Computer Numerical Control Lathe Research Articles

Advanced Monitoring of Manufacturing Process through Video Analytics.

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

Effect of cutting process using cutting insert of grade UTi20T

In the metal-cutting industry, the precision of the metal-cutting process is of paramount importance. Errors in the metal-cutting process not only lead to damage to the cutting tool but also result in the production of low-quality materials. The incorporation of insert materials in the cutting process is aimed at maintaining cutting precision and achieving superior results. This research seeks to investigate the impact of the cutting process utilizing grade KC5410 cutting insert under minimum quantity lubrication (MQL) Conditions. In this study, machining tests were conducted using the ALPHA 1350S 2-axis computer numerical control (CNC) lathe machine under MQL conditions, employing cutting tool inserts UTi20T supplied by Mitsubishi. Two types of tools were utilized in the cutting process, namely UTi20T. Critical aspects such as cutting force, total power consumption, surface roughness, and tool life were analyzed to provide comprehensive insights into the efficiency of the cutting process. The findings of this study significantly contribute to the understanding of how the integration of Grade KC5410 cutting inserts under MQL conditions can enhance the overall efficiency of metal-cutting operations. The successful machinability assessment was conducted by implementing sustainable machining practices.

Revolutionizing CNC Lathe Education: Designing Instructional Media Integrated Using Augmented Reality Technology

Utilization of Computer Numerical Control (CNC) machines is crucial in modern manufacturing. Nevertheless, enhancing the skills of CNC machine operators remains a challenge in education and training. In response, the solution involves designing learning materials that simulate the machining process, particularly in innovative CNC learning integrated with Augmented Reality (AR) technology. The research utilizes the Multimedia Development Life Cycle (MDLC) model, encompassing steps like concept development, design, material collection, assembly, testing, and distribution. AR devices deliver additional visual information to CNC lathe students. The research results in learning materials using augmented reality to present 3D visualizations through markers, creating object representations to train 2-axis CNC lathes. These materials, designed as 3D virtual objects, offer real-time practicum experiences to learners. Black box testing confirms the effective implementation of augmented reality in CNC lathe training, providing cost savings in practicum materials and cutting tools during repeated testing on a 2-axis CNC lathe.

Machinability performance of different fiber orientations of roll wrapped CFRP pipes

AbstractIntegrating fiber reinforcement plastics (FRP) materials into the industry plays a key role especially for pipes. However, due to the production methods of CFRP pipes, surface finishing processes are inevitable at the end of production. Despite the widespread use of CFRP materials, the predominant focus in the literature has been on their mechanical performance. This study aims to contribute to the limited research on the machinability of CFRP materials. In this context, the turning machining process for CFRP pipes was experimentally investigated in the present study. For this purpose, carbon fiber pipes with an inner diameter of 30 mm and an outer diameter of 60 mm were produced and subjected to computer numerical control (CNC) turning. First, a cylindrical aluminum pipe is used as a mold to manufacture CFRP pipes. Unidirectional (UD) carbon fabrics were wrapped on these aluminum molds. Shrink tape was used to enhance the surface smoothness and required pressure to prevent delaminating of the end product during the process. UD carbon fabrics are wrapped on to the mold at the selected angles (0°, 45°, and 90°) and the CFRP pipe specimens were manufactured using epoxy matrix. Pipes were processed on CNC lathe at 120, 160, 200 rev/min speeds and f 0.4 mm/rev feed rate. The surfaces of the machined specimens were measured with a microscope and a surface roughness device. On the other hand, wear on the tools was observed after the process.Highlights CFRP pipes were produced with different fibre orientations, i.e., 0°, 45°, and 90°. All CFRP pipes are made under equal conditions and cured at 80°. Turning of CFRP pipes was examined under dry environments. Performance criteria: surface roughness, temperature, tool wear, and chip formation were explored. 45° fibre angles CFRP pipes showed the best machinability performance.

Machinability of waste bamboo powder ash reinforced 7075-T651/B4C Hybrid composite in computer numerical control lathe

Metal Matrix Composites (MMC) are novelly prepared with the reinforcement of different wood ash in the matrix material to achieve desired properties. Utilizing the waste material for reinforcement may reduce the material cost. Usually, fly ash from thermal power plants has become a popular reinforcement material for MMC through stir casting and employing in friction stir processing. This research is motivated by using ash of waste bamboo powder (AWBP). The waste of bamboo pieces was collected from the furniture industry. They have been gathered, prepared to ash and used as reinforcement of 4 wt%, 8 wt%, and 10 wt% in stir casting of aluminium alloy 7075-T651 and that was optimized as 8 %. The objective of this research is CNC turning type machinability of AA7075-T651/ B4C/AWBP hybrid Composites. The details of the reinforcement are 2 wt% B4C and 8 wt% AWBP. The effects of such reinforcement in AA7075-T651/ B4C/AWBP hybrid composite have improved the friction and wear behaviours, such as high wear resistance and low wear rate. The composite was cased as circular rods. The CNC turning type machinability was investigated in the CNC turning centre. The cutting speed varied from 1000 rpm to 3500 rpm, the feed rate varied from 100 mm per hour to 200 mm per hour. The Al2O3 nanoparticles concentration on cutting fluids varied from 0.25 wt% to 1 wt%. The response surface methodology was used to optimize the parameters. A mathematical model was developed to predict the responses.

Competencies Development Strategy Using Augmented Reality for Self-Management of Learning in Manufacturing Laboratories (AR-ManufacturingLab)

Technological tools in education open new learning possibilities. This proposal describes integrating augmented reality and different educational elements to develop engineering competencies, specifically using specialized machinery in the manufacturing laboratory. It is necessary to encourage self-management of learning, where users interact with the devices employing an application that contains significant information about procedures to perform and essential elements to consider when manipulating the machinery. The machines involved include industrial robots, CNC (Computer Numerical Control) lathes and milling machines, and PLCs (Programmable Logic Controls). In a traditional training model, an instructor guides the users in learning how to manipulate the equipment. This proposal intends that the participants use their mobile devices to receive each machine's instruction and the necessary documentation. The participants' learning pace varies. So, the research aimed to reduce the deficit in the disciplinary competencies since, in the traditional methodology, there is no heterogeneity in understanding the contents. We designed a competency development strategy with six laboratory practices, integrating multimedia elements to address the significant learning content of each manufacturing cell device. Each lesson contained information about each device and a practice activity and self-evaluation to ensure learning the content.

Analysis of DC magnetron sputtered coated with distinct inserts in CNC machining process

Machining is the process of cutting or removing materials from a work item with a cutting tool to get the desired form. CNC is widely used because of its compact size which enables more number of productions in all type of automotive industries. Many intricate parts required by various industries are created using computer numerical control (CNC) machining. While mass production is useful in some cases, which require selective parts in most industries. Businesses require CNC for their manufacturing process, which enables them for précised jobs with help of this CNC machine software. CNC lathes can make precise cuts quickly with a variety of tools. Hence, the major objective of this work is to extend the life and quality of the cutting tool by improving its hardness and wear resistance by using DC Magnetron Sputtering to coat a 1 μm thin film layer of Tungsten carbide on High speed steel insert. This insert is to be characterized for chemical composition, micro structured, grain distribution and hardness of its surface etc.

Cutting force and surface roughness measurement in turning of Monel K 500 using GRA method

This study aims to develop a methodology for identifying the settings that provide the best results during a computer numerical control (CNC) turning operation. The efficiency of the turning process is affected by both the cutting force used and the surface roughness of the turned surface. This research uses the Taguchi approach to determine the ideal turning parameters that will lead to the lowest feasible surface roughness and cutting force. The turning processes were tested experimentally on a CNC lathe. Turning operations in Monel K 500 are carried out in line with the orthogonal array utilizing L27. The ANOVA method was used to carry out the analysis of the experiment's findings. The grey relational analysis(GRA) is used to identify the cutting force that should be employed and the surface roughness that should be present. GRA is used in the estimation of the best possible circumstances for linked parameters.

Reliability modeling for failure correlations to a CNC lathe subsystem based on Pagerank Algorithm

The modeling of Computer Numerical Control (CNC) lathes has been constrained by various issues, such as the use of single methods, inaccurate model parameters, and inadequate consideration of subsystem failures. To overcome these challenges, this study proposes a reliability modeling method based on the Pagerank Algorithm for analyzing failure correlations in CNC lathe subsystems. The proposed method involves analyzing the failure correlation between subsystems through failure transfer-directed graph and representing the relationship between subsystems using an adjacency matrix. Based on the fault correlation between subsystem, a reliability model of the CNC lathe subsystem is established, and the Pagerank Algorithm is utilized to optimize the influence degree of each subsystem. To validate the proposed method, field use failure information of CNC lathe is utilized as an example, and the above theoretical modeling is verified and analyzed. Finally, the reliability model based on failure correlation is verified using the D-test value, relative error method, and accurate factor. In comparison to the reliability model without considering failure correlation, the proposed model results in a small relative error and more accurate value.

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters.

Effects of Mechanical Scanning Speeds on Eddy-Current Detection of Cavity Defects

This article presents a distributed current-source (DCS) model considering the perturbation of the eddy-current (EC) field and its EC-generated magnetic flux density (MFD) caused by the defect in a moving conductor. The perturbed EC field, which is formulated into two subproblems (with and without a defect) in state space, is iteratively solved for the transient responses of a 3-D EC system and used to analyze the parametric effects on the fine and coarse detection of cavity defects. The DCS model improves the computational efficiency by reducing the solution domain and the number of experiments for parameter optimization, leading to the development of an EC probe consisting of an MFD sensor and a pair of rectangular electromagnets. Experimentally evaluated on a testbed configured on a computer numerical control (CNC) lathe for 2-D area scanning, the findings demonstrate the feasibility of mechanical scanning at a high speed to improve EC detection efficiency.

Pengaruh Variasi Kecepatan Putaran Spindle dan Kedalaman Pemakanan Terhadap Kekasaran Permukaan Alumunium ADC12 pada Mesin Bubut CNC Okuma L150T

In the rapidly growing manufacturing industry, especially in the field of machining, the use of computer systems has become increasingly prevalent. Consumer demands for precise workpieces with consistent quality, quick completion, and large quantities can be more easily achieved using CNC (Computer Numerical Control) machine tools compared to conventional machine tools. To determine the influence of spindle rotational speed and feed depth variation on the surface roughness of ADC12 aluminum in the Okuma L150T CNC lathe, an experimental study was conducted. Aluminum ADC12 specimens were used, and variations in spindle rotational speed and feed depth were tested for surface roughness using a Mitutoyo SJ-210 surface roughness tester. The obtained data were then analyzed and discussed based on statistical correlation and hypothesis testing. From the results of this study, the following conclusions can be drawn: (1) There is an influence of spindle rotational speed variation. At speeds of 900 rpm, 1500 rpm, and 2100 rpm, the average surface roughness levels of the workpiece were 7.45 μm, 6.35 μm, and 5.23 μm, respectively. Therefore, higher spindle rotational speeds result in lower surface roughness of the workpiece. (2) There is an influence of feed depth variation. At depths of 0.25 mm, 0.50 mm, and 0.75 mm, the average surface roughness levels of the workpiece were 5.23 μm, 6.32 μm, and 7.42 μm, respectively. Thus, deeper feed depths lead to higher surface roughness of the workpiece. (3) The combination of feed depth variation and spindle rotational speed that resulted in the workpiece with the lowest average surface roughness was achieved at a feed depth of 0.25 mm and spindle rotational speed of 2100, which amounted to 5.23 μm.

Investigation of the effect of vibration in the multi-objective optimization of dry turning of hardened steel

PurposeThis study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.Design/methodology/approachIn this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).FindingsThe optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.Research limitations/implicationsThe MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.Originality/valueMost studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

Optimization and analysis of machining performance for the milling process during milling of W-Al-Si-C alloy material

This study determined the optimum HSS cutting tool technique parameters for milling W-Al-Si-C rods using Taguchi methodology. This paper explains the empirical results of the selection of appropriate cutting settings that assure lower power consumption in high-end Computer Numerical Control (CNC) machines. An experiment employing the Taguchi methodology on an extruded W-Al- Si-C rod was performed on a CNC lathe with cutting speed, feed rate, and depth of cut as the process parameters. The performance characteristics (energy usage) were quantified by a data collection system. Minor energy process parameters were selected after data analysis. Experimental results are presented to demonstrate the worth of the chosen methodology. A total of 350[Formula: see text]rpm, 0.37[Formula: see text]mm/rev feed rate, and 1[Formula: see text]mm of cut depth produced the best MRR result. The maximum material removal rate (MRR) is obtained at lower levels of spindle speed and depth of cut, i.e., 1.452[Formula: see text]g/sec.

Increasing Overall Equipment Effectiveness in the Computer Numerical Control Lathe Machines using the Total Productive Maintenance Approach

Production machines are the most important part of a production system. Machines need effective and efficient maintenance to be able to guarantee and support the production process to run well. The steel industry is an industry that produces various types and shapes of steel with one of the machines being Computer Numerical Control (CNC) type BT-A16. These machines often have problems such as downtime that interfere with the production experience. The purpose of this research is to find out the problems in the CNC BT-A16 production machine and provide recommendations for improvements to improve machine performance or Overall Equipment Effectiveness (OEE). The approach used is Total Productive Maintenance by calculating the OEE value. Problem analysis was carried out through Focus Group Discussion with expert judgment when determining the Failure Mode Effect Analysis (FMEA) analysis. Research findings indicate that the cause of the engine is not optimal comes from abnormal heat in the gearbox and machine spindle components caused by operator error when inputting data. The definite improvement is making a special schedule for maintenance, checking all maintenance schedules, and conducting training to increase knowledge and skills. The results of the corrective action show an increase in the OEE value from 70.8% to 79.9% or an increase of 9.1%, so the increase in the OEE value will increase the efficiency of the BT-A16 CNC lathe.

Performance of Al2O3/TiO2 Hybrid Nano-Cutting Fluid in MQL Turning Operation via RSM Approach

Cutting fluids can be used to cool workpieces at high cutting speeds and remove chips from cutting zones. The effectiveness of cutting fluids may be improved with the addition of hybrid nanoparticle dispersion. This study evaluates the effectiveness of an Al2O3-TiO2 hybrid as a cutting fluid in turning operations. The Al2O3-TiO2 hybrid nano-cutting fluid was prepared using a one-step method in computer numerical control (CNC) coolant with concentrations of up to 4%. Utilizing air-assisted nano-cutting fluids injected through a minimum quantity lubrication (MQL) setup, the effectiveness of turning cutting performance, cutting temperature (°C), average surface roughness (Ra), and tool wear (%) were evaluated. Then, the response surface method (RSM) was utilized as the design of experiment (DOE) to optimize the turning cutting performance parameters. The combination of 4% hybrid nano-cutting fluid concentration, 0.1 mm/rev feed rate, and 0.55 mm depth of cut yielded the lowest cutting temperature, surface roughness, and tool wear values of 25.3 °C, 0.480 µm, and 0.0104%, respectively. The 4% concentration of Al2O3/TiO2 hybrid nano-cutting fluid inclusion achieved the highest surface roughness reduction that led to better surface finish and the lowest tool-wear reduction led to longer tool life. Therefore, Al2O3/TiO2 hybrid nano-cutting fluids were strongly recommended in turning operations for CNC lathes.

Conceiving a Digital Twin for a Flexible Manufacturing System

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.

An Efficient IIoT Gateway for Cloud–Edge Collaboration in Cloud Manufacturing

The cloud manufacturing system can provide consumers with on-demand manufacturing services, which significantly improve the utilization rate of distributed manufacturing resources and the response speed of personalized product needs. In the cloud manufacturing platform, the successful implementation of various industrial applications relies on the uploading and streaming of related field-level manufacturing data. For example, the realization of manufacturing service composition application should match the manufacturing tasks with distributed manufacturing resources according to their working state data and performance measurement data. Therefore, this paper proposes a data integration and analysis framework of a cloud manufacturing system based on cloud–edge collaboration and the Industrial Internet of Things (IIoT). A service-oriented information model is established to uniformly describe the related operational data and functional attributes of heterogeneous manufacturing resources. Secondly, a real-time transmission and integration method of high-volume operational field and sensor data based on message middleware is proposed to realize the remote monitoring of distributed manufacturing resources and efficient distribution of related data. Finally, a cloud–edge collaboration mechanism is put forward to train and update the parameters of various artificial intelligence models deployed at edge gateways. In the experiment, taking the computer numerical control (CNC) lathe as an example, the effectiveness of the proposed manufacturing resource access method is verified. Taking the fault diagnosis model of the CNC lathe as an example, the efficiency of the proposed cloud–edge collaboration mechanism for model updating is verified.

Performance Assessment of Fiber-Reinforced Concrete Produced with Waste Lathe Fibers

The amount of steel waste produced is on the increase due to improvements in steel manufacturing industries. The increase in such wastes causes significant environmental problems and, furthermore, a large area is also required to store these waste products. Instead of disposing of these wastes, the reuse of them in different industries is an important success in terms of both reducing environmental pollution and providing low-cost products. From this motivation, the effect of lathe scrap fibers generated from Computer Numerical Control (CNC) lathe machine tools on concrete performance was investigated in this study. Pursuant to this aim and considering different fiber content, an experimental study was conducted on some test specimens. Workability and slump values of concrete produced with different lathe scrap fibers were determined, and these properties were compared with those of plain concrete. For the hardened concrete, 150 mm × 150 mm × 150 mm cubic specimens and cylindrical specimens with a diameter of 100 mm and a height of 200 mm were tested to identify compressive strength and splitting tensile strength of the concrete produced with different volume fracture of lathe waste scrap (0%, 1%, 2% and 3%). With the addition of the lathe scrap, the compressive and splitting tensile strength of fiber-reinforced concrete increases, but after a certain value of steel fiber content, there is a decrease in workability. Next, a three-point bending test was carried out on samples with dimensions of 100 × 100 × 400 mm and a span length of 300 mm to obtain the flexure behavior of different mixtures. It has been shown that the flexural strength of fiber-reinforced concrete increases with an increasing content of waste lathe. Furthermore, microstructural analysis was performed to observe the interaction between lathe scrap fiber and concrete. Good adhesion was observed between the steel fiber and cementitious concrete. According to the results obtained, waste lathe scrap fiber also worked as a good crack arrestor. Lastly, practical empirical equations were developed to calculate the compressive strength and splitting tensile strength of fiber-reinforced concrete produced with waste lathe scrap.

Experimental Investigation of Machining Time and Optimization of Machining Parameters Using RSM and Genetic Algorithm (GA) on 2205-Duplex Stainless Steel

Duplex stainless steel has become one of the fastest-growing materials in the stainless steel family due to pitting resistance, stress-corrosion cracking, the combination of excellent mechanical properties, production features, and the area of applications such as oil and gas, nuclear and thermal power plants, chemical processing industries, saltwater processing industries, and pipeline systems. However, it is more difficult to machine due to its high toughness, low thermal conductivity, and ductility. The experiment has conducted using 2205- Duplex Stainless steel round bar material considering carbide cutting tools using Computer Numerical Control lathe to estimate machining time to address and meet the industrial need. Using Central Composite Designed by using Response Surface Methodology technique develops a second-order mathematical model based on the machining parameters. The Analysis of Variance technique was used to investigate the material's performance characteristics, and the impact of cutting parameters on the work piece was analyzed using the Design Expert-V12 software. Cutting speed is the most crucial determining factor compared to other factors. The Genetic Algorithm is trained and tested in MATLAB to evaluate the best possible solutions. The genetic Algorithm recommends the most outstanding lowest predicted value of 1.2204 mm. The confirmatory analysis shows the experimental values, and their error percentage is within ±2%; these shows indicated predicted values are very close to the Genetic Algorithm results. The conclusions were in good agreement with the experimental machining time values.