High-Speed Machining for CNC Milling Simulation Using CAM Software

High-speed and high-accuracy control technology has been developed in Japan and applied to CNC (Computer Numerical Control) machine tools. Therefore, a CNC machine tool, which is made in Japan, has shown a successful history of its performance. Further development of these technologies is important for Japan to maintain its leadership in advanced manufacturing. The purpose of this research is to develop a high-speed and high-accuracy machining system. Two methods are implemented in the proposed prototype system. The first method is “geometric-model interpolation,” which is used to generate high-speed and highaccuracy command positions, and the second method is “predictive compensation,” which is used to correct machining errors. In the proposed prototype system, these errors are simulated preliminarily and compensated for in the servo data by controlling the position and the feed rate. This report describes the configuration of the proposed prototype system, and the performance of the prototype is evaluated by comparing its speed and accuracy with an existing commercial CNC machine tool. The results show that the prototype system is able to control a CNC machine tool with higher speed and greater accuracy than current CNC machine tool systems.

The enveloping surface is formed of characteristic lines (also called contact line) at all different enveloping moment, a novel CNC (computerized numerical control) machining method based on enveloping characteristic for enveloping surface is proposed in this study. In the proposed method, the characteristic line is the machining unit, the CNC machining is driven by the characteristics lines, the CNC machining tool path distribution along the characteristics lines rather than the traditional CNC machining tool path such as contour layered,etc. In addition, the envelope characteristic automatic recognition algorithm and kinematics model of CNC machining are proposed to achieve the CNC machining based on enveloping characteristic. Finally, the proposed CNC machining method and traditional CNC machining method for single-enveloping surface and double-enveloping surface are conducted as, for instance, the machining experiment results confirm that the proposed methodology enables the machining precision and efficiency to be successfully improved. Hence, the novel CNC machining method proposed in this study is an effective technology to further improve the machining precision and efficiency of enveloping surface.

Sintered metals as flame traps

Traditional Computer Numerical Control (CNC) machines use ISO6983 (G/M code) for part programming. G/M code has a number of drawbacks and one of them is lack of interoperability. The Standard for the Exchange of Product for NC (STEP-NC) as a potential replacement for G/M code aims to provide a unified and interoperable data model for CNC. In a modern CNC machine tool, more and more motors, actuators and sensors are implemented and connected to the NC system, which leads to large quantity of data being transmitted. The real-time Ethernet field-bus is faster and more deterministic and can fulfill the requirement of data transmission in the high-speed and high-precision machining scenarios. It can provide more determinism on communication, openness, interoperability and reliability than a traditional field-bus. With a traditional CNC system using G/M code, when the machining is interrupted by incidents, restarting the machining process is time-consuming and highly experience-dependent. The proposed CNC controller can generate just-in-time tool paths for feature-based machining from a STEP-NC file. When machining stoppage occurs, the system can recover from stoppage incidents with minimum human intervention. This is done by generating new tool paths for the remaining machining process with or without the availability of the original cutting tool. The system uses a real-time Ethernet field-bus as the connection between the controller and the motors.

Additive manufacturing techniques are replacing conventional subtractive machining processes; however, the surface quality and defects have been a key roadblock to expanding AM’s uses. This paper describes experimental investigations in the high-speed dry machining and additive manufacturing (AM) of titanium alloy (Ti6Al4V), discussing the effect of machining and AM conditions on the surface characteristics due to the micro-deformation layer. Analysis of the machined surfaces shows the deposition of microparticles at a high cutting speed of 170 m/min at moderate feed rates. The predominant thermal softening effect at a high cutting speed causes restructuring of the micro-deformation layer. Thus, the machined surface shows fewer alterations and a correspondingly lower surface roughness. A high cutting speed also favors the induction of high residual stresses that are compressive. Shallow grooves are seen throughout the surface along the feed spacing with a higher depth of cut of 0.8 mm. An increase in the cutting speed from 170 m/min to 190 m/min leads to a 61% increase in the surface finish owing to a rise in machining temperature leading to thermal softening, and subsequent restructuring of the machined surface layer occurs. For the feed rate, the surface finish values decrease gradually as the feed rate increases, and the worst finish of 1.37 µm is attained at a feed rate of 0.875 mm/rev. This study also compares different AM processes for Ti6Al4V based on the defects and their effects on mechanical properties, such as tensile and fatigue strength. It was observed that the ultimate tensile strength and the yield strength were approximately 20% more in SLM and direct energy deposition as compared to electron beam melting. The mechanism of these effects is also explained by elaborating on the influence of grain size, phase, and other microstructural behaviors on the final mechanical properties of the produced part.

Any field will strive for continuous development; this is not only in the aspect of economic but also in the technological and environmental standpoint. Nowadays, technological advancement is embedded with environmental concern too; for instance, the computer numerical control (CNC) machining means of manufacture is reported to have more sustainable feature over the conventional machining means of manufacture. However, the field survey across Fiji manufacturing industries revealed that there was less usage of CNC machines. Also, it reveals that there is less awareness on the sustainability standpoint. However, the aim of the study is about identifying the current barriers and understand the nature of those barriers that limits machining industries progress towards CNC usage. Several barriers are identified from the literature survey, which was narrowed down to eight essential barriers by conducting a questionnaire survey with industrial and academic experts. These barriers are analysed using interpretive structural modelling and decision‐making trial and evaluation laboratory hybrid approach. The discussion is made to understand the interrelationship or the cause and effect relationship between the barriers. The outcomes of this study create awareness in education to increase graduates or technicians understanding CNC machines and to align curriculum accordingly for industrial applications.

Computer Numerical Control (CNC) machining is a subtractive manufacturing technique that removes layers of material from a blank or workpiece to create a specific product. With increasing global competition, minimizing downtime during production is essential to maximize machine availability and productivity. This study investigates the application of machine learning models, specifically Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to forecast CNC machine downtime from multiple failure sources. The study uses data collected from 16 CNC machines at Company A in Malaysia over an extended period. The data contain key variables for each downtime event, such as machine ID, failure type, start date/time, end date/time, and downtime duration in minutes. Failure types are categorized into several groups, including mechanical, electrical, and tool malfunctions. After hyperparameter tuning, the XGBoost model outperformed the RF model, achieving a Mean Squared Error (MSE) of 0.4017, Root MSE (RMSE) of 0.634, and Mean Absolute Error (MAE) of 0.470 on the test set, while the RF model yielded higher errors, with an MSE of 1.2654, RMSE of 1.125, and MAE of 0.943. These results demonstrate the superiority of the XGBoost model over RF in predicting future CNC downtime, as indicated by its lower prediction errors. Future work should focus on refining the model with larger, more diverse datasets and exploring its integration into AI-based decision support systems to enhance machine availability and operational efficiency.

The aim of this research work is to offer a unique procedure of integrated Reverse Engineering (RE) and Additive Manufacturing (AM) technologies through implementation of a comprehensive experimental study to present an extensive comparison between applicable rapid technologies for blade rapid investment casting. Different direct and indirect AM techniques were used to produce sacrificial investment casting patterns. In addition, an aluminium mould of a blade for wax injection was fabricated using Computer Numerical Control (CNC) machining to compare conventional investment casting with the proposed rapid investment casting process. Dimensional inspection of cast blades showed that the MultiJet Modelling (MJM) method has the most economic justification and compatibility with blade rapid investment casting and it can be considered as an alternative to conventional wax precision CNC machining. On the other hand, sacrificial patterns produced by the Perfactory system showed lower applicability for investment casting due to pattern flexibility and difficulties in burn-out step. As for examined rapid tooling technologies, silicone rubber moulding was not a stable technique for small batch fabrication of blade wax patterns. In contrast, the results proved that epoxy resin tooling could make possible new cost-effective approaches for low volume production of gas turbine blades.

Computer numerically controlled (CNC) machines have long been known and used in various industrial sectors such as the aircraft, shipping, automotive, molding and others industries. Various types of existing CNC machines such as CNC turning, CNC milling, CNC shaping, CNC drilling, CNC laser cutting and others have been commonly used in various manufacturing industries. CNC machines for production units and training unit CNC machines can be used according to milling machining parameters, machine capacity, product quality targets and workpiece material types. In milling machining using a 3-A training unit CNC machine, the first step is to design a workpiece, design G and M codes, simulate it using software such as CAD / CAM and can also be done by simulating it with a pen plotter. After evaluating and the simulation results are in accordance with the workpiece design, it can be executed for product machining. Different designs of CNC machining products have different toolpaths. In general, there are three types of techniques for path generation tools, namely iso-parametric, iso-planner, and iso-scallop height. Tool path generation on UMSU CNC milling machining using the CNC TU-3A has been developed a toolpath generation suitable for the UMSU machining. The type of tool path generation technique to be used can be considered from several factors including the complexity of the product design, the type of milling tool used, product quality, machining time and other factors.

Five-axis machining has been widely used in aerospace, automobile and mold/die industries. It has many advantages compared to conventional three-axis machining, including higher metal removal rates, better surface finish, and more precise part surface in one setup. This research presents a new method for curve machining on five-axis Computer Numerical Control (CNC) machine tools. It uses the CNC interpolator approach, or called curve interpolator, which can produce accurate tool position as well as tool orientation. The interpolator calculates a new command in real time, the same time period needed for sampling the control-loop feedback devices. It performs trajectory planning and inverse kinematics transformation in each sampling period. To implement this curve interpolator, a 3-D parametric curve g-code must be defined for five-axis CNC machining. The comparisons for this real-time method and the conventional off-line method in terms of trajectory accuracy and feedrate variation are demonstrated in the end of this chapter.

Introduction. Modern mechanical engineering is characterised by a constant increase in the proportion of parts with complex configurations (bodies of revolution with rectilinear and curvilinear elements) manufactured using materials with special physical and mechanical properties. The production of such complex components requires high precision, flexibility, and efficiency. The complex shape and variety of technological manufacturing processes are most effectively determined by their efficient processing on automated multifunctional equipment with a central processing unit (CPU). Aim and tasks. This study aims to investigate the processing of complex-shaped details made of difficult-to-process materials on Computer Numerical Control (CNC) or CNC machining is the automated control of machine tools by a computer lathes to increase the efficiency of the technological process. Results. The efficiency of the technological process has been increased when processing complex-shaped parts made of difficult-to-process materials on CNC lathes, as a methodology has been developed for determining cutting modes using the criterion of “processing productivity”. The main task at the technological preparation stage of production is optimisation in determining the parameters of the cutting mode, based on the criterion of “durability at maximum productivity”. In the developed methodology for determining the cutting mode, the volume of material removed per unit time is used as a criterion for “processing productivity”, while simultaneously taking into account the relationship between ар, f, and vc through the cutting power and tool durability, which allows an increase in productivity of up to 74%. Multi-pass roughing is more effective for CNC lathes than single-pass, and maximum cutting productivity is achieved at maximum tool feed. Conclusions. The analysis showed that productivity gains from increasing the cutting depth are limited by machine power. Optimisation based on the "tool life at maximum productivity" criterion is effective only when the machine power is not a limiting factor. It was found that reducing the target tool life (T) does not always increase productivity. Reducing T is effective only in conditions where higher cutting speeds do not exceed the available machine power; otherwise, the actual tool life may even increase. The optimisation of T must consider machine power limitations: reducing T is effective only when higher cutting speeds do not exceed the permissible power.

Nowadays, computer numerical control (CNC) machine tool undertakes more processing tasks than other common machine tools because of its highly automated machining ability and high performance. However, due to the lack of intelligence in machining process planning, machining procedure of products mostly depends on process planners rather than CNC machine tools. To make product quality less dependable on process planner’s ability and improve the efficiency of process planning in order to fulfill changeable market, this paper presents an approach to design and develop CNC machining process knowledge base using cloud technology. The general standard STEP-NC is mapped to web ontology language (OWL) to describe machining process-related knowledge in a readable and comprehensible way. This mapping relation also makes knowledge suitable for storage in HBase. Through this ontology model, descriptive and logical knowledge can be collected. Hadoop platform is used in this approach to provide the NoSQL database HBase for large-scale knowledge storage and MapReduce programming model for large-scale knowledge processing. Taking advantage of MapReduce, knowledge query engine and reasoning engine can be developed. Users can submit task and resource descriptive files to the cloud through CNC controller and get machining process solutions from knowledge base. Evaluation mechanism is also adopted to filter low-quality knoweldge.

This paper presents a JavaScript-based approach for image-to-G-code conversion in CNC (Computer Numerical Control) machine control. The developed code enables the translation of images and text into machine-readable instructions for precise reproduction using CNC machines. The code implements image loading, preprocessing, binarization, thinning, and G-code generation functionalities. It offers adjustable parameters for CNC and image settings, allowing customization and optimization of the machining process. Experimental evaluations confirm the code's efficiency, accuracy, and usability. The results demonstrate reliable image preprocessing, effective binarization techniques, successful image thinning, and precise G-code generation. The code's accessibility, user-friendly interface, and real-time preview capabilities further enhance its usability. This JavaScript-based approach contributes to the integration of digital workflows into CNC machining, offering a promising solution for accurate and efficient fabrication.

High-quality material manufacture requires the improvement of the computer numerical control (CNC) machine performance. Such task can be achieved by considering the tool trajectory, in which it is important to minimize the acceleration and jerk levels. Previous research works are focused on providing smooth movements at most with C2 continuous trajectories, and the results of using C3 continuity in the trajectory are not evaluated. The contribution of this study is the design of a methodology for obtaining C3 tool trajectories while acceleration and jerk levels are constrained through adaptive feed-rate. C3 and C2 trajectories are executed in a CNC machine. Speed, acceleration, and jerk are monitored in order to validate the proposed methodology and to analyse and quantify the differences between using C2 and using C3 trajectories.

This paper introduces a new intelligent integration between an IoT platform and deep learning neural network (DNN) algorithm for the online monitoring of computer numerical control (CNC) machines. The proposed infrastructure is utilized for monitoring the cutting process while maintaining the cutting stability of CNC machines in order to ensure effective cutting processes that can help to increase the quality of products. For this purpose, a force sensor is installed in the milling CNC machine center to measure the vibration conditions. Accordingly, an IoT architecture is designed to connect the sensor node and the cloud server to capture the real-time machine’s status via message queue telemetry transport (MQTT) protocol. To classify the different cutting conditions (i.e., stable cutting and unstable cuttings), an improved model of DNN is designed in order to maintain the healthy state of the CNC machine. As a result, the developed deep learning can accurately investigate if the transmitted data of the smart sensor via the internet is real cutting data or fake data caused by cyber-attacks or the inefficient reading of the sensor due to the environment temperature, humidity, and noise signals. The outstanding results are obtained from the proposed approach indicating that deep learning can outperform other traditional machine learning methods for vibration control. The Contact elements for IoT are utilized to display the cutting information on a graphical dashboard and monitor the cutting process in real-time. Experimental verifications are performed to conduct different cutting conditions of slot milling while implementing the proposed deep machine learning and IoT-based monitoring system. Diverse scenarios are presented to verify the effectiveness of the developed system, where it can disconnect immediately to secure the system automatically when detecting the cyber-attack and switch to the backup broker to continue the runtime operation.