Machining Center Research Articles

Cutting Tool Monitoring Technology Using Wireless Acoustic Emission Sensor

Acoustic emission (AE) waves in milling are measured by mounting an AE sensor on the workpiece owing to the need of a signal line for the AE sensor. However, if the distance between the AE sensor and the machining point is excessive, attenuation may hinder the measurement of AE waves. Therefore, AE waves in milling should preferably be measured at the tool side. This study developed a device for monitoring cutting tools using a wireless AE sensor. The proposed device measures the amount of chipping at the cutting edge of the end mill during milling. This study specifically focused on the milling of narrow grooves with a small-diameter end mill using a machining center. With the developed device, the AE waves generated from the chipping of the cutting edge are measured at the tool, and the measured data are transmitted wirelessly. Three different methods were tested for attaching the AE sensor to the tool holder, confirming that the AE waves could be measured. Then, an end mill was placed in contact with a diamond, and the AE waves generated by chipping of the cutting edge were measured. Narrow grooves were milled with an end mill to demonstrate that the device could measure the AE waves generated from the cutting edge when chipping occurred. Observations suggest that the developed device can monitor the size of chipping of cutting edges.

A novel FMECA method for CNC machine tools based on D-GRA and data envelopment analysis

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement.

The Analysis and Compensation Experiment of Linear Feed Axis Positioning Error Based on Matrix Operation

The positioning accuracy of the linear feed axis is a key factor affecting the machining accuracy of CNC machine tools. The generation process of the positioning error of the linear feed axis is expounded. The Z axis of the vertical linear feed axis of the machining center is the research object, the positioning error analytical matrix is established, and the positioning error calculation of the Z axis of the vertical linear feed axis is completed based on the principle of solving the inhomogeneous linear equation. The positioning error detection of the vertical linear feed axis Z axis of the CNC machining center is carried out. Based on the characteristics of the error detection data, an error compensation model is constructed. , the variance of the positioning error detection point after compensation is reduced to 1.562, which meets the needs of precision machining.

Welding Corrective Maintenance Solutions for Molds

Designing and manufacturing a new mold is laborious work and requires well-trained human resources but also considerable material investments. The achizition and implementation the CAD/CAM softwares, three and five-axis machining centers, cutting tools with high performance are necessary to processing a mold. That is why it is frequently used to repair used molds. This repair is done by welding and then manufacturing on machines with numerical control. The present paper presents these stages of corrective maintenance of a mold. Starting from a concrete case, the solutions for remediation of the used mold are presented. The two basic operations in retrofitting, welding and computer-assisted processing on CNC machines are also analyzed.

Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines

Computer numerical control (CNC) and machine center (MCT) machines are mechanical devices that manipulate different tools using computer programming as inputs. Predicting failures in CNC and MCT machines before their actual failure time is crucial to reduce maintenance costs and increase productivity. This study is centered around a novel deep learning-based model using a 1D convolutional neural network (CNN) for early fault detection in MCT machines. We collected sensor-based data from CNC/MCT machines and applied various preprocessing techniques to prepare the dataset. Our experimental results demonstrate that the 1D-CNN model achieves a higher accuracy of 91.57% compared to traditional machine learning classifiers and other deep learning models, including Random Forest (RF) at 89.71%, multi-layer perceptron (MLP) at 87.45%, XGBoost at 89.67%, logistic regression (LR) at 75.93%, support vector machine (SVM) at 75.96%, K-nearest neighbors (KNN) at 82.93%, decision tree at 88.36%, naïve Bayes at 68.31%, long short-term memory (LSTM) at 90.80%, and a hybrid 1D CNN + LSTM model at 88.51%. Moreover, our proposed 1D CNN model outperformed all other mentioned models in precision, recall, and F-1 scores, with 91.87%, 91.57%, and 91.63%, respectively. These findings highlight the efficacy of the 1D CNN model in providing optimal performance with an MCT machine’s dataset, making it particularly suitable for small manufacturing companies seeking to automate early fault detection and classification in CNC and MCT machines. This approach enhances productivity and aids in proactive maintenance and safety measures, demonstrating its potential to revolutionize the manufacturing industry.

The impact of heat treatment process on the drilling characteristics of Al–5Si–1Cu–Mg alloy produced by sand casting

Aluminum–Silicon (Al–Si) based materials are commonly preferred in engineering studies requiring high mechanical performance as an alternative to steel materials. These alloys are especially preferred in the automotive industry, aerospace components and heavy machinery parts. In post-casting processes, machining operations are of great importance for high geometric precision, surface quality and longer fatigue life in mechanical environments. In this research, the microstructural, mechanical and machining characteristics of the Al–5Si–1Cu–Mg material produced by sand casting method in both as-cast (AC) and heat-treated (HTed) (Solid solution, quenching and aging-SQA) states were experimentally investigated. Microstructural examinations were carried out with optical microscope and SEM images. Mechanical properties were determined by tensile and hardness tests. Then, drilling experiments were performed in the CNC vertical machining center with 8 mm diameter uncoated HSS (High Speed Steel) cutting tools at constant cutting conditions (i.e., cutting speed-V: 125 m/min, feed rate-f: 0.05 mm/rev and depth of cut-DoC: 15 mm). In microstructural investigations, it was determined that the microstructure of the Al–5Si–1Cu–Mg material in AC state consists of α-Al, eutectic Si, β-Fe (β-Al5FeSi) and π-Fe (π-Al8Mg3FeSi6) intermetallics. After the SQA, the existing phases generally exhibited a spherical structure, and it was seen that the β phase in the microstructure transformed into the Ɵ (Al7FeCu2) phase. SQA improved the hardness, yield and tensile durability of the material, whereas decreasing the elongation to fracture. SQA process improved the machining characteristics of the material by decreasing thrust force, moment, surface roughness and built-up edge (BUE) formation. The machined subsurface structure of HTed alloy under constant cutting conditions was determined to be more stable and smooth and the machined surface microhardness of HTed alloy was higher compared to as-cast alloy due to the effect of solid precipitation hardening. In addition, shorter and more broken chips occurred in the machining of HTed alloys due to the effect of low elongation to fracture.

Application of PCA-LSTM algorithm in predicting component faults in CNC machining centers

Abstract In order to further explore the relationship between component failures and CNC machining center failures, a method of predicting component failures in advance was proposed to reduce the likelihood of machining center failures and improve CNC machining efficiency. In this paper, the principal component analysis method is used to optimize the Long short-term memory neural network to build a network model to study and predict the fault status of NC machining center components in work. The use of Principal Component Analysis (PCA) to extract the main features of machine tool faults can improve the speed of iterative training networks during the machining process and reduce the input dimension and prediction difficulty of LSTM. The extracted data is used as input for the LSTM network. The fault type is output. The comparison of diagnostic results between PCA-LSTM, LSTM, and BP neural networks shows that PCA-LSTM is a high-precision prediction model. The root mean square difference is reduced to 1.572. The predicted error results can effectively reflect the changes that occur in the machining center components with the occurrence of faults. It can be seen that it is feasible to use PCA-LSTM as a tool for predicting the faults of CNC machining center components.

Experiment and simulation analysis on static and dynamic performance of a precision horizontal machining center

Experiment and simulation analysis on static and dynamic performance of a precision horizontal machining center

Rotation error separation of a UTF300 high-speed and precision motorized spindle

To improve the measurement accuracy of high-speed and precision motorized spindle rotary error as the research objective, based on the three-point method, an error separation model and an objective optimization function for noise-containing signals are developed. To improve the convergence speed, globally optimize the model objectives, and obtain the best optimization region of the sensor mounting angle, it is proposed that an enhanced adaptive particle swarm optimization algorithm be used. With the improved particle swarm algorithm, the convergence speed was greater than that of the primary particle swarm algorithm by more than 50%. The spindle radial rotation error was experimentally measured and separated using a high-speed vertical machining center, and the deviation between the separation result and the experimental rotation error was 4.5%, indicating that the separation result's accuracy was high. It also proved the correctness and feasibility of the optimization algorithm.

Influence of HSS drill coatings on surface finish and cylindricity of AA6061/C/ZrO2 composite in CNC drilling operations under dry conditions

Abstract This study aims to evaluate the effect of HSS drill tool coatings, namely single-layer TiN, single-layer AlCrN, and double-layer AlTiN + TiSiXN (Durana), on the surface roughness and cylindricity of AA6061 (90 wt%)/C (5 wt%)/ZrO2 (5 wt%) hybrid composite material processed by the stir casting method. The fabricated sample was examined for the uniform particle distribution of reinforcements using Scanning Electron Microscope. The drilling operation was carried out on the fabricated in a CNC machining center by setting the spindle speeds of 800, 1200, and 1600 rpm, depths of cut of 0.5, 1, and 1.5 mm, and feeds of 50, 100, and 150 mm/rev. An orthogonal array (L27) designed by Taguchi’s method was used as the design of the experiment for the optimization of the best cutting parameters and coating. Surface roughness and cylindricity errors were determined for the 27 experimental runs. Field emission scanning electron microscopic (FESEM) examination and Energy Dispersive Spectroscopy (EDS) were used to analyze the surface integrity and elemental composition, respectively. The results revealed that Durana had a significant effect on the surface substrate with minimum surface roughness (Ra) of 1.666 μm and obtained the minimum cylindricity error for the parameters of depth of cut 0.5 mm, spindle speed of 1200 and feed of 100 mm rev−1.

Innovative CNC Increases Productivity and Efficiency on 5-Axis Machines

Machine tool manufacturer Okuma has upgraded more CNC machine tools with its OSP-P500 control, including some of its high-tech 5-axis machining centres

Design, analytical and computational analysis, and development of a high-precision CNC spindle for a vertical machining center

This research focuses on design, simulation and development of a high precision Computer Numerically Controlled (CNC) spindle for vertical machining center. High speed spindle is designed and analyzed under the varying load conditions. SolidWorks 2020 is used for designing of CNC spindle (based on ISO BT40 standards) and ANSYS Workbench 2020 is used for analysis. Moreover, analytical calculations for deformation are also done. Three different materials i.e. AISI 4340 steel, Grey cast iron, and Ti6Al4V alloy are chosen for the analysis. Effect of varying load on deformation and stress on CNC spindle is discussed. AISI 4340 steel showed better mechanical characteristics as compared to other materials. Moreover, the results showed that steel gives lesser values of deformation as compared to other materials, when applied under different load conditions. Although Grey cast iron can be economical and weight of the spindle can also be reduced but Grey Cast iron has greater value of deformation when load is applied. On the other hand, Ti6Al4V alloy specimen exhibited less stress as compared to other materials but mechanical properties in terms of modulus of elasticity are not that good as AISI 4340 steel. So, AISI 4340 steel could be a good option for spindle materials that require desired properties such as strong corrosion resistance, tensile strength, yield strength, and propagation. This will boost the accuracy and efficiency of spindle. Based on these results, high-precision CNC spindle was developed.

5-Axis Machining Centres for Maximum Precision and Performance

Machine tool manufacturer Heller’s latest machine tool is the F 5000, a high performance 5-axis machining centre. We find out more about the latest model

Improving the Efficiency of Variable Electric Drives of Laser Machining Centers

Improving the Efficiency of Variable Electric Drives of Laser Machining Centers

Bionic optimization design for the crossbeam of a five-axis machining center based on honeycomb sandwich structures

Bionic optimization design for the crossbeam of a five-axis machining center based on honeycomb sandwich structures

Production scheduling decision-making technology for multiple CNC machining centers with constraints on serviceable time

Production scheduling decision-making technology for multiple CNC machining centers with constraints on serviceable time

Comparative Study of Conventional Machine Learning versus Deep Learning-Based Approaches for Tool Condition Assessments in Milling Processes

This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector machines (SVMs), and boosting ensemble decision trees, utilizing sensor data from a CNC machining center. These methods focus on the challenges and importance of feature selection, data preprocessing, and the application of tailored machine learning models to specific industrial tasks. Results show that SVM, with an accuracy of 96%, excels in handling high-dimensional data and robust feature extraction. In contrast, LSTM, which is appropriate for sequential data, is constrained by limited training data and the absence of pre-trained networks. Boosting ensemble decision trees also demonstrate efficacy in reducing model bias and variance. Conclusively, selecting an optimal machine learning strategy is crucial, depending on task complexity and data characteristics, highlighting the need for further research into domain-specific models to improve performance in industrial settings.

25 Years Ago

NC Engineering reports record machine tool sales; Tricept robotic machining centres set to become a common sight; £4m expansion of Commercial Hydraulic Kontak’s facility; and demanding lead times drive move to multi-spindle machining centres

Route to Winning New Customers

Oxfordshire subcontractor Factory 33 is reaping the rewards of investing in the latest cutting-edge machining centre technology

Non-destructive on-machine inspection of machining-induced deformed layers

Complete inspection of workpiece surface integrity invariably involves a form of destructive testing to enable the assessment of microstructural defects such as machining-induced white layers and near-surface plastic deformation. The incumbent offline and destructive microscopy inspection process is incompatible with both a digital and sustainable manufacturing vision of zero waste, as such, a non-destructive technique which utilises a novel X-ray diffraction surface integrity inspection method (XRD-SIIM) has been developed. This approach has been designed to complement traditional machinability-type assessments of tool life and machined surface topography, establishing a new process flow for validation. In this paper, for the first time, non-destructive on-machine validation of workpiece microstructural surface integrity is demonstrated, via a comparative investigation into the effect of insert grade, cutting speed and coolant delivery method on the depth of the imparted plastic deformation depth. It is shown that XRD-SIIM allows repeatable, non-destructive determination of deformed layers within a typical machining centre enclosure, with comparable findings to the incumbent cross-sectional microscopy approach. The generation of surface integrity digital fingerprints of a machining operation facilitates rapid comparison between testing variables, with a transition to an objective quantifiable assessment rather than one which open to subjectivity. In turn, XRD-SIIM expedites the development and benchmarking of new operations, tooling, materials, or coolant.