Industrial Machine Tools Research Articles

Modelling and optimization method for energy saving of computer numerical control machine tools under operating condition

The widespread application of machine tools in industry results in very high energy consumption. In order to save energy, it is an effective means to reduce the processing power while improving the processing efficiency of machine tools. Since milling parameters directly affect machine performance, the selection of suitable milling parameters is particularly important. In this regard, this study proposes an integrated modelling and optimization method for energy saving of computer numerical control (CNC) machine tools under operating condition. Firstly, the operating condition of the CNC machine are analyzed and the processing power and efficiency mathematical models are established based on the four milling parameters (n, fz, ae, ap). Subsequently, the Pareto optimal solution is introduced to establish an optimization model integrating the processing power and time. To solve the optimization problem, an adaptive chaotic multi-objective chimp algorithm with fast convergence and satisfactory optimization is proposed considering three aspects: search space, convergence factor, and chaotic mapping. Afterwards, further degrees of freedom and sensitivity analyses were carried out on the constructed model using the Sobol method. Finally, comparative analysis and validation are conducted through simulation cases and actual processing experiments, respectively. The results show that both validation scenarios have the same conclusion. Compared with three existing algorithms, the proposed method reduces the processing power by 5.63 %, 4.65 % and 4.51 % and improves the processing efficiency by 27.88 %, 15.11 % and 15.31 %, respectively. Based on the above enhancements, the energy consumption is reduced by 58.21 %, 45.54 % and 41.45 % respectively.

Multivariable Iterative Learning Control Design for Precision Control of Flexible Feed Drives.

Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment.

Voice User Interface based control for Industrial machine tools

To meet productivity goals, machining operations require high levels of efficiency to achieve the desired manufacturing quality in the available time. Additionally, machine tools can lead to safety-critical scenarios due to the high process dynamics and hazardous forces and torques. Therefore, workers need to exhibit special care while interacting with certain kinds of machines. Operational safety can be increased by eradicating the dependence on tangible control of machine tools. Current developments in Artificial Intelligence, especially in natural language processing, lead to increased utilization of speech-based interaction between humans and machines. However, these developments are mainly limited to areas of consumer electronics. Nevertheless, this type of human-machine interaction offers great potential for machining operations. Artificial neural network architectures like Transformer have rapidly emerged as the dominant architecture for natural language processing, overtaking alternative neural architectures such as Convolutional and Recurrent Neural Networks in performance for natural language understanding and natural language generation tasks. The architecture scales with training data and model size, enables efficient parallel training, and captures wide-ranging sequence features. These advancements can be used for enhancing the ease of use and safety of machine tools, which are often safety critical. In this article, a Voice-User-Interface based control of machine tools is conceptually outlined. Such a control can increase the efficiency of the workers, as it eradicates the need of hand usage. It further increases the safety of workers operating the machines and can detect anomalous sounds corresponding to machine failures during the machining process. The voice is first passed through a speech-to-text pipeline for converting the speech signals to relevant text, the text output is mapped to machine commands and passed to the physical machine. The implementation of the setup guarantees the speech-based control – even for safety-critical tasks -of machine tools. Moreover, this paper outlines the benefits and challenges of the developed VUI architecture.

FROM THE TRADITIONAL MULTILATERATION TO THE INTEGRATED MULTILATERATION APPROACH: A ROADMAP TOWARDS THE AUTOMATIZATION OF VOLUMETRIC ERROR MAPPING PROCESS FOR LARGE MACHINE TOOLS

Multilateration-based volumetric error mapping is gaining widespread adoption in the coordinate measurement machine and machine tool industry as the most effective approach for geometric characterization of large-size machines. In the traditional method, tracking interferometers are placed on the machine table while the measurement retro-reflector moves with the spindle. However, this approach has several limitations, including manual instrument manipulation and a sequential measurement scheme that hinders unattended execution. To overcome these limitations, the integrated multilateration method has been introduced, which addresses these challenges and reduces measurement uncertainty. This method offers the potential for fully autonomous volumetric error mapping of large-size machines and enables the development of new functionalities such as traceable coordinate measurement with large machine tools. This research presents the roadmap followed by the authors to automate the volumetric error mapping process for large machine tools. It outlines the current status of the technology, starting from the conceptual development and culminating in the experimental results obtained from real industrial machine tools.

FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives

This paper presents an analysis of the advantages stemming from the application of field-programmable gate arrays (FPGAs) in servo drives used within the control systems of industrial numerical machine tools. The method of improving the control system that allows for increasing the precision of machining, as well as incorporating new functionalities and streamlining diagnostic processes, is described. As demonstrated, the utilization of digital controllers with robust computational power and high-performance real-time communication interfaces is essential for achieving these objectives. This study underscores the limitations of commonly employed digital controllers in servo drives, which are constructed based on microcontrollers or signal processors collaborating with application-specific integrated circuits (ASICs). In contrast, the proposed FPGA-based solution offers substantial computational power and significantly reduced latencies in the real-time communication interface compared to other examined alternatives. This enables the realization of the planned objectives, specifically the enhancement of technical parameters and diagnostic capabilities of machine tools. Furthermore, the research indicates that FPGA-based digital controllers exhibit relatively low power consumption and a simplified design of the electronic printed circuit board in comparison to other analyzed digital platforms. These features can contribute to heightened reliability and diminished production costs of such controllers. Additional conclusions drawn from the study indicate that FPGA-based controllers provide greater developmental possibilities and their production is marked by potential resilience to challenges associated with the availability of electronic components in the market.

Robust Control of Dual-Linear-Motor-Driven Gantry Stage for Coordinated Contouring Tasks Based on Feed Velocity

Contouring control is one of the most important tasks for the dual-linear-motor-driven gantry system (DLMDGS), whose control performance determines the in various precision machining equipment, mainly industrial machine tools. Nevertheless, there are some limitations in present results, including the insufficient consideration of system uncertainty and the approximation errors introduced by the computation of the path length. To overcome these issues, the effects of the rotational dynamic and uncertainties are studied and further refined in this article, and a robust control scheme with adaptive neural network identification is designed based on the desired contouring velocity. The method improves existing approaches and avoids the imperfection of the previous one, based on tracking control under global task coordinate frame. The effects of various environmental forces are considered and the B-spline wavelet neural network is employed to approximate unknown dynamic caused by the thrust ripples of the three motors. Some experiments on a real system verify the effectiveness and superiority of the proposed velocity-based contouring control method.

Model predictive control for compliant feed drives with offset-free tracking behavior

Industrial machine tool feed drives are predominantly controlled by cascade control due to their low tuning complexity and inherent robustness. However, the cascaded structure requires the inner cascades to have higher dynamics than the outer cascades, which limits the achievable dynamic accuracy. Direct control approaches, which substitute the position and velocity cascade, offer the potential to utilize the unused potential. A promising approach is model predictive control (MPC), which optimizes the manipulated variable with a plant model along a prediction horizon. However, model uncertainties between the nominal model and the real plant lead to tracking errors. Therefore, this paper presents, a linear MPC (LMPC) and an adaptive MPC (AMPC) with an additional integral action to robustly compensate for model mismatches. Both controllers use a compliant model, are real-time capable with a sample rate of {{2},textrm{kHz}} and consider state and input space constraints. The AMPC accounts for position-varying stiffness and friction. The controllers are experimentally compared with classical P-PI cascaded control on a ball screw drive. They show a tracking error reduction of {{37}{,%}} (LMPC) and {{44}{,%}} (AMPC) during a high speed motion profile and an increase in bandwidth of {{180}{,%}} (LMPC) and {{184}{,%}} (AMPC), resulting in significantly improved dynamic accuracy.

Proportional Derivative – Type Iterative Learning Algorithm for a Motion Control System

In this paper, Iterative Learning Control (ILC) combined with a Proportional Derivative (PD) regulator is proposed to deal with the problem of designing a control signal for motion control systems. The main idea in iterative learning control is to gradually improve the performance of the system by exploiting data from the previous iterations. The learning control algorithm can obtain a better tracking control performance for the next run and hence outperforms conventional control approaches such as Proportional Integral Derivative (PID) controller and feedforward control. The main area of application for ILC is control of industrial robots and CNC machine tool, printing, and other industrial applications. The learning algorithms can also be used in combination with other control techniques. For example, learning feedforward control is designed in the first iteration. Then iterative learning control is applied to improve performance in the subsequent iterations. In addition, the conventional feedback regulator is designed in combination with iterative control to deal with uncertainty. Simulation results demonstrate the potential benefits, sensitivity and robustness of the proposed method.

Scalable production of large components by industrial robots and machine tools through segmentation.

The production of large components currently requires cost-intensive special machine tools with large workspaces. The corresponding process chains are usually sequential and hard to scale. Furthermore, large components are usually manufactured in small batches; consequently, the planning effort has a significant share in the manufacturing costs. This paper presents a novel approach for manufacturing large components by industrial robots and machine tools through segmented manufacturing. This leads to a decoupling of component size and necessary workspace and enables a new type of flexible and scalable manufacturing system. The presented solution is based on the automatic segmentation of the CAD model of the component into segments, which are provided with predefined connection elements. The proposed segmentation strategy divides the part into segments whose structural design is adapted to the capabilities (workspace, axis configuration, etc.) of the field components available on the shopfloor. The capabilities are provided by specific information models containing a self-description. The process planning step of each segment is automated by utilizing the similarity of the segments and the self-description of the corresponding field component. The result is a transformation of a batch size one production into an automated quasi-serial production of the segments. To generate the final component geometry, the individual segments are mounted and joined by robot-guided Direct Energy Deposition. The final surface finish is achieved by post-processing using a mobile machine tool coupled to the component. The entire approach is demonstrated along the process chain for manufacturing a forming tool.

Open circuit fault diagnosis strategy of PMSM drive system based on grey prediction theory for industrial robot

Permanent magnet synchronous motor (PMSM) is widely used in industrial robot joints and machine tools due to its high torque density, good controllability, and compact structure. Automatic fault diagnosis and early warning of PMSM drive system of industrial robot is the key and difficult point for the smooth development of automatic production process. In view of the problems that the traditional open circuit fault diagnosis method is not fast enough, not intelligent enough, and prone to misdiagnosis in the case of load change, an open circuit fault diagnosis and location strategy for windings and power switches of PMSM drive system based on grey prediction theory is proposed. The grey prediction method only needs to collect a small amount of motor current data and uses the grey model to estimate and predict the rule of the system. It can still diagnose and locate faults accurately in the case of load change, and compared with the traditional current detection method, the diagnosis speed is fast, and the accuracy is high. Through the theoretical analysis, simulation, and experimental results, it is verified that proposed strategy can diagnose the power switch and winding open circuit faults accurately in real time, which improves the reliability of the electric drive system in the application of industrial robot.

Technological robot-Machine tool collaboration for agile production.

The flexibility and efficiency in parts production can be significantly increased through the technological cooperation of industrial robots and machine tools. The paper presents an approach in which a robot, in addition to the classic handling tasks, enhance machine tools by additional manufacturing technologies and thus beneficially supports workpiece machining. This can take place in various configurations, starting with pre- and final machining by the robot outside the machine, through sequential cooperative machining of the workpiece clamped in the machine, to parallel, synchronized machining of a workpiece in the machine. The approach results in a novel type of collaborative manufacturing equipment for matrix production that will improve the versatility, efficiency and profitability in production.

Combined Predictive and Feedback Contour Error Control With Dynamic Contour Error Estimation for Industrial Five-Axis Machine Tools

This article proposes a real-time method to control the contour error for industrial five-axis machine tools by combining the generalized predictive control (GPC) and the feedback correction (FBC) with dynamic contour error estimation (CEE). The CEE is developed by well considering the curve’s curvature and torsion based on Taylor series expansion and Frenet frame theory. By utilizing the spherical linear interpolation, the tool orientation and the tool tip position are synchronized with respect to the curve length. A novel dynamic foot point searching procedure is established to weaken the influences of the tracking errors’ magnitude on the CEE precision. To tackle the transmission effect, the GPC is adopted to predict the servo systems’ outputs, and then, the induced tool pose deviations, which are subsequently utilized to counteract the contour errors, are predicted by constructing Jacobian matrixes. Especially, the FBC loops are constructed to suppress the influences of disturbances and further to reduce the magnitudes of contour errors. Simulations and experiments are conducted to verify the effectiveness of the proposed methods.

Fabrication of polygonal Fresnel lenses with a rotating cutting tool on three-axis ultraprecision lathes

Fresnel lenses are gaining wider applications due to their advantages of being lighter, thinner and suitable for increasingly complex and miniaturized optical systems. To increase the fill factor when combining multiple Fresnel lenses to form an array, polygonal Fresnel lenses have been designed and machined with the ultra-precision machine tools in industry. However, for the three-axis machine tool equipped with the X-, Z-, and C-axes, it is difficult to machine the straight edges of the polygonal Fresnel lens due to the lack of a Y-axis. Although the synchronized servo motions of the linear axis and the spindle can be utilized to fit the relative motion trajectory of the tool to a straight line, the cutting tool rotates relative to the workpiece and thus the tool rake face is not always perpendicular to the direction of the relative motion during machining. In this paper, a high-precision rotating platform is integrated into the three-axis ultra-precision machine tool to make the cutting tool track the rotation of the spindle and keep the tool rake face perpendicular to the direction of the relative motion at all times. Then the tool trajectory is generated and the influence of the tool nose radius is compensated. Finally, an experiment of cutting the hexagonal Fresnel lens is performed on a three-axis ultra-precision lathe, and the machined workpiece is inspected with a white light interferometer. The experimental results show that the profile accuracy of the machined polygonal Fresnel lens satisfies the requirements in industrial applications.

Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

Time series data generated by manufacturing machines during processing is widely used in mass part production to assess if processes run without errors. Systems that make use of this data use machine learning approaches for flagging a time series as a deviation from normal behaviour. In single part production, the amount of data generated is not sufficient for learning-based classification. Here, methods often focus on global signal variance but have trouble finding anomalies that present as local signal deviations. The referencing of the process states of the machine is usually performed by state indexing which, however, is not sufficient in highly flexible production plants. In this paper, a system that learns granular patterns in time series based on mean shift clustering is used for detecting processing segments in varying machine conditions. An anomaly detection then finds deviating patterns based on the previously identified processing segments. The anomalies can then be labeled by a human-in-the-loop approach for enabling future anomaly classification using a combination of machine learning algorithms. The method of anomaly detection is validated using an industrial machine tool and multiple test series.

Serial Manipulator Time-Jerk Optimal Trajectory Planning Based on Hybrid IWOA-PSO Algorithm

Loading and unloading operations are widely employed in industrial robots and CNC machine tools. In the present study, an effective algorithm is established for optimum time-jerk path planning and reducing the vibration of the serial manipulator, and enhancing the robot efficiency. To this end, the manipulator’s trajectory is constructed using quintic B-spline interpolation in the joint space and the optimal objective function is constructed in terms of time and mean jerk. A hybrid improved whale optimization and particle swarm optimization (IWOA-PSO) method is proposed to optimize the objective function. First, WOA is improved by employing adaptive weight and threshold to balance exploration and exploitation of the WOA to prevent falling into the local optimum solution. Then, PSO method and IWOA techniques are combined to enhance the convergence speed of the IWOA. Applying different algorithms to 23 benchmark functions demonstrate superiority of the proposed algorithm to state-of-the-art algorithms. A 6-DOF serial industrial manipulator integrated in CNC machine was taken as a case study and the joint positions are considered the IWOA-PSO inputs. All calculations are performed in the MATLAB 2018b environment. The obtained results demonstrate that the proposed IWOA-PSO can effectively reduce the jerk of the robot while improving its work efficiency.

Technological innovation in the winery addressing oenology 4.0: Testing of an automated system for the alcoholic fermentation management

In recent years, the use of automated machine tools in the wine industry has increasingly gained ground to simplify and optimize winemaking, complying with Industry 4.0 requirements. This work aimed to analyse a system for the automatic management of yeast nutrition in alcoholic fermentation in terms of environmental, management, and economic performance in comparison with traditional fermentation management. The automated system is a transportable and easily installable place and start system, equipped with a control unit and rods for the dosage of nutrients, and it works with a memory unit in which fermentative kinetics curves are loaded. The curves are predefined or customized according to oenologists’ needs. Hence, fermentation time, manpower, nutrients, oxygen, water, and energy consumption were evaluated concerning the alcoholic fermentation process. The analysis was carried out considering two different Italian wineries with different working capacities. Furthermore, life cycle assessment methodology and variable costs analysis was performed. Overall, the automated system reveals to be a promising investment, especially if applied to wineries characterized by high-volume tanks, where scale factor played a crucial role. Nutrients used by the automated system are more expensive but more environmentally sustainable than traditional ones.

Interpolation and Extrapolation of Optimally Fitted Kinematic Error Model for Five-Axis Machine Tools

Abstract Machine tool geometric errors are frequently corrected by populating compensation tables that contain position-dependent offsets to each commanded axis position. While each offset can be determined by directly measuring the individual geometric error at that location, it is often more efficient to compute the compensation using a volumetric error model derived from measurements across the entire axis space. However, interpolation and extrapolation of measurements, once explicit in direct measurement methods, become implicit and obfuscated in the curve-fitting process of volumetric error methods. The drive to maximize model accuracy while minimizing measurement sets can lead to significant model errors in workspace regions at or beyond the range of the metrology equipment. In this paper, a method of constructing machine tool volumetric error models is presented in which the characteristics of the interpolation and extrapolation errors are constrained. Using a typical five-axis machine tool compensation methodology, a constraint bounding the tool tip modeled error slope is added to the error model identification process. By including this constraint over the entire space, the geometric errors over the interpolation space are still well identified. Also, the model performance over the extrapolation space is consistent with the behavior of the geometric error model over the interpolation space. The methodology is applied to an industrial five-axis machine tool. In the experimental implementation, for measurements outside of the measured region, an unconstrained model increases the mean residual by 40% while the constrained model reduces the mean residual by 40%.

Effect of electrode material on removal efficiency regarding single discharges in wire EDM

For the precise machining of demanding materials, wire electrical discharge machining (WEDM) is a flexible and often irreplaceable manufacturing process. In order to enhance productivity as the main focus of the wire EDM process, the advancement of the fundamental procedural understanding is of decisive importance. In order to be able to energetically evaluate the removal process, the individual energetic contributors of the process hence the individual discharges need to be understood in terms of their contribution to material removal. In this paper, an experimental setup is presented, which permits the generation of individual discharges on a modern industrial wire EDM machine tool. For three different wire electrodes, the correlation of the discharge energy and the individual removal volume is quantitatively described, showing that coated wires achieve a significantly higher energy-specific removal. Furthermore the removal efficiency is defined as a key figure to transfer the findings to the continuous process and compare theoretical and effective removal rate.

PENGARUH VARIASI PUTARAN SPINDEL TERHADAP GAYA POTONG PADA PROSES PEMESINAN

The turning process is one of the most important machining processes in the industrial world. In this process, a change in shape is carried out by removing some of the material in the form of industrial machine tools. Inappropriate selection of the shape/type of tool, workpiece material, and cutting conditions will affect the characteristics of the tool. In the study, it was found that the rotational speed was 90,110,155,190 rpm, with a depth of cut of 0.3 mm at a feed rate of 0.023 mm / rev against different specimens, namely Dural Aluminum (Al 2024), Pure Aluminum (Al 1100) using a chisel insert type DCGT 070204K. -10 and DCMT 070202 M-20 with conventional lathe type Emco Maximat V - 13 IP 54. Data retrieval uses three sensors, namely a load cell sensor for cutting force, and the photoelectric counter module for rotation (rpm). The results showed that each specimen tested had a different value in each test experiment, such as testing on Pure Aluminum (Al 1100) specimens with a cut depth of 0.3 mm and rotation variations of 90, 110, 155, and 190. rpm can be seen in DCMT tool that the cutting force is greater than the DCGT tool. The cutting force produced on the DCMT tool blade is 9.91 N with a rotation of 110 rpm and at 190 rpm the DCMT tool gets a force of 6.83 N. It can be concluded that at low rotation the cutting force that occurs will be greater because of the vibrations that occur bigger.

PENGARUH VARIASI MEDIA PENDINGIN OLI, DROMUS, MINYAK SAYUR TERHADAP KEKASARAN PERMUKAAN BAJA SS-400 PADA PROSES MESIN BUBUT KONVENSIONAL (LATHE MACHINE)

The development of increasingly sophisticated and modern technology, especially in the industrial field, both machine tools, power plants and metallurgy play an important role in the industrial world. As is the case for machine tools used in the machining process, including lathes, milling machines, drilling machines, scrap machines, grinding, and others. The lathe process is the process of forming a material by removing some of the material in the form of a snarl due to the relative motion of the tool to the workpiece, where the workpiece is rotated on the spindle and the chisel is delivered to the workpiece in a translational manner. The quality of the turning results, especially on the surface, is strongly influenced by three parameters, namely spindle speed (Speed), feed motion (feed), and depth of cut (Depth of Cut). As for other factors that support the quality of the turning results, including the workpiece, the type of chisel used and the cooling media actually have a significant influence, the cooling medium used is Oil, Dromus and Vegetable Oil, the chisel used by HSS and the depth of cutting (F) 0.1 mm and 0.2 mm. The percentage of the effect of variations in cooling media on the surface roughness of SS-400 steel in the turning process is as follows: Oil = 75% Dromus = 64% Vegetable Oil = 70% It can be seen from the above percentage that for now the cooling medium Oil is the most influential cooling medium. And better than Dromus and Vegetable Oil cooling media against roughness in the turning process.