5-axis Machining Research Articles

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.

Block processing time-based programming for high-speed, high-precision 5-axis machining

For high-speed and high-precision machining with orthogonal 3-axis minute line segment commands, a program should be generated by appropriately reducing the chord error (tolerance) or the block length (line segment length) based on the computer numerical control (CNC) block processing time Tb. The block processing time Tb is the duration from when the CNC decodes one block command in a program until when it controls the motors, depending on the processing power of the CNC. Accordingly, even in simultaneous 5-axis machining, it is necessary to research whether programs generated based on Tb can achieve high-speed and high-precision machining. Therefore, this study aims to realize simultaneous 5-axis machining at higher speed and precision using CNC programs with minute line segment commands based on Tb. In addition, we propose a more general identification method compared with that used in previous studies to identify Tb. Simultaneous 5-axis programs include two cases of control, namely, tool center point (TCP) control and Non-TCP control, and Tb was identified for both controls. For the machining method, using the block length based on Tb, we generated programs to machine the upper flat surface of a workpiece using simultaneous 5-axis machining while rotating the two rotary axes using a ball-end mill. We machined the workpiece and evaluated the machining speed and precision of the machined surface. We showed that simultaneous 5-axis machining with higher speed and precision can be achieved using programs based on Tb. In addition, we analyzed and considered the reasons for the higher-speed and higher-precision machining.

Evaluation of a telescopic simultaneous ballbar in a 3-axis machine tool using a reference equipment

This paper outlines the evaluation methodology for a Telescopic Simultaneous Ballbar (TSB) developed for distance measurement using an interferometer as a reference equipment. The procedure is executed in a 3-axis machine tool, one of the final applications of the TSB that is machine tool volumetric verification. Two distinct evaluation techniques are devised, with the new virtual line approach enabling an assessment of the TSB's performance within an extended verification volume and requiring only a single alignment position for the interferometer. The measurement repeatability of the equipment is assessed and its measurement uncertainty is estimated in workshop conditions. The results reveal the flexibility of the TSB measurement process similar to that of a laser tracker and the degree of measurement accuracy, which shows values comparable to the interferometer. Consequently, the TSB could facilitate faster and more accurate machine tool verification and calibration compared to existing systems.

A FE Model for the Efficient Simulation of the Thermo-Elastic Machine Tool Behavior

In modern machine tools, the thermo-elastic positioning error has a large share on the overall machine tool error. A multitude of different approaches can be found in the literature to correct this error in order to improve the accuracy of the machine tool. However, an FE-based approach to model the thermo-elastic error of 5 axis machine tools is still an active field of research. Therefore, the goal of this work is the improvement of the machine tool accuracy by modelling the machine tool error using a FE model. The model uses real time measurement data of the thermo-elastic error using an R-Test, as well as real time temperature measurements of the machine structure.

Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication.

Accurate prediction of five-axis machining cycle times with deep neural networks using Bi-LSTM

This paper presents a novel machine learning (ML) based approach to predict machining cycle (part program running) times for complex 5-axis machining. Typical 5-axis machining toolpaths consist of short-segmented discrete linear cutter location lines (CL-lines) with simultaneously varying tool center point (TCP) and orientation vectors (ORI). As the 5-axis machine tool NC (numerical control) system tries to interpolate such part programs smoothly, the TCP motion decelerates and accelerates repeatedly causing the actual feedrate to fluctuate. The actual observed (resultant) feedrate can be approximately 30 % lower than the user commanded one. Furthermore, if the toolpath requires the 5-axis machine tool to travel closer to its singular point, or if the workpiece placement on the table is not optimal, actual feedrate is even lowered further. This paper presents two ML-based approaches to accurately predict 5-axis machining toolpaths. The first strategy uses a bidirectional long short-term memory (Bi-LSTM) network to model the machine tool behavior and generates a direct final cycle time estimation for any given part program. This approach only uses the toolpath geometry to predict the cycle time, and for training it only needs the “final” cycle times of similar part programs making it practical on today’s shop floors. The second strategy requires individual CL-line processing times for training. In return, it can provide highly accurate cycle time predictions. Both strategies can capture the interpolation dynamics of 5-axis machine tool NC systems accurately. Simulation studies and experimental validations are conducted on modern 5-axis machine tools with varying workpiece placements and interpolation parameters. Proposed approaches have shown to predict cycle times with 90–95 % accuracy on real-life complex 5-axis machining toolpaths.

Promoting Machining Efficiency of 5-Axis Machine Tools with Non-Contact On-Machine Measurement Function

Promoting Machining Efficiency of 5-Axis Machine Tools with Non-Contact On-Machine Measurement Function

Industrial IoT system for laser-wire direct energy deposition: data collection and visualization of manufacturing process signals

Industry 4.0, also known as the Fourth Industrial Revolution, is a term used to describe the current trend of automation and data exchange in manufacturing and other industries. The Internet of Things (IoT) plays a crucial role in Industry 4.0 by connecting devices, machines, and products to the Internet and enabling real-time data exchange. Moreover, additive manufacturing is a key developing manufacturing technology in Industry 4.0. New technologies such as data analysis with Artificial Intelligence and machine vision are widely used in optimization. However, in a lab environment, these technologies depend on the data collected from the process. For such work, the researchers should be able to focus on their core research rather than on the development of infrastructure to collect and analyse the data. This research presents an open software and hardware IoT solution to monitor a laser wire direct energy deposition system installed in a cartesian type 3-axis machine tool. The IoT solution adopts three open-source tools for core issues, such as 1) interoperability, flexibility, and availability; 2) data storage; and 3) data visualization of sensor data and manufacturing process signals. The system architecture is based on one or more edge devices connected to sensors and forwarding their data toward a local API endpoint. The endpoint is created with Node-RED, an open-source visual flow-based development tool for IoT data. Node-RED forwards the data to an open-source InfluxDB database. Finally, the data is visualized with an open-source Grafana application. The system is prototyped, designed, implemented, and tested in a lab environment to monitor a laser-wire direct energy deposition process. The significance of such a flexible IoT data collection system for research and development projects can be integral. Thus, providing savings in time and money can substantially speed up the development of new technologies where the value arises from the sensor data and its analysis.

ESTIMATION OF MACHINE TOOL ACCURACY BASED ON MONTE CARLO SIMULATION USING MOVEMENT AXES GEOMETRIC ERRORS MODEL

Accuracy is one of the most important feature of a machine tool. Being able to estimate the overall accuracy of a machine tool represented by the total volumetric error can be a huge advantage in the design process. This paper provides overview of methods used for geometric error transfer modeling and volumetric error modeling. An error transfer rigid body model is derived and after that, a random linear guideway straightness error profile generator is proposed. This allows to simulate a movement axis with random straightness errors of rails and computed geometric errors of an assembled axis. Volumetric error of a 3-axis machine tool is then modeled using its kinematic scheme and homogenous transformation matrices with 21 kinematic errors. Using simulated axes as input to the volumetric model, an entire machine tool can be simulated. From that, the total volumetric error can be obtained and a Monte Carlo simulation carried out. This allows to analyze statistics of the total volumetric error and with slight input changes (rail straightness tolerance), a sensitivity analysis can be performed to determine the most influential errors in the machine tool.

Accurate Detection and Smoothness-Oriented Avoidance Method of Singularity in 5-Axis CNC Machining

As an inherent flaw in the kinematic chain mechanism of 5-axis machine tools, singularity can induce dramatic changes in machine axes motion and unfavorable fluctuations in feedrate. For effective singularity avoidance, it is desirable to first achieve accurate and efficient singularity detection and then eliminate the singularity without impairing tool orientation smoothness. This paper presents a novel approach for accurately detecting and smoothly avoiding the singularity in 5-axis CNC machining. In the detection method, two exclusion criteria are presented to efficiently exclude most non-singular segments of the tool orientation spline, and a curve intersection-based algorithm is thus developed to accurately identify the singular segments. In the singularity avoidance method, a concept of admissible tool orientation annulus (ATOA) is introduced, which serves to confine the range and magnitude of the tool orientation spline’s adjustments, and a local adjustment algorithm is then developed to enable the escape of the tool orientation from the singular region with controllable direction and deviation, while maintaining its continuity and smoothness. The effectiveness of singularity avoidance and the kinematic performance of the tool orientation modified by our method, are comparable to a state-of-the-art singularity avoidance algorithm. Finally, the conducted experiments validate the proposed method.

Effects of Boom-Tip Control and a Rotating Cabin on Loading Efficiency of a Forwarder

Climate change and associated heat waves and droughts are causing enormous amounts of damaged wood in Central Europe. To face these challenges, mechanized timber harvesting systems consisting of single-grip-harvesters and forwarders are commonly employed due to their high productivity and work safety. Despite the advantages of these work systems, the operation of advanced forestry machines requires lengthy training and entails high levels of mental strain for machine operators. In recent years, operator assistance systems have been installed in forest machines with the intention of reducing mental workload of machine operators, thereby improving productivity. However, knowledge of the actual effect of operator assistance systems on productivity is still lacking.The present case study surveyed the effect of two recently released operator assitance features, Intelligent Boom Control (»IBC«) and a rotating cabin (»RC«), on productivity during loading cycles, by means of a time study. Therefore, IBC and RC were tested in different loading settings using a forwarder, John Deere 1210G. Three loading angles were tested (55°, 90° and 125° azimuthal and counterclockwise to the machine axis) in combination with five loading distances (4 m, 5.5 m, 7 m, 8.5 m, and 10 m distance from the crane pillar). The 15 loading positions were sampled using four variants (I: IBC off RC off, II: RC on IBC off, III: IBC on RC off, IV: IBC on RC on), capturing 10 replications for each position and variant, resulting in 600 loading cycles in total.When the operator was not supported by any system, mean time consumption per loading cycle amounted to 20.6 ± 0.114 sec. The utilization of IBC resulted in a significant reduction in time consumption of 2 seconds per loading cycle. Moreover, further time savings were observed when IBC was engaged in combination with a rotating cabin, leading to a mean time consumption of 17.8 ± 0.114 sec (or 14% improvement) per loading cycle. Although the lowest time consumption was observed when IBC and RC were engaged, the use of RC alone did not show any significant time improvements.Since loading activities occupy approximately 50% of the total cycle time in timber forwarding, potential time savings within this work element are crucial for further improvements of work productivity. This pilot case study quantified the time savings when IBC and RC were engaged during loading in an experimental setting. The results can be used as a basis for further investigations dealing with factors influencing the productivity of highly mechanized timber harvesting systems.

The Pivotal Role of 5-Axis Machining

The Pivotal Role of 5-Axis Machining

Thermally compensated 5-axis machine tools evaluated with impeller machining tests

Thermally compensated 5-axis machine tools evaluated with impeller machining tests

On C0 and C1 continuity of envelopes of rotational solids and its application to 5-axis CNC machining

We study the smoothness of envelopes generated by motions of rotational rigid bodies in the context of 5-axis Computer Numerically Controlled (CNC) machining. A moving cutting tool, conceptualized as a rotational solid, forms a surface, called envelope, that delimits a part of 3D space where the tool engages the material block. The smoothness of the resulting envelope depends both on the smoothness of the motion and smoothness of the tool. While the motions of the tool are typically required to be at least C2, the tools are frequently only C0 continuous, which results in discontinuous envelopes. In this work, we classify a family of instantaneous motions that, in spite of only C0 continuous shape of the tool, result in C0 continuous envelopes. We show that such motions are flexible enough to follow a free-form surface, preserving tangential contact between the tool and surface along two points, therefore having applications in shape slot milling or in a semi-finishing stage of 5-axis flank machining. We also show that C1 tools and motions still can generate smooth envelopes.

Development of Low-Magnetic Susceptibility Microcoils via 5-Axis Machining for Analysis of Biological and Environmental Samples.

In environmental research, it is critical to understand how toxins impact invertebrate eggs and egg banks, which, due to their tiny size, are very challenging to study by conventional nuclear magnetic resonance (NMR) spectroscopy. Microcoil technology has been extensively utilized to enhance the mass-sensitivity of NMR. In a previous study, 5-axis computer numerical control (CNC) micromilling (shown to be a viable alternative to traditional microcoil production methods) was used to create a prototype copper slotted-tube resonator (STR). Despite the excellent limit of detection (LOD) of the resonator, the quality of the line shape was very poor due to the magnetic susceptibility of the copper resonator itself. This is best solved using magnetic susceptibility-matched materials. In this study, approaches are investigated that improve the susceptibility while retaining the versatility of coil milling. One method involves machining STRs from various copper/aluminum alloys, while the other involves machining ones from an aluminum 2011 alloy and electroplating them with copper. In all cases, combining copper and aluminum to produce resonators resulted in improved line shape and SNR compared to pure copper resonators due to their reduced magnetic susceptibility. However, the copper-plated aluminum resonators showed optimal performance from the devices tested. The enhanced LOD of these STRs allowed for the first 1H-13C heteronuclear multiple quantum coherence (HMQC) of a single intact 13C-labeled Daphnia magna egg (∼4 μg total biomass). This is a key step toward future screening programs that aim to elucidate the toxic processes in aquatic eggs.

The Next Step for Growth

Subcontractor Wellington Engineering has progressed to 5-axis machining with investment to help it meet diverse demands from a diverse range of sectors

Set Up for The Future

Manufacturing leader drives aerospace firm ASG TGM to double turnover after million-pound 5-axis machine tool investment

Virtual Dynamics Model for 5-axis Machining of Thin-Walled Blades

Abstract The five-axis ball-end milling dynamics of thin-walled blades is presented. The cutting forces are predicted from the ball end mill–blade geometry engagement maps along the tool path. The Frequency Response Function (FRF) of the thin-walled blade is predicted using Finite Element shell elements, and it is updated along the toolpath as the metal is removed. The predicted cutting forces are applied on both the workpiece and tool FRFs to predict the forced vibrations and chatter stability at each tool location. A simplified method to update the cutter–workpiece engagement (CWE) is used to obtain the three-dimensional stability lobe diagram at each desired point on the blade. The integrated model is used to simulate the 5-axis machining of thin-walled blades in the digital environment. The proposed digital model is experimentally validated by machining a series of thin-walled rectangular plates and a twisted fan blade.

Experimental study of geometric error of CNC turning machine tools based on ISO 13041-6

The product quality of machining results is greatly influenced by the accuracy and precision of CNC lathe machine tools. Regular inspection of the geometric inaccuracy of the machine tool is necessary to verify its operational viability. This research contribution focuses on conducting experimental studies to evaluate machine tool geometric error. The aim is to explore cost-effective measurement methods as alternatives to direct measurements, which often involve laser interferometers and ball bar tests. The objective of this study is to investigate the geometric inaccuracy of a CNC turning machine by conducting experimental cutting tests in accordance with ISO 13041-6:2009. The testing will utilize conventional workpiece forms and requirements, including circularity features, flatness, circular features, and maybe combination features. Several geometric errors that can be acquired with this method include circularity errors, linear positional errors, and squareness errors. The cutting test for each workpiece feature of the given shape and specification requires the use of 5 specimens. Consequently, the mean value of the geometric error may be computed. The geometric error value is derived by the analysis of measurement data collected from a Coordinate Measuring Machine (CMM) applied to a specimen of the machined workpiece. Moreover, the evaluation of the geometric error condition of machine tools is ascertained through the comparison of the average data for each category of geometric error against the permissible standard values given in ISO 10791-2, ISO 10791-4, and ISO 13041-4. The findings of the study indicate that the implementation of the object machine study is not viable for the production of machined workpieces of satisfactory quality. This is primarily due to the presence of geometric errors in CNC turning that exceed the acceptable tolerance levels. Specifically, these errors manifest as linear positional deviations along multiple coordinates along the X-axis and Z-axis, as well as squareness deviations between the X-axis and Z-axis. The maximum value of the linear positional error along the X-axis is 55.2 μm, while the maximum value of the linear positional error along the Z-axis is 25.6 μm. Additionally, the greatest observed squareness error is 37.3 μm. The X and Z machine axes exhibit deviations beyond acceptable limits in terms of unidirectional accuracy and unidirectional repeatability, as per the established norm

Design and manufacture of a large field of view thermal infrared catoptric imaging system in an αZ configuration.

A geometry of a catoptric imaging system using three mirrors in an AlphaZ configuration is presented. This geometry allows large field of view and large aperture catoptric systems, useful for optronic pods as an example. A proof of concept with an 18×24 degree full field of view and a F/1.5 aperture is built using slow tool servo and 5 axis machining and characterized in the long wave infrared domain. The built system achieves nearly diffraction limited performance.