CNC Milling Machine Research Articles

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.

Tool wear prediction based on K-means and Adaboost auto-encoder

Abstract A new tool wear prediction model is proposed to address the tool wear issue, aimed at monitoring tool wear based on specific task requirements and guiding tool replacement during actual cutting operations. In the data preprocessing phase, tool wear states are classified using unsupervised K-means clustering. The time, frequency, and time-frequency domain features are then labeled and fused using an autoencoder neural network applied to the original set of signal features from the tool. For tool wear prediction, an enhanced autoencoder neural network leveraging AdaBoost is employed to establish the prediction model. The reconstruction error serves as the chosen loss function to assess the autoencoder's performance, taking into account data correlation and the inherent lossy nature of the autoencoder. Experimental results from real machining data obtained from a CNC milling machine demonstrate that the proposed model achieves higher prediction accuracy while reducing data dimensions.

Application of artificial neural network in predicting the performance of SPIF for aerospace grade AA7075-T6

Single Point Incremental Forming (SPIF) is recently been used to prepare customized parts with complex geometries. The vertical CNC-milling machine and CNC code are generally used to carry out the forming operation. The material used in the present study is aerospace grade AA7075-T6 due to its inherent properties of high strength-to-weight ratio. The input parameters such as annealing temperature, step depth, tool shape, and the number of cut-out blanks were taken into consideration. The Output parameters like surface roughness and formability were recorded and evaluated by developing an Artificial Neural Network (ANN) based prediction model. Both single-output and double-output neural networks were established for this purpose. In case of one-output structure, the training function of trainlm, transfer function of logsig and 8 no. of neurons whereas, the training function of trainlm, transfer function of tansig and 6 no. of neurons are found to be suitable combination for surface roughness and formability respectively. On the other hand, in case of two-output structure, the training function of trainlm, transfer function of tansig and 10 no. of neurons are found to be suitable. The MAPE and R-values of the developed ANN model denote good agreement with the experimental results. The developed model will reduce the cost, effort, and time of young engineers and practitioners to select accurate parameters without performing expensive experimental runs.

Optimising Sensor Placement for Tool Condition Monitoring: A Comparative Analysis of Acoustic Emission Data

In the monitoring of machining processes and in the context of tool condition monitoring, the position of acoustic emission sensors affects the quality of the resulting information. This drives this comparative study which presents a comparison of acoustic emission data recorded simultaneously by a sensor attached to the side of the milling table and another sensor near the spindle during a tool wear test on a CNC milling machine. The signal information is clearly influenced by the different positions. Whereas the signals near the spindle are dominated by vibrations of the spindle motor during machining, these strong contributions do not occur in the recorded data of the machining process detected by the sensor mounted to the table. However, the position of the sensor near the spindle has the advantage of maintaining a constant distance to the source of the acoustic emissions of the machining process, as the sensor moves with the tool. In contrast, the sensor attached to the milling table is affected from the fact that the distance to the milling tool is constantly changing. This results in different dominant frequencies, that are analysed in this study. To analyse both the entire frequency range and the higher frequencies, two distinct approaches are employed, one without and one with the use of a high-pass filter. Furthermore, the recorded signals were used to calculate the characteristic features, namely the Partial Powers, thereby enabling assessment of the condition of the tool. The results demonstrate that this single feature type is sufficient for training a machine learning model to predict the tool wear, focusing on the flank wear width. This study analyses the efficiency of wear prediction with respect to both sensors and frequency spectra, and compares the results. In this context, the most accurate predictions were achieved with Gaussian process regression models.

An integrated design method for remanufacturing scheme considering carbon emission and customer demands

Design for remanufacturing (DfRem) plays an important role and aims to reduce carbon emissions during the remanufacturing phase at the initial design stage. However, low-carbon requirements and customer demands are not considered integrally when generating schemes, which might to limit the potential to reduce carbon emissions. To address the problem, this paper proposes a DfRem scheme generation method that combines the Quality Function Deployment for Low-Carbon (QFDC) model with the FMAS (Functional-Motion-Action-Structure) model. Firstly, the demands are categorized into customer, remanufacturability and low-carbon demands, and the FAHP (Fuzzy Analytic Hierarchy Process) is used to evaluate the weight of demands. Secondly, the QFDC model is developed to convert demands to engineering characteristics, and to determine the design priorities. Thirdly, the FMAS model is established to map engineering characteristics into related structures, and a low-carbon and customer-satisfied DfRem scheme is generated. Finally, the efficacy of proposed method is demonstrated through a case study on the remanufacturing of a CNC milling machine, displaying its superiority in reducing carbon emissions while meeting customer demands.

Friction stir process parameters optimization in the fabrication of agate particle reinforced AA6061-T6 aluminum alloy surface composite

The main objective of this study was to optimize the friction stir process parameters to fabricate agate particle reinforced AA6061-T6 Aluminum alloy surface composite for hardness and tensile strength. The surface composite samples were made using a CNC milling machine based on Taguchi’s mixed L16 orthogonal array. The process parameters such as tool rotational speed, traverse speed, percent of reinforcement, number of passes and pin profiles were selected in this study. The optimal properties for the developed surface composite were obtained when surface composites having 18% reinforcement were fabricated by a two-pass FSP using a squared pin tool with a rotational speed of 1400 rpm and traverse speed of 60 mm/min. At the optimal process parameters, the tensile strength and hardness of the fabricated surface composite were found to be 312.23 MPa and 74.82 HRB respectively. ANOVA analysis performed at a 95% confidence level revealed that all process parameters were significant and the contribution of the number of passes on the properties of surface composite was higher than other process parameters. Microstructural analysis revealed that medium rotational speed and high traverse speed produced a uniform distribution of agate particles which helped to improve the properties.

Numerical modelling and experimental investigation of incremental sheet metal forming of Aluminum Alloy Al 3003-O

Abstract Incremental sheet forming (ISF) is an unconventional sheet metal forming technique that can be widely used in the automotive, medical, and aerospace industries. The final product is achieved by moving a rotating tool along a defined spiral or profile tool path controlled by a CNC milling machine. A truncated cone shape was modeled in single point incremental forming (SPIF) to investigate the influence of step size, and sheet initial thickness parameters on the forming force components Fx , Fy , Fz during the forming process.

Evaluation of Surface Quality and Productivity in Conventional Milling of Copper Beryllium Using Minimum Quantity Lubrication

Copper Beryllium (C17200) has ideal physical and mechanical properties of high fatigue strength, thermal conductivity, and hardness, making the alloy ideal for various high-reliability applications in aerospace, producing inserts and tooling for hazardous environments. However, surface quality and productivity challenges are prominent when processing the alloy due to its properties. This study evaluates the surface quality and productivity in end-milling of Copper Beryllium by analyzing the effects of feed rate, minimum quantity lubrication (MQL) flow rate, and cutting depth on surface roughness (Ra) and material removal rate (MRR). The experiment was designed using the Box-Behnken design, and the samples were machined on a CNC milling machine. The results showed that the MQL flow rate was significant to surface roughness, while the cutting depth was significant to MRR. The optimum parameters were determined as a feed rate of – 60 mm/min, an MQL flow rate of – 80 ml/hr., and a cutting depth of – 0.511 mm, which yielded a surface roughness of 0.12 µm and MRR of 10.19 g/min. The study's novelty is that it considers MQL flow using vegetable oil-based cutting fluid (CF) as an input parameter in machining, offering insight into eco-friendly and cost-effective machining practices. Finally, the significance of the study lies in investigating the machinability of Cu-Be alloy material, addressing challenges related to surface quality and productivity during milling operation.

Deep Koopman Operator-based degradation modelling

Developing reliable health indicators for industrial assets is essential for accurate condition monitoring, fault detection, and predicting the remaining useful lifetime. However, constructing such indicators is challenging, especially given the increasing complexity of industrial systems and the not well-understood degradation dynamics. Previously proposed autoencoder-based methods for unsupervised health indicator construction faced the difficulty of constraining the latent representation over the system’s lifetime to obtain trendable and prognosable health indicators. Koopman operator theory provides a natural solution to this challenge. In this work, we first demonstrate the successful application of the Deep Koopman Operator approach for learning the dynamics of industrial systems. This results in a latent representation that provides sufficient information for estimating the remaining useful life of the asset. Secondly, we propose a novel Koopman-Inspired Degradation Model for modelling the degradation of dynamical systems with control. The Koopman-based algorithms demonstrate superior or comparable performance with autoencoder-based approaches in predicting the remaining useful life of assets such as CNC milling machine cutters and Li-ion batteries.

Experimental research and optimization based on response surface methodology on machining characteristics of cast Al-7Si-0.6 Mg alloy: Effects of cutting parameters and heat treatment

In this study, structural, mechanical and machining features of Al-7Si-0.6 Mg alloy manufactured by permanent mold casting (PMC) in both as-cast (AC) and T6 heat-treated (HT) conditions were investigated. Structural and mechanical properties were determined using conventional methods. Milling experiments were conducted with titanium aluminum nitride (TiAlN) coated carbide end mills in CNC milling machine under conditions of three different cutting speeds (V: 50, 80 and 110 m/min), feed rate (f: 0.08; 0.16 and 0.24 mm/rev) and constant depth of cut (DoC) (1.5 mm). It was seen that structure of AC Al-7Si-0.6 Mg comprised of aluminum-rich α-Al, coral-like eutectic Al-Si, primary Si, Mg2Si, Fe-rich plate, acicular β-Fe (β-Al5FeSi) and script-like π-Fe (π-AlSiMgFe) intermetallic phases. Eutectic Al-Si, primary Si, β-Fe and π-Fe phases in the structure of the alloy in the HT condition were partially dissolved and turned into an agglomerated and spherical state. While the hardness (HB), yield (YS) and tensile strength (TS) of the alloy raised with HT, the elongation to fracture (EF) reduced. While cutting force (CF), surface roughness (SR), built-up layer (BUL) and built-up edge (BUE) decreased with increasing V, they increased with increasing f in the milling of both cast and HT alloys. Adhered layers and feed marks were consisted of the cutted surfaces of the alloys. While the most adhered layer was observed in AC alloys at a low V (50 m/min) and high f (0.24 mm/rev) combination, the least adhered layer formation was observed at the highest V and the lowest f value in HT alloys. Optimum parameters with Response Surface Methodology (RSM) were determined as V: 125 m/min and f: 0.04 mm/rev in AC and HT cases. The statistical significance of V and f on CF and SR was revealed by analysis of variance (ANOVA), while mathematical models were improved to predict CF and SR.

Advanced CNC thread milling: a comprehensive canned cycle for efficient cutting of threads with fixed or variable pitch and radius

This paper presents the design, implementation, and experimental validation of a novel canned cycle for CNC milling machines, enabling the precise and efficient cutting of threads with fixed or variable pitch and radius. Conventional canned cycles are limited to fixed pitch threads, restricting the versatility of CNC milling machines in thread machining applications.The development process involves integrating a sophisticated control algorithm into the CNC milling machine's software, giving the operator remarkable control over the thread cutting process. This algorithm allows the operator to choose between external or internal threads, set both initial and final radii, determine initial and final pitches, specify the number of turns, and select the left or right-hand thread type. Such flexibility enables the creation of threads with diverse geometries. Furthermore, the proposed canned cycle provides the capability to switch between roughing and finishing passes by adjusting the step motion along the prescribed helical curve.Simulation tests conducted under various threading cases clearly demonstrate the efficiency of the proposed canned cycle. These results showcase its capability to address a wide range of machining scenarios, offering practical solutions applicable across a spectrum of applications.

Examining the impact of slide burnishing parameters on the 3D surface features of medium carbon steel

Burnishing is a non-cutting finishing technique in which pressure is applied to plastically deform the irregularly distributed surface materials. In this process, the peaks of the surface are pressed into the valleys with a hard tool, resulting in a smoother surface with improved properties. The influence of brunishing parameters such as brunishing force, feed rate and number of passes on the 3D surface roughness of medium carbon steel was analyzed. An orthogonal L9 array Taguchi design was used to create a robust experiment. A CNC milling machine and a confocal microscope were used to grind and measure the workpiece surface. The topography roughness responses Sq, Sv, Sz and Sa were used to evaluate the roughness changes. The optimal parameters for brunishing were a feed rate of 0.05 m/min, 2 passes and a force of 80N for Sq and Sa and a force of 100N for Sv and Sz. This research is expected to provide insights into optimizing mass finishing processes to improve surface integrity, contributing to advances in manufacturing and materials science.

Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks

Objectives: To analyze the critical postures of the CNC milling machine operators by RULA (Rapid Upper Limb Assessment) scores and develop an ANN (Artificial Neural Network) prediction model. Methods: The methodology includes a postural analysis of 40 male CNC milling machine operators across Bangladesh, employing both manual (using manual RULA assessment worksheet) and digital (using CATIA V5R21 software) RULA methods complemented by an ANN prediction model. Finally, Digital RULA scores through DHM (Digital Human Modeling) and ANN predicted RULA scores would be compared. Findings: Digital RULA analysis reveals that lifting, carrying, and positioning are the most crucial ergonomic postures, and the most prominent high-risk category limbs are wrist and arm. The overall initial RULA score for lifting, carrying, and positioning are 7, 6, and 7, respectively, and reduced to 3, 3 and 4 respectively for ergonomically designed posture. The ANN model, structured with input, hidden, and output layers of 7, 10, and 1 nodes, significantly refines ergonomic risk prediction by aligning predicted scores closely with actual outcomes during the first stage, emphasized for training. It demonstrates a perfect correlation (R=1) in training, testing, validation, and overall performance for using manual RULA scores. The model's accuracy is further evidenced by minimal prediction offsets across all datasets for digital RULA score in the second stage, with correlation coefficients of 0.87003 (training), 0.93676 (validation), 0.89113 (testing), and (0.88395) for overall. This study contributes significant advancements in ergonomic risk assessment, highlighting the adoption of improved postures to reduce musculoskeletal disorders. Novelty: Employing both manual and DHM methods for RULA score calculation combined with ANN model, which can predict postural risk as floating number and fit a wider range of parameters. Keywords: ANN, CNC, Digital Human Modeling (DHM), Ergonomics, RULA

Analisis Mesin CNC Milling dengan Metode Overall Equipment Effectiveness dalam Mendeteksi Six Big Losses di PT. A

PT. A is a company engaged in the manufacturing industry with products produced in the form of (dies, jigs, and parts manufacturing). During the activity, the CNC milling machine Milltex Vex 580b experienced engine damage which resulted in the machine working unproductively and affecting the level of effectiveness of the CNC milling machine Milltex Vex 580b. Overall Equipment Effectiveness (OEE) is a method consisting of availability level, quality level, and performance level as a measurement of optimal machine performance tool productivity to maintain the effectiveness of a machine. To eliminate losses using Six Big Losses which are six big losses that can affect the level of machine effectiveness, the average OEE result is 69% where this result is still below world-class standards. This is due to losses caused by a reduction in loss speed of 42%. By analyzing the fishbone diagram (consequences) of this method, it can be concluded that the cause of the low effectiveness value in machine performance is due to the level of availability which is influenced by the reduction in loss speed. Keywords: Effectiveness, Overall Equipment Effectiveness, Six Big Losses

Processing of a Part Such as a Composite Bearing Housing on a Milling Machine With a Digital Control Program

In the era of the industrial revolution of ndustry 4.0, the Republic of Kazakhstan is rapidly introducing automated robots and digital control programs in many of its production facilities. In this context, special attention is paid to modern technologies, and AKIRA SEIKI milling machines with FANUC control software stand out as advanced technologies in this process. They are becoming the most popular in the CIS countries due to the economic efficiency and intellectual support of the Taiwanese manufacturer. Practical aspects of constructing CNC milling machines and their practical use are presented in this article. Particular attention is paid to the functionality of milling machines using the example of processing such a part as a composite bearing housing. In addition, the process of writing processing operation codes in the CIMKO computer program is described. The code is provided with brief descriptions to help you understand the compiled workpiece processing operations. With the growing need for efficiency and precision in production, many companies and industrial plants in the Republic of Kazakhstan are considering AKIRA SEIKI milling machines with FANUC control software as a major investment. These machines not only provide high productivity, but also help automate complex technological processes. CIMKO, among other things, allows the development and optimization of machining code, which complements the efficiency and accuracy of the machines.

Extrusion-based additive manufacturing of aluminium-filled ethylene vinyl acetate for electrically conductive 3D part printing

Addition of carbon-based fillers to enhance electrical conductivity is quite popular but addition of metal fillers is still a challenge due to oxidation and unavailability of metal nano-powders. In this paper, fabrication of flexible electrically conductive parts is proposed using aluminium metal powder as filler material. Aluminium metal powder is mixed with ethylene vinyl acetate (EVA) by chemical mixing process. Cyclohexane is used as a mixing medium in which EVA is dissolved and aluminium powder is added by weight. An in-house developed material extrusion tool mounted on CNC milling machine is used for printing of flexible electrically conductive samples. Pellet-based extrusion process is used for printing flexible parts as conventional filament-based 3D is not suitable due to buckling. Composite with filler loading in 5%, 10%, 20% and 30% were made and samples were printed. Investigations were made on printability of composite and its electrical conductivity. The aluminium fillers presence and dispersion in polymer were ensured. The samples were printed in temperature range of 130–140 °C through 0.4 mm diameter nozzle. It was found that the resistivity drops by around 60% on varying the filler concentration from 20% to 30%; 81.6 kΩ is the lowest resistance observed with 30% filler concentration. A simulation study on thermal behaviour of material extrusion tool has been done and is validated practically. The application of flexible electrically conductive parts lies in fabrication of various electronic devices like antenna, display boards, solar panels, and wearable health monitoring devices.

Data model-based toolpath generation techniques for CNC milling machines

Introduction: With the development of computer technology and data modeling, the use of point cloud models to generate tool paths is particularly important for improving productivity and accuracy.Methods: This study proposes a new method that first preprocesses the point cloud data using four-point denoising and octree methods to improve processing efficiency. Subsequently, roughing tool paths were analyzed using the layer slicing method and finishing paths using the residual height method.Results and Discussion: The experimental results show that the layer slicing method has a minimum error close to 10% on the roughing path generation and the computation time is reduced to 35 s, while the residual height method has an error rate of 10.17% on the finishing path and the computation time is only 11.82 s, which reflects a high trajectory smoothness and accuracy. The above results show that the study not only optimizes the tool path generation process and improves the machining efficiency and accuracy, but also demonstrates the potential application of point cloud models in the machining of complex parts.Conclusion: The novel tool roughing and finishing methods provide more reliable path planning for actual machining operations, and future research will be devoted to further improving the performance of the data processing algorithms and exploring more efficient path planning strategies to facilitate automated production.

Computational and Experimental Investigation of Thermal Generation in CNC Milling Machine Spindle Βearing with the Oil-Air Lubrication Method

This study investigates the thermal generation dynamics in the spindle unit of a CNC milling machine. The main objective is to analyze the heat transfer from the bearings to the housing during operation, using simulations and experiments. An air-oil-cooled lubrication system is employed, which enables the airflow to dissipate some of the heat produced. Before conducting the experiment, heat generation and transfer are accurately predicted. The air-oil mixture, formed by pressurized air atomizing oil droplets, is effective in cooling high-temperature regions. A spindle ER16-80SK 24k is integrated with a CNC machine for real-time data acquisition, including parameters such as spindle speed and temperature. The results show significant temperature rises in all bearings, which reach a steady state after an hour of operation. There are noticeable differences in range, from 6.1% to 43.4%, between the experimental and simulated maximum temperatures, indicating possible real factors not considered in the simulation. The environmental impacts of oil particle emission are also discussed, which require proper ventilation management during operations. This research provides valuable insight into the thermal dynamics of CNC milling machine spindles and establishes a solid basis for further research and development in this important field of machining technology.

Development of Surface Roughness Prediction and Monitoring System in Milling Process

High surface quality is an important indicator for high-performance machining during the manufacturing process. The surface roughness generated in machining can be affected by cutting parameters and machining vibration. To achieve processing efficiency, monitoring surface quality within the desired range is important. This study aimed to develop a surface roughness prediction system for the milling process. The predictive model was established based on data collected from machining experiments with the response surface methodology. The surface roughness is related to independent variables, including cutting parameters and machining vibration, in terms of nonlinear functions by regression analysis and the neural network approach, respectively. To be implemented in a CNC milling machine for online application, a predictive model was introduced in the Virtual Machine Extension (VMX) intelligent software development platform. This model can acquire the cutting parameters from the controller via the Open Platform Communications Unified Architecture (OPCUA) interface as well as the vibration features from the sensory module. The system can calculate the roughness based on these data and issue alert when the predicted value exceeds the preset threshold or abnormal vibration is detected.

Optimizing highly reflective CNC abrasive polishing on aluminum sheets

ABSTRACT The polishing industry’s demand for achieving a smooth, highly reflective surface, flat, particle-free metallic surface, crucial for imaging and optical applications has increased. As aluminum metallic mirrors are more in demand, an eco-friendly, cost-effective mechanical polishing procedure that can be carried out on a CNC milling machine is proposed. The proposed CNC Abrasive Polishing (CNC AP), performed on CNC machines achieves 3 nm roughness average with high reflectivity on aluminum sheets. The influence of machining parameters, such as including polishing wheel speed (S), depth of cut (DoC), feed rate (FR), and passes, to evaluate the surface quality (Ra), material removal rate, and reflectance were studied, using Response Surface Methodology. The experiments were conducted based on the L16 orthogonal array. A 3D optical profilometer, an atomic force microscope (AFM), and a scanning electron microscope (SEM) with EDS are among the advanced tools used for analyzing polished surfaces. UV-VIS spectrophotometer measurements indicated a substantial 35% to 96% improvement in the optical qualities. Additionally, a simple visual reflective grid method was used to assess the deformation behavior of the reflective Al sheets.