical model: For the slender and long tubular structure of screw shaft, the screw shaft temperature is approximately equal to its recirculating coolant temperature. The reason is that the intensive forced coolant convection is capable of eliminating screw shaft temperature rises caused by friction heat generations and ambient air convections. Based on this premise, screw coolant temperature can be consistently controlled by an active strategy, further to correct the thermal elongation of screw shaft. It can be experimentally verified that the thermal variations of machine positioning accuracy caused by the active coolant control strategy are not more than 10µm, which are lower than traditional strategy. Besides, based on detected structural temperatures of precision ball screw unit, the empirical model above is further proved to be reliable by FE simulation method.

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components. This seriously impedes the practical application of data-driven diagnosis. In comparison, the full-labeled machine signals from test rigs or online datasets can be achieved easily, which is helpful for the diagnosis of real equipment. Thus, we introduced an improved Wasserstein distance-based transfer learning method (WDA), which learns transferable features between labeled and unlabeled signals from different forms of equipment. In WDA, Wasserstein distance with cosine similarity is applied to narrow the gap between signals collected from different machines. Meanwhile, we use the Kuhn–Munkres algorithm to calculate the Wasserstein distance. In order to further verify the proposed method, we developed a set of case studies, including two different mechanical parts, five transfer scenarios, and eight transfer learning fault diagnosis experiments. WDA reached an average accuracy of 93.72% in bearing fault diagnosis and 84.84% in ball screw fault diagnosis, which greatly surpasses state-of-the-art transfer learning fault diagnosis methods. In addition, comprehensive analysis and feature visualization are also presented.

Ball screws are the driving component used to convert rotational motion to linear motion for precision equipment and may be applied with the turning torque in practice. In past research, the contact load between ball and the raceway is assumed to be uniform wherein only the axial load is considered. This paper aims to establish a load distribution model to analyze the contact load of all balls when a turning torque caused by assembly error is applied. The axial dynamic deformation model is obtained to study the effect of the elastic creep on transmission accuracy. The authors use the kinematic analysis method to obtain the load distribution and axial elastic deformation considering the geometric errors of balls. The relationship between the introduced deformation coefficient and the defined geometric error coefficient are analyzed, and the change trend of the maximum deformation coefficient varying with geometric error coefficient is obtained. Furthermore, the position deviation and position precision of the ball screw are studied, and the fatigue life of the ball screw is analyzed based on the maximum contact load. The results show that the load distribution model considering geometric errors with the effect of turning torque proposed in this paper is a satisfactory reference to study the precision sustainability of ball screw.

High fidelity models of feed drive are critical factors to increase positioning accuracy and decrease contour error. To predict feed drives dynamics, this paper reports a novel method for modeling dynamics of feed drive by combining advantages of theoretical derivations and deep learning. First, the paper derives a rigid-flexible-combined dynamics model (RFCDM) for feed drive from classical dynamics theory. Then parameters identification of RFCDM is accomplished by referring product manuals and conducting constant velocity experiment with different feed rates. Continuous action reinforcement learning automata (CARLA) is adopted to tune all parameters of RFCDM simultaneously. A simulation error estimation model (SEEM) is applied to approximate simulation error between models simulation position and worktables actual position. The hybrid dynamics model (HDM) of feed drives which integrates RFCDM with SEEM is validated by experiments with various trajectories. Experimental results show that the gap between HDMs prediction position and worktables actual position is on the order of magnitude of 0.01 mm which is about 1/10 of the tracking error, indicating the HDM can predict the dynamics of feed drives with safe accuracy.

A spindle feed system is continuously subjected to cutting forces, cutting heat and frictional heat during machining processes, which interact to form a complex, nonlinear and thermal-force coupling field. The coupling field causes deformations and vibrations of the mechanism that affect the machining accuracy. Therefore, determining the thermal-force coupling dynamic characteristics of the mechanism is beneficial, as it lays the theoretical foundations for accurate error compensation and the design and optimization of the spindle feed system. The deformation of the spindle feed system in actual working processes is approached by modeling, simulation and verification. Based on the theory of mechanics and heat transfer, the models for the transient milling force and the temperature field are established to obtain the real-time, dynamic cutting force and the associated heat values. A thermo-mechanical coupling model is also deduced, and the deformations generated by the cutting force and heat are simulated using the finite element method. Moreover, the thermo-mechanical coupling deformations of the system are emulated. The above deformation results are tested and confirmed. This paper establishes the deformation rules under the cutting force and cutting heat as well as their associated coupling effects. When studying thermo-mechanical coupling, the initial deformations of the ball screw are dominated by the cutting forces. The effects of heat on the X-direction deformations gradually exceed the effects of the cutting forces. The Y-direction deformations are primarily caused by the forces, with little influence from the heat. The Z-direction deformations are mainly caused by the heat, with negligible influence from the forces.

In this paper, we propose a method to monitor the preload of an operational ball screw drive system mostly used in CNC machine tools by evaluating the natural frequency of the screw nut in the axial direction and the corresponding working table displacement. First, we derived a discrete dynamic model and used it to study the variation in the ball screw system preload. Second, we proposed a method for monitoring the ball screw preload in operational mode via the motor current signal and the screw nut axial vibration signal. We apply this method by acquiring and analyzing the signals from the system as it undergoes a transition in its motion, then compose the frequency response function (FRF) of the ball screw system. This enables us to detect the axial natural frequency of the ball screw drive system. The ball screw preload is then calculated and monitored as the system is operational. We provide an efficient and intuitive method to monitor the variation in preload level of an operational ball screw system. This method can be used as an indicator of the health status of the drive system in particular, as well as for the entire machine tool in general.

This paper aims to investigate the load distribution and contact stiffness characteristics of the single-nut ball screw pair (SNBSP). First, the transformed relationship of coordinate systems is established. Then, the whole rolling elements load distribution model of the SNBSP is presented. Based on this, the whole rolling elements contact stiffness model is obtained. Applying the Newton–Raphson iterative method to solve the model, the normal force of rolling elements and the contact angles between balls and raceway surface are determined. The calculation results are reasonably consistent with those of the half pitch model. Then, the local contact stiffness and global contact stiffness are obtained. Furthermore, the effects of axial load and structural parameters of the SNBSP on the normal contact force, contact angle, and local and global contact stiffness are discussed using numeric analysis. Finally, a dynamic model of the z-axis feed system with time-varying axial stiffness is established, and the accuracy of the model is verified by experiments.

The positioning precision of the ball screw system in machine tools is primarily affected by the thermal deformation of the screw. Hence, the adaptive prediction method must be established to calculate thermal errors of the ball screw system. First, based on heat transfer theory, a new adaptive moving thermal network model of the screw system with moving thermal excitation and varying operating conditions is proposed. The time-serial data of kinematic-pair representative temperatures and the time-serial position data of the moving nut are used as inputs. This adaptive model is used to determine the temperatures of the key elements in the supporting bearings and those of the moving nut and key points of the screw shaft. Further, the heat generation rate of each kinematic pair can be obtained simultaneously. Furthermore, a real-time prediction method of thermal errors of the screw system with moving thermal excitation under various operating conditions is presented. The effectiveness and robustness of the real-time prediction method of thermal errors was verified experimentally. This method is suitable for the rapid prediction of thermal errors of screw systems by monitoring the real-time kinematic-pair representative temperatures and real-time position of the moving nut.

To enable the smart technologies and safe operation of transit and rail transportation, such as hot box detector, track health monitoring and wireless communication on the railroad side, a cost-effective energy source is in need. This paper presents the design, modeling, in-lab experiment and field-test results of a compact ball-screw based electromagnetic energy harvester with a mechanical motion rectifier (MMR) mechanism for smart railway transportation. The MMR mechanism is realized by the embedded one-way clutches in the bevel gears, which converts the bi-directional track vibration into the unidirectional rotation of the generator. Compared to previous designs, the proposed harvester has reduced backlash and thus can harvest energy from a small input of the track deflection induced by the moving train. Two prototypes with different key design parameters were built and tested. A comprehensive model considering the train-rail-harvester interaction was developed to analyze the dynamic characteristics of the coupled system and predict the energy harvesting performance of the harvesters at different train speeds. Both in-lab and field tests were carried out to examine the energy harvesting performance of the harvesters and validate the model. Field test results illustrated that an average power of 1.12 W and 2.24 W were achieved for two prototypes respectively when a Type A rapid transit passed by with a 30 km/h vehicle speed.

A common problem in motion control is the onset of vibrations due to the inertial forces when a compliant load is being displaced. It is the case of the vibration of large machine tools, but also the vibration of slender loads in pick&place. The main goal of the present work is to predict and minimise the overshoot that a compliant load moved by a servodrive suffers when it reaches the final position. Instead of modifying the control algorithm or adding dampers, the authors propose an optimal path planning based on the selection of a square sine velocity profile, a standard in machine tools NCs. Thus, a 3 degrees of freedom mechatronic model that integrates the dynamics of the drive-transmission-compliant load system and the control has been developed to predict the overshoot. Analysing the velocity profile, it has been possible to determine several values of the commanded jerk that minimize the overshoot and are related to the natural frequency of the compliant load. The prediction of the overshoot magnitude as well as the optimal jerk values have been experimentally validated in a workbench with a single axis with a ball screw transmission and a compliant load where a laser interferometer has been used to measure the overshoot.

Double-nut ball screws bear the action of bidirectional pretightening force, leading to the deformation of the contact area between the ball and the raceway. Under this condition, it is important to analyze and calculate the static stiffness of the ball screw. However, the conventional calculation method is inaccurate. Hence, a new method for the static stiffness analysis of a double-nut ball screw is proposed. Through the structural analysis of the ball screw and internal load distribution, a load deformation model was established based on the Hertzian contact theory. Through the load analysis of the ball screw, a static stiffness model of the ball screw was established and applied to a case study and a finite element simulation. The rigidity of THK double-nut ball screws used in the X-axis feed system of a high-stiffness heavy-duty friction stir welding robot (developed by the research group) was calculated. When the workload was lower than 1.1 × 104 N, the slope of the double-nut static stiffness curve increased significantly with the increase in the workload, and when the workload was greater than 1.1 × 104 N, its upward slope tended to stabilize. The simulated and experimental stiffness curves were in good agreement; when the external axial load was greater than 2.8 × 104 N, the stiffness value calculated using the finite element method gradually converged to the theoretical value; and when the axial load reached 3.0 × 104 N, the simulation and test curves matched well. The analysis method of the double-nut ball screw was found to be concise and accurate, and the stiffness curves calculated using the two methods were consistent. The simulation analysis of the static stiffness presented herein is expected to aid the design of double-nut ball screws of high-rigidity heavy-duty equipment.

Ball screws used for high-speed feed systems generate friction heat, which affects the preload and supporting system stiffness, and further, the dynamic characteristics of the screw system are modified and negative effects on the positioning accuracy are produced. Therefore, a thermal dynamic model for investigating dynamic performance variation with temperature is needed. First, a new dynamic temperature model of a hollow cylinder with varied heat flow was proposed based on the heat transfer theory. By using the thermocouples to read the real-time surface temperatures of heat sources, this model can be used to obtain the real-time temperature field of the supporting bearings and nut, and by using the FOCAS function to read the real-time position of nut, a finite difference heat transfer model with real-time moving heat sources was established. Based on this model, the dynamic thermally induced preload and stiffness models of the system were obtained. Second, a real-time thermal dynamic model of the ball screw system was achieved. Finally, an inverse identification method of the heat excitation was proposed. Through measuring dynamic characteristics of the feed system in machine tools, the real-time thermal dynamic models of the screw system were validated. It provided the theoretical and practical foundation for real-time monitoring and controlling of dynamic characteristics of preloaded ball screw systems.

The accurate prediction and test of the dynamic characteristics of the ball screw feed system are of great significance to the design, manufacture and improvement of the machine tool. At present, the dynamic modeling of ball screw feed system mostly ignores the influence of assembly on the dynamic characteristics of the system. In order to solve the above-mentioned problems, this paper studies the machine tool with both ends fixed ball screw, considering the influences of the pre-stretching of the lead screw on the joint of the screw and the nut. With the lead screw divided into three sections, the lumped parameter model of the ball screw feed system is established, and the influences of the lead screw pre-stretching on the natural frequency of the system are analyzed. The designed auxiliary clamping device is used to measure the frequency response of the system, which includes the translational motion and the rotational motion. The results of the experiments show that the established dynamic model can predict the dynamic characteristics of the system well. The torque wrench and the designed adaptor are used to adjust the pre-stretching of the lead screw, and the frequency response of the system are tested under different pre-stretching conditions of the lead screw. Both theoretical and experimental results show that the pre-stretching of the lead screw has a significant effect on the contact state of the screw-nut joint, which makes the natural frequency of the system change obviously.

Ball screw mechanism (BSM) plays a key role to transmit motion and power in a machine tool. The accuracy of the machine tool is greatly influenced by the accuracy of the BSM. Since accuracy degradation of BSM is primarily due to wear, the wear behavior of BSM is studied considering the time-varying motion and loading working conditions. A new wear prediction model of BSM is introduced in this research considering complex working conditions. Because BSM is subject to dynamic fluctuations of rotational speed and loading in the operation process, a loading model is established to describe the dynamic contact force between the ball and the raceway, and a motion model is developed to describe the dynamic sliding and rolling behaviors between the ball and the raceway. The degradation of BSM accuracy is analyzed considering time-varying motion and loading working conditions. Experiments have also been designed and conducted to demonstrate the effectiveness of the accuracy degradation analysis for BSM.

A real-time wear measurement system for tripod type Constant Velocity (CV) joints is proposed in this study. Adhesive wear on tripod type CV joints is very crucial to the life time of CV joints but it is very difficult to measure the wear depth of a tripod type CV joint separate from the drive shaft assembly. The wear measurement system developed in this study can measure the wear depth of a tripod type CV joint in real-time as it applies kinematics and forces to the CV joint. In order to apply the same motions as the motions of an actual CV joint, kinematics of the CV joint has been analyzed. As a result, reciprocating stroke and yaw motions have been completely implemented in the system. The wear measurement system comprises four parts. The first part is for applying normal forces to a set of spherical roller and housing track. One servo motor, spur gear and ball screw sets were employed to generate and augment the normal forces. The second and third parts are for applying reciprocating stroke and yaw motions, respectively. These parts synchronize with each other to make the same motion profiles as the actual CV joint. The fourth part is for measurement. This part consists of load cell and rotary encoder to measure the surge normal forces caused by the flaking phenomena and the wear depth caused by the adhesive wear. In the case of the encoder, the wear depth is augmented by the ball screw and spur gear sets so 0.18°/μm of measurement resolution can be achieved. For the validation of the system, a tripod type CV joint was selected, and its wear depth was measured by the system and the results were compared with the wear depth measured by a profilometer.

Precision locknuts are widely used in rotary machinery especially the machine tools operating in high speed with high precision. The flank-locking locknut studied in this paper is mainly employed in spindle and ball screw assemblies of machine tools to secure the preloading in the bearings required for better structural stiffness. In service the locknuts often suffer corrosion from the harsh machining environment. The possible effects of corrosion on the surface morphology of locknut, such as flatness and roughness, and its performance were investigated. The locknut was submerged in a 5% NaCl solution following ASTM B895 standard for different durations- 1, 2, and 4 h to speed up the possible corrosion. The treated locknut was then undergone assembling test following ISO 2320 standard. The flatness and roughness on the end face and threads were measured along with microscopic examination. It was found that with increasing duration of corrosion, the surface flatness and roughness became worse. However, the tightening constant of the locknut showed a positive correlation with the increased surface roughness raised by the corrosion treatment.

High-speed machining is a key technology for greatly improving productivity and reducing production costs. Angular contact ball bearing is the key component of ball screw feed system. Predictive analysis of bearings thermal behavior is significant to ensure the working accuracy of machine tools. Due to the continuous dynamic changes under actual working conditions, it is difficult to predict and model of the bearings heating characteristics. This paper develops a comprehensive calculation method for predicting the thermodynamic characteristics on high speed feed shaft bearings. Based on quasi-static model and thermal dynamic model, Monte Carlo optimization algorithm is used to inverse the temperature field experimental temperature data. The real heating characteristics of heat source are comprehensively predicted and analyzed, so as to accurately predict the bearing thermal dynamic performances of feed shaft bearings. The research work in this paper lays a strong foundation for the thermal error modeling and thermodynamic analysis of the ball screw feed system of machine tools.