Search Results (569)

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

The increasing utilization of hard-to-cut materials in high-performance sectors such as aerospace and defense has pushed manufacturing systems to be flexible in processing large workpieces with a wide range of materials while also delivering high precision. Recent studies have highlighted the potential of integrating industrial robots (IRs) with electric discharge machining (EDM) to create a non-contact, low-force manufacturing platform, particularly suited for the accurate machining of hard-to-cut materials into complex and large-scale monolithic components. In response to this potential, a novel robotic EDM system has been developed. However, the manual programming and control of such a convoluted system present a significant challenge, often leading to inefficiencies and increased error rates, creating a scenario where the EDM process becomes unfeasible. To enhance the industrial applicability of this robotic EDM technology, this study focuses on a novel methodology to develop and validate a digital twin (DT) of the physical robotic EDM system. The digital twin functions as a virtual experimental environment for tool motion, effectively addressing the challenges posed by collisions and kinematic singularities inherent in the physical system, yet with proven 20-micron EDM gap accuracy. Furthermore, it facilitates a CNC-like, user-friendly offline programming framework for robotic EDM cutting path generation. Full article

In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters. Full article

Fused Filament Fabrication (FFF) enables the manufacturing of complex polymer components. However, surface finish and dimensional accuracy remain key limitations for their integration into functional assemblies. This study explores the potential of conventional turning as a post-processing strategy to improve the geometric and surface quality of PLA reinforced with carbon fiber (CF) parts produced by FFF. Machinability was evaluated through the analysis of cutting forces, thermal behavior, energy consumption, and surface integrity under varying cutting speeds, feed rates, and specimen slenderness. The results indicate that feed is the most influential parameter across all performance metrics, with lower values leading to improved dimensional accuracy and surface finish, achieving the most significant reductions of 63% in surface roughness (Sa) and 62% in cylindricity deviation. Nevertheless, the surface roughness is higher than that of metals, and deviations in geometry along the length of the specimen have been observed. A critical shear stress of 0.237 MPa has been identified as the limit for interlayer failure, defining the boundary conditions for viable cutting operation. The incorporation of CNC turning as a post-processing step reduced the total fabrication time by approximately 83% compared with high-resolution FFF, while maintaining dimensional accuracy and enhancing surface quality. These findings support the use of machining operations as a viable and efficient post-processing method for improving the functionality of polymer-based components produced by additive manufacturing. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery. Full article

In the highly competitive manufacturing environment of today, operational success depends on increasing efficiency and cutting waste. The goal of this research is to use Arena simulation software to model a CNC production system to assess and improve system performance. Three different parts are processed by the model, which also includes rework loops in which a portion of faulty products are sent back for further processing. Finding bottlenecks, evaluating important performance metrics such as output, queue lengths, waiting times, and machine utilization, and testing improvement scenarios are the primary goals. Study findings indicate that waiting times were greatly shortened and resource usage was balanced in alternative scenarios, which were accomplished by shifting workloads, line balancing, and modifying inter-arrival durations. The results demonstrate how well simulation can represent and resolve inefficiencies in intricate industrial systems. Manufacturers may optimize manufacturing processes without interfering with ongoing operations thanks to this method, which encourages educated decision-making. Full article

The reliability and safety of five-axis CNC abrasive water jet machining are critical for many industries. This study employs Failure Mode and Effects Analysis (FMEA) to identify and mitigate potential failures in this machining system. Traditional FMEA, which relies on crisp numerical values, often struggles with handling uncertainty in risk assessment. To address this limitation, this paper introduces an Interval-Valued Spherical Fuzzy FMEA (IVSF-FMEA) approach, which enhances risk evaluation by incorporating membership, non-membership, and hesitancy degrees. The IVSF-FMEA method leverages the inherent rotational symmetry of interval-valued spherical fuzzy sets and the permutation symmetry among severity, occurrence, and detectability criteria, resulting in a transformation-invariant and unbiased risk assessment framework. Applying IVSF-FMEA to seven periodic failure (PF) modes in five-axis CNC water jet machining achieves a more precise prioritization of risks, leading to improved decision-making and resource allocation. The findings highlight improper fixturing of the workpiece (PF6) as the most critical failure mode, with the highest RPN value of −0.54, followed by mechanical vibrations (PF2) and tool wear and breakage (PF1). This indicates that ensuring proper fixturing stability is essential for maintaining machining accuracy and preventing defects. Comparative analysis with traditional FMEA demonstrates the superiority of the proposed fuzzy-based approach in handling subjective assessments and reducing ambiguity. The findings highlight improper fixturing, mechanical vibrations, and tool wear as the most critical failure modes, necessitating targeted risk mitigation strategies. This research contributes to advancing risk assessment methodologies in complex manufacturing environments. Full article

The research presented herein investigates the impact of axial force and feed rate in the Friction Stir Welding (FSW) of aluminum alloy 6061-T6 in a GROB G552 horizontal 5-axis CNC machine with adaptive torque control enabled. The purpose of this study is to further advance the performance and characteristics of FSW aluminum alloys in 5-axis CNCs, particularly in conjunction with adaptive torque control. The Taguchi and ANOVA methods were utilized to define parameter tables and analyze the resulting data. Optical microscopy and tensile tests were performed on the welded samples to evaluate weld quality. The results from this study provide clear evidence that axial force has a significant effect on tensile strength in FSW AA6061-T6. The maximum UTS found in this study, welded with an axial force of 9.4 kN, retained 69% tensile strength of the base material. Conversely, a decrease in strength and an increase in void formation was found at higher feed rates with this force. Ideal welds, with minimal defects across all feed rates, were performed with an axial force of 8.3 kN. A feed rate of 300 mm/min at this force resulted in a 67% base metal strength. These findings contribute to improving joint strength and application efficiency in FSW AA6061-T6 performed in a horizontal 5-axis CNC machine where adaptive torque control is enabled. Full article

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic experimental comparison was conducted between a manual Digital Vernier Caliper (DVC) and an automated Coordinate Measuring Machine (CMM) using five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Dimensional measurements were taken from five consecutive machining zones to capture localized metrological behaviors. The results indicated that the CMM consistently achieved lower expanded uncertainty (as low as 0.00166 mm) and minimal propagated uncertainties (≤0.0038 mm), regardless of material hardness or cutting position. In contrast, the DVC demonstrated significantly higher uncertainty (up to 0.03333 mm) and propagated errors exceeding 0.035 mm, particularly in harder materials and unsupported zones affected by surface degradation and fixture variability. Root-sum-square (RSS) modeling confirmed that manual measurements are more prone to operator-induced error amplification. While the DVC sometimes recorded lower absolute errors, its substantial uncertainty margins hampered measurement reliability. To statistically validate these findings, a two-way ANOVA was performed, confirming that both the measurement system and machining zone significantly impacted uncertainty, as well as their interaction. These results emphasize the importance of material-informed and zone-sensitive metrology, highlighting the advantages of automated systems in sustaining measurement repeatability and dimensional stability in high-precision applications. 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

In recent manufacturing environments, the use of digitally retrofitted equipment has grown substantially, yet this trend also amplifies the challenge of ensuring stable operation through effective anomaly detection. Retrofitted systems suffer from two critical obstacles: a severe scarcity of labeled data and substantial variability in operational patterns across machines and products. To overcome these issues, this study introduces a novel anomaly detection framework that integrates Model-Agnostic Meta-Learning (MAML) with a Long Short-Term Memory Autoencoder (LSTM-Autoencoder) under a multi-machine-based task formulation. By constructing meta-tasks from time-series datasets collected on multiple five-axis computer numerical control (CNC) machines, our method enables rapid adaptation to unseen machines and production scenarios with only a few training examples. The experimental results demonstrate that, even under data-scarce conditions, the proposed model achieves an accuracy of 98.02% and an F1-score of 94.74%, representing improvements of 4.2 percentage points in accuracy and 16.9 percentage points in F1-score over conventional transfer learning approaches. Furthermore, in cross-validation on entirely new machine data, our framework outperforms existing models by 18.1% in accuracy, evidencing superior generalization capability. These findings suggest that the proposed multi-machine-based Model-Agnostic Meta-Learning Long Short-Term Memory Autoencoder (MAML LSTM-Autoencoder) can significantly enhance operational efficiency and reduce maintenance costs in retrofitted manufacturing equipment, thereby improving overall productivity and paving the way for real-time industrial deployment. 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