Machine Tool Structure Research Articles

Stiffness design and multi-objective optimization of machine tool structure based on biological inspiration

This paper proposes a novel method to design the internal stiffeners layout of the supporting parts of a machine tool. This method skillfully adopts the natural growth law of leaf veins. Firstly, the similarity between the growth law of leaf veins and the layout of stiffeners in the supporting parts is dealt with in detail. The mechanical properties of different growth types of leaf veins are compared and analyzed by the finite element simulation. In addition, the optimality of the adaptive growth law of leaf veins is also analyzed. Then, a parameter optimization method of stiffeners layout based on a variable cross-section structure is proposed. Distinct from the traditional design of stiffeners layout, the method proposed in this paper not only adopts the idea of a variable cross-section structure, but also simulates the adaptive growth law of leaf veins through a parametric method. In the process of establishing a meta model, three modeling methods are compared, and the excellent performance of the genetic algorithm and BP neural network (GABPNN) method in meta model accuracy is determined. And a marine predator algorithm is implemented to optimize the meta model. Finally, the method proposed in this paper is applied to the design of the column part of a machine tool, and the effectiveness of the proposed method is verified by simulation and experiments, which provides a good idea for the design of stiffeners layout of the supporting parts of a machine tool.

The process correlation interaction construction of Digital Twin for dynamic characteristics of machine tool structures with multi-dimensional variables

Digital Twin (DT) has become the foundation for intelligent manufacturing as a breakthrough application technology framework and can solve complex vibration problems in the machining process. However, this technology is mainly aimed at application development in a specific domain or a particular factor. The process correlation and interaction characteristics of the system have not been reflected, thus making it challenging to capture the dynamic characteristics of the machining process. This paper presents a system-oriented DT framework with process correlation interaction mechanism that integrates multiple models for dynamic process modeling. The data model is constructed as the core driving model of DT. Based on structural dynamics theory, the weak machine components are taken as digital threads of the data model to correlate position variables and cutting excitation variables. Then, the Frequency Response Function (FRF) corresponding to the principal mode of vibration at the weak machine component is used as an intermediate data for overall online process performance characterization and DT model interaction to enable mirror synchronization of the operation process. The results show that the presented DT can provide multifunctional applications such as cutting parameters optimization, process correlated variables visualization, and machining stability assessment.

Multibody dynamic modeling of five-axis machine tools with improved efficiency

Predicting the dynamics between the tool and the workpiece is crucial for simulating the control performance and vibration of a machine tool as it changes its pose during machining. This paper presents a computationally efficient multibody dynamic model to simulate the pose-dependent dynamics of machine tool structures from the reduced-order elastic models of its substructures. The flexible substructures are assembled with a Jacobian-based kinematics formulation at the joints without needing to use Lagrange multipliers and constraint equations to define the five-axis dynamics of a machine tool, leading to a significant reduction in the number of unknowns. The frequency response of the machine is quickly updated as the machine changes its kinematic configuration and position by adjusting the sliding and rotating joint parameters in a geometric Jacobian matrix instead of calculating a new set of joint forces between the substructures. The order of the multibody model is further reduced by only including the most dominant vibration modes of the machine tool which are identified based on their contributions to the total kinetic energy. Identification of the most dominant modes is carried out directly on the multibody model of the assembled machine tool instead of individual full-order substructure models which substantially increases the number of excluded vibration modes. The proposed multibody model is demonstrated to predict the frequency response of a five-axis machine tool with a 90% faster computation than the existing multibody models based on reduced-order substructures.

A novel method for machine tool structure condition monitoring based on knowledge graph

A novel method for machine tool structure condition monitoring based on knowledge graph

Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance

Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance

Data-Driven Thermal Deviation Prediction in Turning Machine-Tool - A Comparative Analysis of Machine Learning Algorithms

Thermal error significantly impacts the machining precision of machine-tools. Thermal deformations in the machine-tool structure caused by the various machine heat sources is at the origin of this phenomenon. In order to ensure the expected quality of the parts, manufacturer have to run the machine-tools for hours before start producing in order to reach the machine thermal stability. This heating phase has a high negative impact on the machine productivity on one hand and on its ecological footprint on the other. This paper presents a data-driven approach to model and predict the thermal error in order to correct the tool reference position accordingly. The automatic adjustment of tool position allows to produce parts with the expected quality and precision regardless of the thermal state of the machines, which substantially increase their productivity. For this purpose, temperature sensors as well as high precision tool position measurement instruments are deployed on a Tornos SwissNano4 machine-tool. A set of experiments are conducted to collect data related to these two measurements. Four major Machine Learning algorithms are trained using a subset of the collected data and tested with the remaining data subset. Quantitative and comparative analysis shows that three of the four algorithms have a prediction with a mean Absolute Error (MAE) below 1µm and a Correlation Coefficient higher than 90%. Even classical linear regression models are able to predict the thermal error with high accuracy. Advanced Machine Learning techniques show high potential to provide a better prediction accuracy.

Reliability assignment of a heavy-duty CNC machine tool spindle system based on fault tree analysis

Research on the distribution of machine tool reliability indexes is of great significance to improve the reliability of machine tools in the design and manufacture stages. Owing to the complex structure of heavy-duty CNC machine tools, multi-source and heterogeneous failure causes, etc., the traditional single method cannot get accurate results. In this paper, based on the FTA method, a three-level machine tool fault tree model is established to carry out research on reliability index assignment. Then, as the levels progress, the distribution of reliability indicators is based on probability importance analysis, reliability redistribution method, and AHP respectively. Finally, the top-down reliability assignment results are obtained. After analysis, the structure obtained by combining multiple reliability allocation methods is more in line with the actual situation. According to the results, the weak parts of the machine tool are designed and improved, thereby improving the reliability of the machine tool.

Analysis of Dynamic Characteristics for Machine Tools Based on Dynamic Stiffness Sensitivity

Dynamic parameters are the intermediate information of the entirety of machine dynamics. The differences between components have not been combined with the structural vibration in the cutting process, so it is difficult to directly represent the dynamic characteristics of the whole machine related to spatial position. This paper presents a method to identify sensitive parts according to the dynamic stiffness-sensitivity algorithm, which represents the dynamic characteristics of the whole machine tool. In this study, two experiments were carried out, the simulation verification experiment (dynamic experiment with variable stiffness) and modal analysis experiment (vibration test of five-axis gantry milling machine). The key modes of sensitive parts obtained by this method can represent the position-related dynamic characteristics of the whole machine. The characteristic obtained is that the inherent properties of machine-tool structure are independent of excitation. The method proposed in this paper can accurately represent the dynamic characteristics of the whole machine tool.

METHOD FOR SAFE EXPERIMENTAL TESTING OF MACHINE TOOL USABLE SPINDLE POWER

The regenerative chatter during milling is a consequence of the machine tool dynamic compliance and the specific setting of the cutting process. The basic request on the machine structure is to enable a stable cut with dominant portion of the installed spindle power. In order to improve the dynamic properties of newly-designed machine tools, the machine tool structures are optimized using various advanced simulation methods and are made of various advanced materials. The final design should be tested experimentally. The test of usable spindle power provides important feedback on the machine tool design. The testing of large machine tools is time consuming due to the significantly varying machine dynamic compliance dependent on the working space. Due to the installed high power and large diameter tools used, the occurrence of chatter might be dangerous with a high risk of machine tool damage. The article presents a method for the safe and time-effective experimental testing of the machine tool usable spindle power. The method is based on the experimental identification of the cutting force coefficients and the experimental identification of machine tool behavior with the support of extrapolation models based on identified experimental data. The dynamic compliance is measured within a rough network of measurement points in the machine working space. The measured dynamic compliance results are characterized by a model based on modal identification data. Only a few cutting tests are done for the cutting force coefficient identification. Finally, a map of the usable spindle power is calculated. This approach makes it possible to calculate the results with respect to the real machining process and the current machine tool conditions across the whole working space in an acceptable time. The approach is demonstrated on a real case study.

Design of Regenerative Flutter Stability Detection System in Machine Tool Cutting

Cutting chatter is a strong relative vibration between cutting tool and work piece in machining process, which will reduce cutting quality and cutting efficiency, and shorten the service life of cutting tool and machine tool. As long as cutting is carried out in production, vibration will occur, and chatter is a strong self-excited vibration between machining work piece and cutting tool. Flutter problem will occur in almost all cutting processes, which will cause a series of problems such as the reduction of dimensional accuracy of machined work pieces, tool damage and so on. In the dynamic design and dynamic analysis of machine tool structure, in order to evaluate and improve the ability of machine tool to resist chatter, and to select the cutting conditions without chatter, it is necessary to judge the cutting stability of machine tool. How to improve the advanced technology of manufacturing industry is an important topic for manufacturing researchers, and the research on the detection of cutting chatter stability has important practical significance for promoting the development of cutting manufacturing industry to high-end technology.

Machining process analysis of multi-tasking machine tools based on form-shaping motions

With the increase in machine tool types, it becomes difficult to select an appropriate machine tool to achieve high-efficiency and high-quality machining. This study aims to automate the selection of machine tools by evaluating and comparing machining processes for the machining of targeted parts. A fundamental method for analyzing machining processes is proposed by introducing the functional description of machine tools in the previous study. However, a simple machine tool structure is supposed as a first step in the proposed method. Therefore, in this study, the machining process analysis method is reconstructed to handle complex machine tool structure such as that of a multi-tasking machine tool. The functional description of machine tools is expanded to represent form-shaping motions of a multi-tasking machine tool by adding new suffixes that indicate the orientation of plural spindles and the posture of non-rotational specific functions. Additionally, evaluation indexes of rationality and similarity of machining operations to exclude unsuitable machining operation candidates are proposed. Case studies are conducted and confirmed that the system developed based on the novel proposed method can derive accountable machining processes according to a multi-tasking machine tool. The results of the machining process analysis may thus support the machine tool selections.

Influence of the positioning accuracy given by the positioning encoder of the CNC machine tools

The CNC machine tools are present at a large extent worldwide, for machining by cutting all kinds of work pieces. Their machining accuracy and quality are crucial to obtaining very accurate and intricate work pieces. The accuracy of a CNC machine tool is given by the quality of each and every kinematic linkage/coupling in its structure. This work aims at researching the influence of several constructive and functional factors on the positioning accuracy of a kinematic coupling in the structure of a CNC machine tool. The influence caused by noncompliance with the Abbé principle on the positioning accuracy of a feed kinematical linkage is analysed into detail, both at idle and load running. Out of this research useful conclusions are resulting for the CNC machine tool designers and builders, with respect to optimising several constructive factors, such as the location of the position encoder, location of the ball screw, stiffness of the directional guideways etc., in order to improve the accuracy of the CNC machine tool. The results of the theoretical research have been validated through experimental trials, confirming the impossibility of full compliance with the Abbé on the CNC machine tools.

Condition for machining feasibility for a five-axis machining center

Recently, five-axis machining centers with non-orthogonal rotation axes have been developed to achieve a high stiffness and large machinable area. Although such machine tools can expand the manufacturing field, adopting not only orthogonal axes, but also non-orthogonal axes, requires generalization of conventional form-shaping theory. Moreover, since it is not possible to imagine simultaneous five-axis movement of machine tools with non-orthogonal axes, it is important to analyze the validity of the developed machine tools with non-orthogonal axes using generalized form-shaping theory by a structure analysis method. In the present paper, generalization of the form-shaping function is first performed, and the conditions whereby machining centers with non-orthogonal axes can achieve every position and posture are clarified for an index showing the validity of the machine tool structure. Finally, the machining feasibility is verified for a machine tool structure with non-orthogonal axes using the derived conditions.

Dynamic modeling and experimental research on position-dependent behavior of twin ball screw feed system

To establish the dynamic model of machine tool structure is an important means to assess the performance of the machine tool structure during the cutting process. It is necessary to study the dynamics of the machine tools in different configurations for the sake of analyzing the dynamic behavior of the machine tools in the entire workspace. In this paper, a robust approach is presented to build an efficient and reliable dynamic model to evaluate the position-dependent dynamics of the twin ball screw (TBS) feed system. First, the TBS feed system is divided into several components, and a finite element (FE) model is built for each component. Second, the Craig-Bampton method is proposed to reduce the order of the substructures. Third, a multipoint constraints (MPCs) method was introduced to model the mechanical joint substructures of the TBS system, and the spring-damper element (SDE) is employed to connect the condensation nodes. Finally, a series experimental tests and full-order FE analysis are conducted on the self-designed TBS worktable in the four positions to validate the effectiveness of the proposed dynamic model. The results show that the proposed approach evaluates accurately the position-dependent behavior of the TBS system.

Position error reduction of tool center point in multi-tasking machine tools through compensating influence of geometric deviations identified by ball bar measurements

A method to compensate the influence of geometric deviations on tool center point (TCP) for a multi-tasking machine tool is proposed in this paper. Some methods to compensate geometric deviations of a rotary axis in five-axis machining centers have been proposed. However, due to the special topological structure of multi-tasking machine tools, the identification and compensation methods for geometric deviations are different from those of the five-axis machining centers, which have been seldom researched until now. In this paper, the main attention is paid to analyze the eccentricities of the trajectories measured by a ball bar under simultaneous three-axis motions and to reduce the influence of the identified geometric deviations on the position error of TCP by the compensation method. It is divided into two sequential subtasks. At first, the geometric deviations are identified by using the eccentricities of measured trajectories. A simple and practical measuring procedure is proposed to identify geometric deviations of rotary axes existing in a multi-tasking machine tool. For the second step, a method is proposed by modifying the original NC code according to the kinematic chain model of the targeted machine tool to compensate the influence of the existing geometric deviations on TCP. An experiment is conducted on a multi-tasking machine tool with a swivel tool spindle head in the horizontal position. The repeatability of the measured eccentricities based on three experimental results is also investigated to reduce the influence of measuring error on the identified results. As a result, the corresponding values of geometric deviations after the compensation are less than 2.2 arcseconds or 2.4 μm. It is concluded that the influence of geometric deviations on TCP is compensated effectively, and the position error of TCP is reduced significantly.

Reconstruction of cutting forces through fusion of accelerometer and spindle current signals

Analysis of cutting forces is critical for understanding, control and optimization of machining operations. Table-dynamometers are the most common tools to measure the cutting forces directly. Workpiece geometry restrictions, high cost, altered dynamics, and limited frequency bandwidth are challenges associated with the direct measurement of the cutting forces using table-dynamometers. In this study, utilization of cost-effective spindle current sensors along with accelerometers is proposed to indirectly predict the cutting forces at the tool tip during milling operations. First, receptance coupling (RC) method is used to obtain dynamics between the tool tip and the accelerometers which are located on the spindle housing. The application of the RC method requires a series of impact hammer tests along with finite element simulations to identify the dynamics of machine tool structure. The measured accelerations are then integrated in frequency domain to obtain displacement of the spindle housing where the accelerometers are attached. A Kalman filter is then designed and applied to the displacement signals to reconstruct the AC component of the cutting forces. Next, the spindle current signals measured through the hall-effect sensors are processed to acquire the tangential and radial cutting forces. Finally, due to different frequency bandwidth of the measured signals through the accelerometers and hall-effect sensors, the reconstructed cutting forces are fused to improve the cutting force measurement accuracy. The proposed method is also verified by conducting a series of half and full-immersion milling tests, and the reconstructed results are compared to the measurements of a reference piezoelectric-based table-dynamometer. Results show that the proposed sensing scheme can predict the cutting forces effectively only at a fraction of the cost of traditional table-dynamometers without affecting overall workpiece geometries.

ENHANCEMENT OF SPECIALISED VERTICAL TURNING LATHE ACCURACY THROUGH MINIMISATION OF THERMAL ERRORS DEPENDING ON TURNING AND MILLING OPERATIONS

Achieving high workpiece accuracy is a long-term goal of machine tool designers. There are many causes of workpiece inaccuracy, with thermal errors being the most dominant. Indirect compensation (using predictive models) is a promising strategy for reducing thermal errors without increasing machine tool cost. A modelling approach using thermal transfer functions (a dynamic method with a physical basis) embodies the potential to deal with this issue. The method does not require interventions into the machine tool structure, uses a minimum of additional gauges and its modelling and calculation speed is suitable for real-time applications with fine results with up to 80% thermal error reduction. Advanced machine tool thermal error compensation models have been successfully applied on various kinds of single-purpose machines (milling, turning, floor-type, etc.) and implemented directly into their control systems. This research reflects modern trends in machine tool usage and as such is focused on the applicability of the modelling approach to describe specialised vertical turning lathe versatility. The specialised vertical turning lathe is adequately capable of carrying out turning and milling operations. Calibration of the reliable compensation model is a real challenge. The applicability of the approach during immediate switching between turning and milling operations is discussed in more detail.

Research on the commonness and dissimilarity of group machine tools based on BP and SAE algorithms

The use of group machine tools has become one of the important production methods for large-scale manufacturing of high-precision parts. Due to the impact and wear of the group machine tool for long-term machining, the dynamic characteristics of the machine tool structure change significantly, resulting in a decrease in product quality; at the same time, the random errors in manufacturing and assembly make the group machine tool structure dynamics more different. Studying the dynamic differences of group machine tools will help improve the processing of group machine tools. This paper proposes a deep learning algorithm based on Back-propagation (BP) neural network and Stacked Auto Encoder (SAE) network to study the dynamics of the machine tool structure under static and dynamic conditions. The BP neural network is used to train the characteristics of some machine tools, and the dynamic characteristics of other groups of machine tools are predicted by changing the parameters. Then SAE network is used to find out the influence rule of group machine tool structure, changing on group machine tool dynamic characteristics. Experiments have proved that this method has certain significance for the study of dynamic characteristics of group machine tools.

Design and development of a five-axis machine tool with high accuracy, stiffness and efficiency for aero-engine casing manufacturing

In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design, key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual drive swing head and a compact motorized spindle are designed with enough drive capability and stiffness, and related structure, assembly method, cooling technology, and performance simulation are given in detail. Next, a design method of supporting stiffness of guide is proposed through the deformation prediction of the spindle end. Based on above work, a prototype of machine tool is developed, and some experiments are carried out, including performance tests of swing head and motorized spindle, and machining of a simulated workpiece of aero-engine casing. All experimental results show that the machine tool has satisfactory accuracy, stiffness and efficiency, which meets the machining requirements of aero-engine casing. The main work can be used as references for engineers and technicians, which are meaningful in practice.

Optimization of Process Parameters of Epoxy Granite for Strength and Damping Characteristics Using TOPSIS Method

Abstract The main requirements for machine tool structures are higher damping, stiffness, and dimensional stability and low thermal expansion coefficient. Compared with cast iron (CI), stone-based polymer composite provides improved damping characteristics, because of which it is being considered as an alternate material for machine tool structures in recent research. In this work, process parameters of epoxy granite (EG) composite were optimized using the technique for order preference by similarity to ideal solution (TOPSIS) method to obtain optimum strength characteristics. The effect of process parameters, namely curing time (A), aggregate mass fraction (B), aggregate size mix (C), curing temperature (D), and stirring speed (E) on static and dynamic characteristics of EG composite were investigated. An analysis of variance test was performed to identify the significant process parameters with a confidence level of 95 %. The predicted process parameters are verified through confirmatory tests, which showed an improved preference value of 0.0948. To obtain optimum strength properties, the recommended optimum process parameters are found to be A = 12 h, B = 0.8, C = aggregate size mix 1, D = 40°C, and E = 90 r/min. Experimental modal analysis also revealed that the damping factor of EG composite with aggregate mass fraction B = 0.8 is 10 times higher than that of CI. Morphological analysis of EG composite using a field emission scanning electron microscope showed that granite aggregates are uniformly distributed with better epoxy bonding characteristics.