Search Results (590)

The lubrication state of the feed system of a CNC machine tool will affect its positioning accuracy, repetition accuracy, and minimum movement amount. Insufficient or excessive lubrication will affect the accuracy. The primary objective of this study is to resolve issues related to the lubrication condition of the feed system, aiming to enhance its operational stability and accuracy. In this study, a measurement system based on images of Newton’s rings was developed. The relationship between the pattern of Newton’s rings and the oil film thickness was established based on the theoretical principle of Newton’s rings. Furthermore, fuzzy logic theory was applied to predict the oil film thickness. In the oil film thickness prediction model based on the radius of Newton’s rings, the average error is 6.5%. When the average feed rate increases by 2 m/min, the oil film thickness value decreases by 43%. Finally, the prediction model is compared with the results of an actual verification experiment. The trends in oil supply timing are consistent between the predicted and experimental results, and the relative error values are less than 10%. Therefore, this study solves the problem of insufficient or excessive oil supply in the feed system guideway, increasing the accuracy of CNC machine tools and contributing to green energy technology. Full article

This article presents the wear characteristics of the working surface of WC-Co (Tungsten Carbide–Cobalt) tungsten carbide tools obtained using the innovative U-FAST (Upgraded Field-Assisted Sintering Technology) method for particleboard machining. Three groups of tools with a similar chemical composition but differing WC (Tungsten Carbide) grain sizes were tested. Milling tests were carried out on a CNC (Computer Numerical Control) machine tool with the following cutting parameters: spindle rotation at 15,000 rpm, a feed rate of 0.25 mm per tooth, and a feed rate of 3.75. The experimental results show that tools with submicron WC grit sizes of 0.4 µm and 0.8 µm have the longest tool life. Wear of the cutting edges occurred through the removal of the cobalt bond between the tungsten carbide grains, leading to fracture and mechanical removal of the grains from the cutting edge surface. The similarities in the relative wear characteristics of blades with submicron tungsten carbide grain sizes suggest that micro-abrasion and bond phase extrusion may be the main wear mechanisms under the experimental conditions. Nanometric WC grain size significantly influences tool wear through chipping and cracking. Full article

To further improve machining accuracy under the constrained conditions of multi-axis dynamic response, current research is focusing on the control of CNC machine toolpaths, with contour error as the target. While extant approaches analyze positions solely at PLC sampling nodes, they neglect inter-sample toolpath fluctuations induced by velocity deviations. This paper proposes a servo state-based polynomial interpolation model predictive control that predicts real-time toolpath behavior by utilizing servo axis states. The polynomial interpolation of servo states (e.g., position/velocity feedback) enables high-fidelity toolpath prediction between PLC nodes, overcoming the limitation imposed by the sampling gap. Experimental validation on a five-axis motion platform with S-shaped trajectories demonstrates that, without extending the prediction horizon of the model predictive control method, the proposed method reduces contour error by approximately 20% at the tool tip and 40% in tool orientation, while decreasing contour error fluctuations by around 60% compared to conventional model predictive control method. Full article

This work presents the design and implementation of an automated, digital, and modular system to address a real-world industrial challenge: the automation and optimization of production schedules for Computer Numerical Control (CNC) machines in a factory in Portugal. The goal is to replicate and enhance the existing manual scheduling process by integrating multiple data sources and formulating a general Mixed-Integer Linear Programming (MILP) model with constraints. This model can be solved using MILP optimization methods to produce efficient scheduling solutions that minimize machine downtime, reduce tool change frequency, and lower operator workload. The proposed system is implemented using open-source Python abstraction interfaces (Python-MIP), employing state-of-the-art of MILP optimization solvers such as CBC and HiGHS for solution validation. The system is designed to accommodate a wide range of constraints and operational factors, which can be switched on or off as needed, thereby enhancing its flexibility and decision-support capabilities. Additionally, a user-friendly graphical application is developed to facilitate the input of specific scheduling data and constraints, enabling flexible and efficient formulation of diverse scheduling scenarios. The proposed system is validated through multiple case studies, demonstrating its effectiveness in optimizing industrial CNC scheduling tasks and providing a scalable, practical tool for real-world factory operations. Full article

With the development of modern manufacturing industry, complex surface parts are more and more widely used in aerospace, automobile manufacturing, the shipbuilding industry, and many other fields; furthermore, their machining demand is growing explosively, and CNC machining technology has become the mainstream machining method of complex surface parts because of its high precision and high efficiency. However, CNC machining of complex surfaces faces many challenges, especially the generation and optimization of tool trajectories. Therefore, vector field-based tool path planning methods have emerged, aiming to improve the efficiency and accuracy of CNC machining of complex surfaces. This paper focuses on the tool trajectory optimization problem in CNC machining of complex surfaces and reviews the current research status of vector field-based tool path planning for surface machining. The study explores the concept of symmetry in the design of tool paths, highlighting the importance of symmetrical vector fields in achieving efficient and high-precision machining. By analyzing the symmetrical properties of complex surfaces and the corresponding vector fields, this paper discusses the current status, difficulties, and core problems of relevant methods, pointing out the direction of breakthroughs and the future development trend. The findings provide a reference and basis for the realization of efficient and high-precision CNC machining of complex surfaces. Full article

Total ankle arthroplasty (TAA) has evolved significantly through advances in alloy selection and manufacturing technologies. This narrative review examines the metallurgical foundations of contemporary TAA implants, analyzing primary alloy systems and their mechanical properties. Cobalt-chromium alloys provide superior mechanical strength and durability but present metal ion release concerns, while titanium alloys (Ti6Al4V) optimize biocompatibility with elastic modulus values (101–113 GPa) closer to bone, despite tribological limitations. Novel β-titanium formulations (Ti-35Nb-7Zr-5Ta, Ti10Mo6Zr4Sn3Nb) eliminate toxic aluminum and vanadium components while achieving lower elastic modulus values (50–85 GPa) that better match cortical bone properties. Manufacturing has transitioned from traditional methods (investment casting, forging, CNC machining) toward additive manufacturing technologies. Selective laser melting and electron beam melting enable patient-specific geometries, controlled porosity, and optimized microstructures, though challenges remain with residual stresses, surface finish requirements, and post-processing needs. Emerging biodegradable materials, composite structures, and hybrid implant designs represent promising future directions for addressing current material limitations. This review provides evidence-based insights for alloy selection and manufacturing approaches, emphasizing the critical role of materials engineering in TAA implant performance and clinical outcomes. Full article

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

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