Computer Numerical Control Machine Research Articles

Energy-Optimal Coverage Motion Trajectory Generation for Industrial Machines*

Coverage motion of industrial machines are widely used for manufacturing tasks such as milling, polishing, laser cutting, and inspection. Motion smoothness of industrial machines under their kinematic limits are crucial in coverage motion to increase the production efficiency and accuracy. Motion trajectory optimization for typical computer numerical control (CNC) machines plays an important role in achieving smooth motion. In addition, with the current increase in energy costs and environmental concerns, there is a great need to reduce energy consumption by industrial machines which are extensively used in manufacturing industries. This study presents an energy optimization approach for coverage motion of industrial machines, which simultaneously integrates trajectory generation and geometric path optimization. The trajectory along a linear segment is described by the modified S-curve profile with harmonic motion employed for smooth jerk continuity to enhance the motion accuracy. An energy consumption model of the feed drive system is used to achieve energy-optimal. A genetic algorithm is applied for the optimization. Experimental validation of the simulation results is carried out using a two-axis feed drive system. Simulation and experimental results shows that the energy saving of the feed drive system is achieved under machine kinematic limits ensuring smooth motion.

A Novel Evaluation Metric Based on Dispersion of Wear Distance for In Situ Tool Condition Monitoring

As the direct performer of the cutting process in computer numerical control (CNC) machine tools, the tool wear condition directly affects the product quality. However, the existing evaluation metrics, e.g., flank wear width and wear area, cannot accurately determine the wear form conversion. In this article, a novel metric based on the dispersion of wear distance is proposed to describe the flank wear forms in more detail. The wear distance refers to the distance projected from the flank wear edge to the main cutting edge. Then, the wear distance of the indirect contact on the tool–chip interface is filtered out by the normal distribution model. Finally, the rate of distance dispersion over time named RDDT is constructed to identify the time nodes during wear forms’ conversion. In the experiment of accelerating milling cutter life, the Mann-Kendall (MK) inspection statistics of the proposed metric is 3.74, and the correlation with the maximum flank wear is 99.58%. Compared with the existing evaluation metrics, RDDT can accurately and robustly identify the two time nodes where the flank wear form changes in the whole life cycle.

Self-Progressive Near-Field Focusing 2-D Full Frequency Scanning Slot Array Antenna Based on Ridge-Gap Waveguide

A millimeter-wave self-progressive near-field focusing (NFF) 2-D full frequency scanning ridge-gap waveguide slot array antenna is proposed in this article. In the proposed array antenna, the adjacent linear NFF leaky-wave antennas (LWAs) are connected end to end in a double layer folded structure to construct a self-progressive phase shift feed network. Thus, the desired high phase progression in the design of the 2-D frequency scanning is provided by the linear NFF LWAs themselves with the superposed propagation length. As a result, the NFF 2-D frequency scanning performance can be achieved without increasing antenna area based on the proposed high-density self-progressive feed network, which is built by applying the ridge-gap waveguide. The proposed antenna prototype is fabricated through the computer numerical control (CNC) machining process and is verified by the experiment. The test information of the object under test in the near-field region can be directly obtained with a certain frequency band, showing good promise in the near-field detection application.

Open-Source Hardware-Based Three-Dimensional Positioning Device for Indoor Measurement and Positioning

Indoor private (local) wireless networks are considered key enablers for the industrial Internet of Things (IIoT). To ensure that a private network satisfies its own requirements and guarantees coverage, it is important to accurately measure the radio performance at the accurate position to further optimize the network. Additionally, as carrier frequencies increase, cellular network-based positioning becomes increasingly important, as it enables more accurate positioning. For example, centimeter-level accuracy is expected in beyond 5G systems owing to the sub-THz band carrier frequency. To accurately compare and assess various cellular network-based positioning methods, positioning devices, which can move along the same trajectory more accurately than at the centimeter-level, are required. To this end, we implemented a three-dimensional (3D) positioning device developed from open-source hardware, which is generally used to implement computer numerical control (CNC) machines and 3D printers. The implemented 3D pointing device is cost-effective and portable and can be freely configurable and resized in one, two, and three dimensions owing to the use of open-source hardware. The received signal strength indicator (RSSI) employing the LoRa module can be obtained using the implemented device. Using a very low carrier frequency system, it is possible to accurately measure the RSSI variations due to multipath propagation. After demonstrating the reproducibility of the data, the effectiveness of the implemented device was confirmed by showing that the RSSI variation and fading correlation coefficient are close to the Rayleigh fading variation.

Hybrid Multi-Object Optimization Method for Tapping Center Machines

This paper proposes a hybrid multi-object optimization method integrating a uniform design, an adaptive network-based fuzzy inference system (ANFIS), and a multi-objective particle swarm optimizer (MOPSO) to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control (CNC) machines. First, rigid tapping parameters and uniform (including 41-level and 19-level) layouts were adopted to collect representative data for modeling. Next, ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data. In tapping center machines, the synchronization errors and cycle times are important considerations, so these two objects were used to build the ANFIS models. Then, a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously. The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the non-systematic method. Additionally, the optimal parameter combination was used to optimize existing CNC tools during the commissioning process. Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping. The position gain and pre-feedback coefficient can reduce the synchronization errors significantly, and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors. The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms; it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield, thereby helping factories achieve carbon reduction.

Accurate TCP Position and Orientation Trajectory Generation in 6DOF Robotic Manipulators and CNC Machine Tools using FIR Filtering and Haversine Synchronisation

The use of robotics in manufacturing is rapidly growing. One promising application in particular is the use of robots as machining platforms. Robotic machining offers many advantages such as large operating envelopes, flexibility in operations and low installation and running costs. Robotic machining platforms much like computer numerically controlled (CNC) machine tools are commanded by numerical controllers (NCs) using the well established G-code part programming language (ISO-6983). Toolpaths are defined in the work piece coordinate system (WCS) as a series of discrete tool centre point (TCP) positions and tool orientations. The role of the NC is to generate smooth feed drive or robot joint commands such that the tool or end effector travels along the commanded toolpath whilst satisfying TCP position and orientation tolerances and robot/machine tool kinematic constraints. Interpolating tool orientations directly in the WCS requires complex real-time spherical interpolation computations. Overcoming this challenge, this paper introduces a C4 continuous tool path trajectory generation method for 6DOF motion. The tool centre point position is smoothed in the WCS, however, the tool orientation is smoothed directly in the rotary coordinate system without the requirement for spherical interpolation. The interpolated tool orientation trajectory follows the path of the great circle arc using the modified Haversine method eliminating non-linear interpolation errors present in linear interpolation of rotary axis positions (machine coordinate system interpolation). The proposed method is validated by simulation of a 6DOF robotic manipulator.

A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data.

As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.

Adapting ‘tool’ size using flow focusing: A new technique for electrochemical jet machining

Electrochemical jet manufacturing methods exploit the localised interaction of an electrified jet with a conductive workpiece. This has been exploited by numerous researchers to deposit and remove material. In recent studies, the same approach has been used as an analysis tool to measure and evaluate engineering components. The capability of this manufacturing method is limited by the resolution, which is governed by the diameter of the jet. Although approaches have been taken to reduce the kerf or interaction volume in the process, it is the jet diameter still provides the fundamental limit. In this study, a new method is proposed which takes advantage of a constriction effect to reduce the jet diameter through flow focusing, which occurs in coaxial two-phase flows. A novel nozzle arrangement is presented which demonstrates jets can be constricted by 79% leading to 54% reduction in machined kerf width. The limitations of this method are investigated in the context of fluid dynamic constraints, identifying optimum operating regions to utilise the approach in a computer numerical control machine tool arrangement. This enables continuously varying tool size in-process, which is analogous to other energy beam processes where spot size can be adjusted with a corresponding response influence.

ANFIS multi-tasking algorithm implementation scheme for ball-on-plate system stabilization

This paper presents the design and realization of a ball-on-plate system using a 3-degree-of-freedom parallel robot controlled by an adaptive neuro-fuzzy in-ference system. The ball-on-plate system is nonlinear, multivariable, with an under-actuated feature. Initially, the parallel robot is designed using SolidWorks and mechanized using a computer numerical control machine. Followed by the presentation of the ball-on-plate system mathematical model and the simplified model obtained. Afterwards, the inverse kinematics are performed to derive the appropriate angle for each servomotor. Eventually, the controller is designed and implemented in a double loop feedback scheme. A comparison between the proposed controller and a conventional proportional–integral–derivative controller in terms of time response, overshoot, and steady-state error is carried out. Furthermore, a comparison between sequential and asynchronous parallel processing is conducted for two different scenarios. The first scenario is when moving the ball to the origin while the second is for disturbance rejection. Simulation and experimental results show that the adaptive neuro-fuzzy inference system implemented using asynchronous parallel processing improves the real-time system stability by considerably decreasing oscillations as well as enhancing the ball movement smoothness with a small stead-state error.

Methodology for the development of augmented reality applications for the elimination of errors in the interpretation of manufacturing drawings

Nowadays, precision machining processes have been widely used in the manufacture of products mainly focused on aerospace, automotive, mold manufacturing and various types of products that demand high production volumes, precision and surface quality. However, these manufacturing processes are not exempt from errors and failures during the machining process. Recent studies found that the main errors are due to the interpretation of the information in the manufacturing drawings due to lack of experience or training of the personnel. This research project aims to eliminate errors due to misinterpretation of information in Computer Numerical Control (CNC) machining processes through the implementation of a methodology to develop Augmented Reality (AR) applications for the interpretation of manufacturing drawings. Its main contribution is to provide virtual support to the operating personnel obtaining multiple benefits such as reducing downtime due to failures in the machining process, ensuring the optimal operation of the machines, avoiding collisions of the tools and ensuring the quality of the products.

The use of the rapid unequal interval interpolation method (RUIIM) for a non-RTCP NC program and five-axis machining

In industries such as electronics 3C, medical, aviation, and energy, multi-axis machining machines are an important element of modern production systems, especially for post-processing, which necessitates the safe operation of control equipment. Some controller and software for five-axis simultaneous machining only interpolate equidistantly between cutter location (CL) points and do not support post-processing using a rotation tool center point (RTCP) so the machining path of the tool has manufacturing errors (overcutting or undercutting) and the workpiece fails to be machined to the correct size. Depending on the maximum allowable error (δMax), this study uses rapid unequal interval sub-point interpolation to perform sub-point interpolation on the original path of the five-axis machining machine, and the sub-point values are output in the computer numerical control (CNC) machining program in C# programming language. The tool tip is then moved to the desired machining position. Finally, an example involving helical milling of a curved surface is used to verify the feasibility of the rapid unequal interval interpolation method (RUIIM). The experimental results show that rapid unequal interval sub-point interpolation gives a more accurate original path that is generated in the CAM and a faster processing speed for the entire controller so the operation speed is increased.

A Comparative Study: The Precision of CNC Machines Using a Sliding Mode Controller (SMC) and a Wi-Fi ESP32

Computerized numerical control (CNC) is one example of advances in automated machine technology. CNC can fulfil high precision and complex product specifications. Industrial CNC milling is still expensive regarding the hardware and software required to operate it. As a result, CNC milling machines are only used by large companies, with only a few mid-sized industries using them. It makes it impossible for medium enterprises to compete with large industries in terms of quality and output. This research aims to develop a CNC that uses an offline Wi-Fi controller based on ESP32 as an application that supports CNC machine work processes for use in small and medium enterprises. In an experiment, the precision of a CNC milling machine equipped with a sliding mode controller (SMC) controller was compared with that of a CNC machine equipped with an ESP-32 Wi-Fi controller. The CNC milling process with SMC control exceeds the tolerance limits in three dimensions based on the results of ten tests, namely length, width, and height or depth. Six values, two in each dimension of length, width, and height or depth, exceeded tolerance limits due to the CNC milling machine's ESP32 Wi-Fi control. Therefore, the CNC with SMC control is more accurate than the ESP32 Wi-Fi control because there are fewer failures with the former. This research has been carried out and provides input for the implementation of SMC, which can be considered in the CNC milling process.

Effect of Coolant Temperature on the Thermal Compensation of a Machine Tool

Machine tool (MT) accuracy is an important factor in the industry and is affected by heat generation through internal and external moving parts; the electrical components used; and variable environmental temperatures. Thermal errors lead to 40–60% of all MT errors. To improve MT accuracy, efficient techniques to minimize thermal errors must be identified. This study investigated the coolant temperature effects under different rotating speeds of a standalone built-in spindle system and computer numerical control (CNC) machine with a direct-drive spindle on the accuracy of thermal deformation prediction. The z-axis thermal deformation of the standalone built-in spindle system and CNC machine with a direct-drive spindle was conducted at different spindle rotating speeds and coolant temperatures at a constant coolant flow rate of 5 LPM. All experiments were conducted in a steady and dynamic operation according to ISO 230-3. For the standalone built-in spindle system, in comparison to the Mares model, the developed new model based on the coolant temperature effect on the Mares model (Mares CT model) can improve the thermal deformation prediction accuracy by 18.17% to 39.50% at different coolant temperatures of 12 ∘C to 26 ∘C and the accuracy can be controlled within the range of 0.03 μm to 5.24 μm, while the supply coolant temperature is above 16 ∘C. However, the thermal compensation analysis of the Mares CT model for a CNC machine with a direct-drive spindle shows a thermal deformation prediction accuracy improvement of 58.30% to 66.35% at different coolant temperatures of 22 ∘C to 28 ∘C and the accuracy can be controlled within the range of 0.14 μm to 4.05 μm. To validate the feasibility of the compensation model in real machining processes, dynamic operational analysis was performed for a standalone built-in spindle system and a CNC machine with a direct-drive spindle, and the thermal deformation prediction accuracy improved by 12.19% to 35.53% with the standalone built-in spindle system and 40.25% to 60.33% with the CNC machine with a direct-drive spindle. The compensation model analysis shows that the coolant temperature has a high impact on thermal deformation prediction and markedly affects system accuracy within certain limits.

Novel compact waveguide filtering twist for computer numerical control machining

Novel compact waveguide filtering twist for computer numerical control machining

A transformer‐based end‐to‐end data‐driven model for multisensor time series monitoring of machine tool condition

AbstractOnline determination of a cutter's health status is crucial for the attainment of condition‐based automated tool change in computer numerically controlled (CNC) machining. Due to the impracticalities associated with direct condition measurements, data‐based modeling of monitoring signals provides a viable practical route. However, the highly noisy and redundant nature of the associated data impacts negatively on model's accuracy and typically calls for additional initial preprocessing before modeling. Additionally, the long sequential data entails widely varying condition distributions exhibited by different cutters, even from the same batch on similar machining parameters, posing a challenge to model generalization. An end‐to‐end model has thus been developed to work directly on unprocessed data to establish global sensitive features from varying distributions for online tool wear estimation in CNC machining. The model utilizes three main functional blocks. First, a data denoising and feature selection block automatically processes raw multisensor data directly, dispensing with scaling or preprocessing of inputs as conventionally done. Each sensor channel's independence is preserved at initial processing ensuring complementary information from different sensors is utilized while simultaneously minimizing existing redundancies. The weighted denoised data is then processed through a transformer encoder block for determination of global dependencies in the time‐series sequence, regardless of the time‐step position. The learned features are then fed to an upper supervised learning block for association with the monitored wear condition. The developed model works directly on raw noisy data irrespective of scaling differences, saving on preprocessing computational cost. The global associations extracted on long sequences by the transformer‐encoder allow for model generalization to varying wear distributions. The parallel processing structure of all channels ensures complementary information is utilized minimizing unforeseen model bias. The model's performance as evaluated on experimental milling data and further comparison with other reported models on same dataset shows attainment of comparable state‐of‐art results.

Fault Detection for CNC Machine Tools Using Auto-Associative Kernel Regression Based on Empirical Mode Decomposition

In manufacturing processes using computerized numerical control (CNC) machines, machine tools are operated repeatedly for a long period for machining hard and difficult-to-machine materials, such as stainless steel. These operating conditions frequently result in tool breakage. The failure of machine tools significantly degrades the product quality and efficiency of the target process. To solve these problems, various studies have been conducted for detecting faults in machine tools. However, the most related studies used only the univariate signal obtained from CNC machines. The fault-detection methods using univariate signals have a limitation in that multivariate models cannot be applied. This can restrict in performance improvement of the fault detection. To address this problem, we employed empirical mode decomposition to construct a multivariate dataset from the univariate signal. Subsequently, auto-associative kernel regression was used to detect faults in the machine tool. To verify the proposed method, we obtained a univariate current signal measured from the machining center in an actual industrial plant. The experimental results demonstrate that the proposed method successfully detects faults in the actual machine tools.

Cost and Quality Optimization Taguchi Design with Grey Relational Analysis of Halloysite Nanotube Hybrid Composite: CNC Machine Manufacturing.

Researchers who work on manufacturing hybrid composites have significant concerns about holistically optimizing more than one performance characteristic, as in the case of cost and quality optimization. They usually trade off one for the other. Hence, this study employed statistical tools and grey relational analyses (GRA) design to model and optimize the surface roughness and cutting force of Computer Numerical Control (CNC) machine settings to manufacture halloysite nanotube hybrid composite. In this paper, the GRA was able to address the multiple optimization complications by producing 0.6 mm depth of cut, 1500 rpm spindle speed, and 40 mmpm feed rate as the CNC machine settings for high-quality and low-cost hybrid composite. It was noticed that the mathematical and interaction modeling of surface roughness, cutting force, and grey relational grade (GRG) allowed different CNC machines to manufacture hybrid composites. This can assist researchers and production engineers of CNC machines. Variance analysis and delta statistical characteristics revealed that the depth of a cut is the most significant machine setting, with a contribution of 49.12%. This paper outlines the possible CNC machine settings for high-quality composite manufacturing. In future studies, it is recommended for researchers in the field of CNC machine manufacturing to consider the modeling analysis aspect of the optimization, which comprehensively provides the opportunity for the adjustment of CNC machines for better material performance, which has been lacking in the literature.

Characterization of terahertz wavefront aberrations using computational shear-interferometry

We propose a new solution for sensing a terahertz (THz) wavefront based on a THz reference-less shear interferometer. The key component of the experimental configuration of the proposed interferometer is a THz Ronchi phase grating (RPG). The RPG is custom designed and fabricated for a 0.28 THz source using mechanical milling on a block of high-density polyethylene with a computer numerical control machine. It acts as a shearing element that generates two diffraction orders, thereby, creating two laterally shifted copies of the investigated wavefront in the sensor plane where a THz camera is placed. The direction of the shear can be varied by rotating the grating. Since the grating is a phase grating, the diffraction efficiency is very high. The approach is verified experimentally by demonstrating interferograms of a spherical wave and wavefront reconstruction from five different shears using a gradient-based iterative process.

Dynamic allocation of human resources: case study in the metal 4.0 manufacturing industry

Industry 4.0 concepts make it possible to rethink human resources allocation, even for more traditional environments like metal machining. While parts machining on Computer Numerical Control (CNC) machines is automated, some manual tasks must still be executed by operators. The current approach is typically that operators are statically allocated to one or many machines. This causes avoidable bottlenecks. We propose an optimisation model to dynamically assign tasks to the operators with the objective of minimising production delays. Three different scenarios are compared; one representing the current widely used static allocation method and two others that allow more flexibility in the operators’ allocation. The dynamic task assignment problem is solved using a constraint programming model. The model was applied to a case study from a high-precision metal manufacturing job shop. Experimental results show that switching from a static allocation to a dynamic one reduces by 76% the average production delays caused by human operators. Supposing more versatile operators under the dynamic allocation leads to further improvements.

Energy-aware sub-regional milling method for free-form surface based on clustering features

Geometric characteristics and curvature of free-form surfaces have a great influence on multi-axis computer numerical control (CNC) machining performances such as processing efficiency, surface quality and energy consumption. The conventional machining methods with the whole tool path generation results in poor low machining efficiency, surface quality, and high energy consumption, due to the repeated and crossing tool path. Therefore, this paper proposes an energy-aware milling method for complex surface based on clustering features. Firstly, the geometric features of free-form surfaces are classified according to their curvature characteristics. Then, free-form surface is discretized and initially divided into different regions, and further divided into patches based on K-means clustering algorithm to generate the same or similar surface type. Moreover, an optimization model for the sub-regional milling tool path is established, and the optimal result is obtained using adaptive dynamic genetic algorithm. Finally, the effectiveness of the proposed method is verified by comparison of traditional milling methods.