Search Results (154)

Barrel tools are increasingly used in high-precision machining of free-form surfaces. However, limited studies evaluate their performance specifically on spherical geometries, where tool–surface contact characteristics differ significantly. Understanding how tool geometry and process parameters influence surface quality and cutting forces in such cases remains underexplored. This study evaluates how barrel cutter radius and varying machining parameters affect cutting forces and surface roughness when milling internal and external spherical surfaces. Machining tests were conducted on structural steel 1.1191 using two barrel cutters with different curvature radii (85 mm and 250 mm) on a 5-axis CNC machine. Feed per tooth and radial depth of cut were systematically varied. Cutting forces were measured using a dynamometer, and surface roughness was assessed using the Rz parameter, which is more sensitive to peak deviations than Ra. Novelty lies in isolating spherical surface shapes (internal vs. external) under identical path trajectories and systematically correlating tool geometry to force and surface metrics. The larger curvature tool (250 mm) consistently generated up to twice the cutting force of the smaller radius tool under equivalent conditions. External surfaces showed higher Rz values than internal ones due to less favorable contact geometry. Radial depth of the cut had a linear influence on force magnitude, while feed rate had a limited effect except at higher depths. Smaller-radius barrel tools and internal geometries are preferable for minimizing cutting forces and achieving better surface quality when machining spherical components. The aim of this paper is to determine the actual force load and surface quality when using specific cutting conditions for internal and external spherical machined surfaces. Full article

Compliant mechanisms are often utilized in precise positioning systems but have not been thoroughly examined in vibration-aided fine CNC machining. This study aims to develop a new 02-DOF flexure stage for vibration-aided fine CNC milling. A hybrid displacement amplifier, featuring a two-lever mechanism, two Scott–Russell mechanisms, and a parallel leading mechanism, was integrated into a symmetric perpendicular series configuration to create an innovative design. The pseudo-rigid body model (PRBM), Lagrangian approach, finite element analysis (FEA), and Firefly optimization algorithm were employed to develop, verify, and optimize the quality response of the new positioner. The PRBM and Lagrangian methods were used to construct an analytical model, while finite element analysis was used to validate the theoretical solution. The primary natural frequency results from theoretical and FEM methods were 318.16 Hz and 308.79 Hz, respectively. The difference between these techniques was 3.04%, demonstrating a reliable modelling strategy. The Firefly optimization approach applied mathematical equations to enhance the key design factors of the mechanism. The prototype was then built, revealing an error of 7.23% between the experimental and simulated frequencies of 331.116 Hz and 308.79 Hz, respectively. The specimen was subsequently mounted on the fabricated optimization positioner, and vibration-assisted fine CNC milling was performed at 100–1000 Hz. At 400 Hz, the specimen achieved ideal surface roughness with a Ra value of 0.187 µm. The developed design is a potential structure that generates non-resonant frequency power for vibration-aided fine CNC milling. Full article

This paper presents the design and validation of a novel specialized fixture device for machining inclined planes with barrel cutters on 3-axis CNC machine tools. Barrel milling, also known as Parabolic Performance Cutting (PPC), is extensively used on 5-axis machines to enhance the efficiency of machining complex surfaces. While significant research has focused on optimizing barrel milling for aspects such as surface roughness and cutting forces, implementing this technique on 3-axis machines poses a challenge due to limitations in tool orientation. To overcome this, an innovative adaptable device was designed, enabling precise workpiece orientation relative to the barrel cutter. To overcome this limitation, an adaptable device was designed that enables precise workpiece orientation relative to the barrel cutter. The device utilizes interchangeable locating elements for different cutter programming angles (such as 18°, 20°, and 42.5°), thereby ensuring correct workpiece positioning. Rigid workpiece clamping is provided by the device’s mechanism to maintain precise workpiece positioning during machining, and probing surfaces are integrated into the device to facilitate the definition of the coordinate system necessary for CNC machine programming. Device control was performed using a Hexagon RA-7312 3D measuring arm. Inspection results indicated minimal dimensional deviations (e.g., surface flatness between 0.002 mm and 0.012 mm) and high angular accuracy (e.g., angular non-closure of 0.006°). The designed device enables the effective and precise use of barrel cutters on 3-axis CNC machines, offering a previously unavailable practical and economical solution for cutting tool tests and cutting regime studies. Full article

This paper presents LokAlp, a modular timber construction system invented and developed by the authors, inspired by the traditional Blockbau technique, and designed for circularity and self-construction. LokAlp utilizes standardized interlocking blocks fabricated from CLT and GLT off-cuts to optimize material reuse and minimize waste. The study explores the application of massive timber digital materials within an open modular system framework, offering an alternative to the prevailing focus on lightweight structural systems, which predominantly rely on primary engineered wood materials rather than reclaimed by-products. The research evaluates geometric adaptability, production feasibility, and on-site assembly efficiency within a computational design and digital fabrication workflow. The definition of the LokAlp system has gone through several iterations. A full-scale demonstrator constructed using the LokAlp final iteration (Mk. XII) incorporated topological enhancements, increasing connection variety and modular coherence. Comparative analyses of subtractive manufacturing via 6-axis robotic milling versus traditional CNC machining revealed a >45% reduction in cycle times with robotic methods, indicating significant potential for sustainable industrial fabrication; however, validation under operational conditions is still required. Augmented reality-assisted assembly improved accuracy and reduced cognitive load compared to traditional 2D documentation, enhancing construction speed. Overall, LokAlp demonstrates a viable circular and sustainable construction approach combining digital fabrication and modular design, warranting further research to integrate robotic workflows and structural optimization. Full article

In computer numeric controller (CNC) milling machining, air-cutting, where the tool moves without engaging the material, will reduce the machining efficiency. This study proposes a novel methodology to detect and minimize non-productive (air-cutting) time in real-time using spindle load monitoring, vibration signal analysis, and NC code tracking. A logic-based benders decomposition (LBBD) approach was used to optimize toolpath segments by analyzing air-cutting occurrences and dynamically modifying the NC code. Two optimization strategies were proposed: increasing the feedrate during short air-cutting segments and decomposing longer segments using G00 and G01 codes with positioning error compensation. A human–machine interface (HMI) developed in C# enables real-time monitoring, detection, and minimization of air-cutting. Experimental results demonstrate up to 73% reduction of air-cutting time and up to 42% savings in total machining time, validated across multiple scenarios with varying cutting parameters. The proposed methodology offers a practical and effective solution to enhance CNC milling productivity. Full article

Face milling with end-mill tools represents a solution for woodworking applications on small-scale or complex surfaces, but information regarding the surface quality per specific tool type, wood material, and processing parameters is still limited. Therefore, this study examined the surface quality of tangential oak and maple CNC face-milled with two end-mill tools—straight-edged and helical—for three values of stepover (5, 7, 9 mm) and two cutting depths (1 and 3 mm). The surface quality was analyzed with roughness parameters, roughness profiles, and stereomicroscopic images and was referenced to that of very smooth surfaces obtained by super finishing. The helical end mill caused significant fiber tearing in maple and disrupted vessel outlines, while prominent tool marks such as regular ridges across the grain were noticed in oak. The best surface roughness was obtained in the case of the straight-edged tool and minimum stepover and depth of cut, which came closest to the quality of the shaved surfaces. An increase in the cutting depth generally increased the core surface roughness and fuzziness, for both tools, and this trend increased with an increase in the stepover value. The species-dependent machining quality implies that the selection of tool geometry and process parameters must be tailored per species. Full article

The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications. Full article

Fused Deposition Modeling (FDM) has evolved from a rapid prototyping technique to an established manufacturing process for various industrial applications, including aerospace, robotics, biomedical engineering, and food production. Despite its versatility, the surface quality and dimensional accuracy of FDM-printed parts remain significant challenges, limiting their applicability in high-performance and precision-driven industries. Some of the primary limitations of FDM are volumetric error, shape deviation, and surface roughness, which directly affect the mechanical properties and functional performance of printed components. Post-processing techniques are available to mitigate these problems. Among the available post-processing techniques, CNC machining has emerged as a viable solution for improving the surface finish and dimensional precision of FDM parts. The integration of subtractive CNC machining with additive FDM printing enables the development of hybrid manufacturing strategies, leveraging the design freedom of 3D printing while ensuring superior surface quality. This paper presents a comprehensive review of recent studies on CNC post-processing of FDM-printed parts, analyzing its impact on surface roughness, dimensional accuracy, and material properties. Additionally, key process parameters influencing the effectiveness of CNC machining are discussed. Full article

In the present study, an approach for determining the different states of ball burnishing (BB) operations aimed at forming regular reliefs’ patterns on planar surfaces is introduced. The methodology involves acquiring multi-axis accelerometer data from CNC-driven milling machine to capture the dynamics of the BB tool and workpiece, mounted on the machine table. Following data acquisition from an AISI 304 stainless steel workpiece, which is subjected to BB treatments at different toolpaths and feed rates, the recorded signals are preprocessed through noise reduction techniques, DC component removal, and outlier correction. The refined data are then transformed using a root mean square (RMS) operation to simplify further analysis. A Gaussian Mixture Model (GMM) is subsequently employed to decompose the compressed RMS signal into distinct components corresponding to various operational states during BB. The experimental trials at feed rates of 500 and 1000 mm/min reveal that increased feed rates enhance the distinguishability of these states, thus leading to an augmented number of statistically significant components. The results obtained from the proposed GMM based algorithm applied on compressed RMS accelerations signals is compared with two other methods, i.e., Short-Time Fourier Transforms and Continuous Wavelet Transform. The results from the comparison show that the proposed GMM method has the advantage of segmenting three to five different states of the BB-process from nonstationary accelerations signals measured, while the other tested methods are capable only to distinguish the state of work of the deforming tool and state of its rapid (re-)positioning between the areas of working, when there is no contact between the BB-tool and workpiece. Full article

We developed an automatic optical inspection (AOI) system for detecting defects in finished workpieces and determining the parameters for CNC machining. The system addresses quality control issues in CNC machining using image processing, machine learning, and G-code analysis techniques. The accuracy and efficiency of CNC machining were improved by reducing manual inspection tasks, minimizing production downtime, and achieving higher precision in defect detection and correction. Experiments were conducted in a pre-planned CNC machining environment to validate the effectiveness of the proposed AOI system. The system was tested on metals and composites and CNC lathes and milling machines. The AOI system significantly improved defect detection accuracy, exceeding 95% across different defect types. The proposed machining parameters enabled a reduction in the recurrence rate of defects by approximately 80%, demonstrating the potential to enhance overall machining quality. By developing AOI recognition and optimizing CNC machining parameters, an automated and intelligent defect detection and correction solution was realized. The reliability and accuracy of CNC processes were improved, and data-driven automated manufacturing and process optimization were achieved, meeting the goals of intelligent manufacturing and Industry 4.0. Full article

This work aimed to develop both a real implementation and a digital twin for a small CNC machining center. The X-, Y-, and Z-axes feed systems were realized as closed-loop motion loops with DC servo motors and encoders. Motion control was provided by Arduino boards and Pololu motor drivers. A simulation study of the step response parameters was carried out, and then the positioning regime was studied, followed by the two-axis simultaneous motion regime (circular interpolation). This study, based on a hybrid simulation diagram realized in Simulink–Simscape, allowed a preliminary tuning of the PID (proportional integral derivative) controllers. Next, the CAE (computer-aided engineering) simulation diagram was complemented with the CAM (computer-aided manufacturing) simulation interface, the two together forming an integrated digital twin system. To validate the contouring performance of the proposed CNC system, a circular groove with an outer diameter of 31 mm and an inner diameter of 29 mm was machined using a 1 mm cylindrical end mill. The trajectory followed the simulated 30 mm circular path. Two sets of controller parameters were applied. Dimensional accuracy was verified using a GOM Atos Core 200 optical scanner and evaluated in GOM Inspect Suite 2020. The results demonstrated good agreement between simulation and physical execution, validating the PID tuning and system accuracy. Full article

In the context of growing environmental concerns and the demand for more sustainable manufacturing practices, this study evaluates the environmental and economic performance of two production routes for a stainless steel support block used in steel mills. A comparative Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) were conducted to assess a conventional subtractive manufacturing process based on Computer Numerical Control (CNC) machining versus a hybrid approach that combines Plasma Arc-Wire Arc Additive Manufacturing (PA-WAAM) with CNC finishing. The LCA was carried out using ReCiPe 2016 Midpoint and Endpoint methodologies in SimaPro, while the LCC employed a cradle-to-gate cost model. Results showed that the hybrid WAAM-CNC route reduced average environmental impacts by 49% across 18 categories and decreased steel consumption by approximately 70% due to near-net-shape fabrication. Although the hybrid method incurred an approximate 3.5 times increase in unit production cost, this was primarily attributed to equipment investment. In contrast, operational costs such as labor, materials, and consumables were significantly lower—by 66%, 28%, and 45%, respectively. These findings support the hybrid approach as a more sustainable manufacturing alternative with the potential for long-term cost optimization as additive technologies mature. Full article

Accurately predicting tool wear in real time is crucial to enhance the tool prognostics and health monitoring system in computerized numerical control (CNC) machining. This paper proposed a novel integrated deep learning model for predicting the wear of milling tools by fusing multi-sensor features. The raw signals of vibration and cutting force acquired from the continuous cutting cycle were used to extract multi-sensor features throughout the full lifecycle of the milling tools in time, frequency, and wavelet domains, respectively. The sensitive features from these signals were identified through correlation analysis and used as input for the stacked sparse autoencoder (SSAE) model with backpropagation neural network (BPNN) as the regression layer to predict tool wear. SSAE models with different activation function configurations of hidden layers were utilized to construct deep neural network models with different prediction performance, which were taken as primary learners of integrated deep learning model. The intergrated SSAE model based on the stacking learning strategy applied the gradient boosting decision tree (GBDT) regression model with Bayes optimized hyperparameters as the secondary learner to predict tool wear. Compared to the single SSAE model and shallow machine learning models, the proposed method significantly improved both the prediction accuracy and reliability. Full article

CNC machine and cutting tools are an indispensable part of the cutting process. Their life default diagnosis is related to the efficiency of the entire production process, which ultimately impacts economic performance. Many methods provided by deep learning articles have been verified for use on large cutting datasets and can help in diagnosing tools’ lifetime well; however, on small samples, the challenge of learning difficulties still emerges. The rise in large language models (LLMs) has brought changes to tool life diagnosis. This study proposes a fault diagnosis algorithm based on GLM-4, and the experimental validation on the PHM 2010 dataset and a proprietary milling cutter dataset demonstrates the superiority of the proposed model, achieving diagnostic accuracies of 93.8% and 93.3%, respectively, outperforming traditional models (SVM, CNN, RNN) and baseline LLMs (ChatGLM2-6B variants). Further robustness and noise-resistance analyses confirm its stability under varying SNR levels (10 dB to −10 dB) and limited training samples. This work highlights the potential of integrating domain-specific feature engineering with LLMs to advance intelligent manufacturing diagnostics. Full article

Real-time parametric interpolation plays a crucial role in achieving high-speed and high-precision multi-axis CNC machining. In the interpolation cycle, the position of the next interpolation point is required to be calculated in real-time to guide the action of the machining process. Due to the existence of the positioning error of the interpolation point, it is extremely difficult to eliminate the feedrate fluctuation, which may lead to dramatic decreases in machining quality and the driving capabilities’ saturation of each axis. A computationally efficient and precise feedrate fluctuation minimization method is proposed for the NURBS tool path interpolation in the CNC milling process. The model for the arc length and curvature, with respect to the parameter of the NURBS tool path, is established to reduce the calculation amount required by interpolation points determination. The deviation between the theoretical and actual interpolation step length is decreased by the proposed arc length compensation method to minimize the feedrate fluctuation. In addition, the interpolation points derived from the arc length compensation process are further corrected by performing the Newton iteration to restrict the feedrate fluctuation within the preset accuracy threshold. The effectiveness and superiorities of the proposed feedrate fluctuation minimization method are verified by simulation and milling experiments. Full article