Design considerations for robotically assembled through-tenon timber joints

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.

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.

Many techniques for the acceleration and deceleration of industrial robots and computer numerical control (CNC) machine tools have been proposed in order to make industrial robots and CNC machine tools perform given tasks efficiently. Although the techniques selecting polynomial functions can generate various acceleration and deceleration characteristics, the major problem is the computational load. The digital convolution techniques are more efficient than the techniques selecting polynomial functions. However, neither velocity profiles of which the deceleration characteristics is independent from the acceleration characteristics nor those of which the acceleration interval is different from the deceleration interval can be generated by the digital convolution techniques. This paper proposes a generalized approach for generating velocity profiles that cannot be generated by the digital convolution techniques. According to the desired characteristics of acceleration and deceleration, each set of coefficients is calculated and is stored. Given a moving distance, and acceleration and deceleration intervals, a velocity profile having the desired characteristics of acceleration and deceleration can be efficiently generated by using these coefficients. Several velocity profiles generated by the proposed technique are applied to one single-axis control system.

CNC PCB Milling and Wood Engraving Machine incorporates the plan and usage of a CNC (Computer Numerical Control) machine to make PCB (Printed Circuit Board) and wood etching machine in a solitary set up. Creation of PCB in the present market is intensely costly when manufacture of testing circuits is considered. Along these lines, this CNC PCB Milling machine would be an effective instrument through which circuit can be scratched and penetrated at a reasonable cost. The creation of this CNC machine is to diminish the cost and multifaceted nature of machine. This venture manages the plan of programmed scaled down CNC machine for PCB drawing and processing. The proposed venture would be profoundly practical in contrast with that of customary manufacture hardware.

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.

Previous techniques for accelerating and decelerating industrial robots and computer numerical control (CNC) machine tools either cannot be computed in real-time, fail to generate various motion profiles, or cannot fully utilise the maximum values of velocity and acceleration. An efficient technique is proposed that has been designed to generate various motion profiles that fully utilise the maximum values of velocity and acceleration.

In this paper, an integrated system to control and manage a state-of-the-art industrial computer numerical control (CNC) machine (Studer S33) using a commercially available tablet (Samsung Galaxy Tablet S2) is presented as a proof of concept (PoC) for the ubiquitous control of industrial machines. As a PoC, the proposed system provides useful insights to support the further development of full-fledged systems for Industrial Internet of Things (IIoT) applications. The proposed system allows for the quasi-decentralisation of the control architecture of conventional programmable logic controller (PLC)-based industrial control systems (ICSs) through data and information exchange over the transmission control protocol and the internet protocol (TCP/IP) suite using multiple agents. Based on the TCP/IP suite, a network device (Samsung Galaxy Tablet S2) and a process field net (PROFINET) device (Siemens Simatic S7-1200) are interfaced using a single-board computer (Raspberry Pi 4). An override system mainly comprising emergency stop and acknowledge buttons is also configured using the single-board computer. The input signals from the override system are transmitted to the PROFINET device (i.e., the industrial control unit (ICU)) over TCP/IP. A fully functional working prototype is realised as a PoC for an integrated system designated for the wireless and ubiquitous control of the CNC machine. The working prototype as an entity mainly comprises a mobile (handheld) touch-sensitive human-machine interface (HMI), a shielded single-board computer, and an override system, all fitted into a compact case with physical dimensions of 300 mm by 180 mm by 175 mm. To avert potential cyber attacks or threats to a reasonable extent and to guarantee the security of the PoC, a multi-factor authentication (MFA) including an administrative password and an IP address is implemented to control the access to the web-based ubiquitous HMI proffered by the PoC.

A realtime curvature-smooth interpolation scheme and motion planning for CNC machining of short line segments

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.

CNC (Computer Numerical Control) machines are central to modern manufacturing processes, offering high precision, repeatability, and productivity. However, their unplanned failures can severely impact throughput and operational cost. Conventional maintenance strategies—reactive and time-based preventive—are inadequate in dynamic industrial environments. Moreover, existing cloud-based predictive systems suffer from latency, network dependency, and privacy challenges. This work presents an Edge-AI-based predictive maintenance architecture that locally analyzes real-time IoT sensor data from CNC machines using compact AI models deployed on Raspberry Pi edge devices. The proposed system employs vibration, temperature, and current sensors to collect equipment health data and utilizes algorithms such as Isolation Forest and Long Short-Term Memory (LSTM) for anomaly detection and Remaining Useful Life (RUL) estimation. Experimental simulation demonstrates real-time fault detection and maintenance scheduling without cloud reliance. The solution promises reduced downtime, enhanced reliability, and cost-efficient manufacturing continuity.

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.

Incremental Sheet Forming is a rather new forming process which is used to obtain complex three dimensional parts by stretching the sheet with a pin tool whose path is controlled by a CNC machine. This study presents a new fixture design for Single Point Incremental Forming (SPIF) and experiments conducted for two profiles. Single Point Incremental Forming is used for small and batch production. The fixture presented in this study was made using mild steel which can be used for performing multiple forming purposes. The circular profile and a square profile were generated using a spherical head tool of 5mm diameter made of Stainless steel and the results were compared with that of Simulations for the same profiles using the analysis software, ABAQUS. The experiments were carried out using Computer Numerical Control (CNC) machine.