Geometric error correction in micro-probe fiber interferometry based on mode field matching for nanometer positioning

The evaluation and control of energy efficiency in machine tools are crucial to achieving energy conservation and emission reduction in manufacturing. However, existing energy efficiency models primarily focus either on the energy efficiency of material removal or on the inherent energy efficiency of machine tools themselves, without considering the impact of speed loss caused by low-load operation on the overall energy efficiency of the machining process. To address this gap, this paper establishes a novel load-energy efficiency model for machining systems to specifically assess the impact of speed loss on machining energy efficiency. Furthermore, a statistical process control method for load-energy efficiency is proposed to monitor energy use. Finally, a case study of load-energy efficiency modelling and control for the CK6153i lathe is conducted, demonstrating the effectiveness of the proposed method through a reduction in energy loss of 417.54 kJ and an increase in load-energy efficiency by 4.59%. The proposed method reveals previously untapped opportunities for energy saving in machining processes. The study provides a more nuanced understanding of the relationship between speed loss and machining energy efficiency, thereby offering practical insights for machining energy optimisation.

Abstract Face gears are increasingly used in compact precision assemblies, yet systematic knowledge about how manufacturing errors propagate into functional deviations remains limited. In particular, the kinematics of the hobbing process for face gears differs fundamentally from established processes for cylindrical gears, resulting in complex sensitivity patterns with respect to tool positioning, machine errors, and process parameter variations. This paper presents a kinematics-based simulation framework to analyze the influence of characteristic error sources in the hobbing process on the resulting crown-gear geometry. The approach models the full engagement between the virtual hob and workpiece and allows the systematic introduction of geometric and kinematic deviations, such as tool runout, axial and radial misalignments, pitch errors, and spindle-related perturbations. The resulting gear topographies are evaluated with geometry-based metrics relevant for micro-scale applications to characterize their impact on flank shape, symmetry, and tooth-space formation. The results provide qualitative insights into which error mechanisms most strongly affect the geometric features of face gears and under which conditions these sensitivities become critical.

The friction and its subsequent material loss are inevitable in every sector. The periodic maintenance and breakdown of the machine tools pose a critical challenge to the product and maintenance costs. Hence, the surface modification through a tailored surface engineering approach is a commonly used practice in industries. The present study focuses on the deposition of iron aluminide (Fe3Al) and graphene composite coating, varying at three different levels on industrial-grade SS316 commonly used for pump components like pump shafts, sleeves and impellers. The coating was completed using the laser cladding technique. The microstructural analysis reveals the existence of an equiaxed cellular microstructure along with the evolution of the secondary phase cementite (Fe3C) for a graphene-rich coating variant. Further, the presence of secondary phases has been validated through XRD. The sliding wear test conducted at both low and high temperature environments explores the higher anti-wear performance of the graphene-rich coating through surface engineering. Further, the formation of a protective alumina layer in the high-temperature wear study facilitates recording a lower wear loss of 39% at 50 N normal load than at room temperature. In contrast, increased normal load causes more wear loss by damaging the protective layer, thereby promoting metal-to-metal contact. Worn-out analysis proclaims that adhesion, transformed abrasion, and pure abrasion are the dominant wear mechanisms for low- and high-temperature wear.

The quality of the machined surface and the dynamic stability of the cutting process are directly correlated to the dynamic cutting force, which itself depends on the geometry of engagement between the cutting tool and workpiece that defines the dynamic chip thickness. The surface topography is one of the most important figures of merit for the evaluation of performance in machining processes, which is influenced by the geometry of the cutting edge, the kinematics of the cutting operation, the flexibility of the machine tool structure, and the resulting structural vibrations during the chip formation process. In order to define the instantaneous engagement between the cutting tool and workpiece precisely, all these factors should be taken into account. In this paper, the mechanics, dynamics, and geometry of boring operations are considered for the development of a virtual simulation model by using the solid modeling techniques. The dynamic parameters of the boring bar are defined by modal analysis experiments, and the cutting force coefficients are experimentally identified by conducting mechanistic cutting tests. The experimental cutting tests are conducted in both absolutely stable and unstable cutting conditions. In order to validate the developed model, firstly, the simulated cutting forces are compared with the corresponding experimental results. Secondly, the simulated topography of the machined surface in both stable and unstable cutting conditions is compared with the SEM images from real cut surfaces. The presented geometric simulation model shows a remarkable potential for exact simulation of boring operations in stable and unstable conditions.

ABSTRACT Through microstructural observation, phase analysis, and mechanical testing, the effects of four induction heat treatment configurations—single-inductor quenching (HT1), dual-inductor quenching (HT2), single-inductor normalizing plus quenching (HT3), and dual-inductor normalizing plus quenching (HT4)—followed by uniform tempering on HT300 gray cast iron (HT300-GCI) guideways were investigated. All tempered hardened zones consisted of twinned martensite and carbides, though microstructural evolution varied. HT1 retained the highest content of retained austenite, followed by HT2. HT3 promoted coarse martensite, while HT4 exhibited the lowest contents of both carbides and retained austenite. Consequently, HT4 achieved the maximum hardened case depth (5.75 mm) and highest peak hardness (887.3 HV0.2). However, HT1 suffered the most severe wear due to higher retained austenite, undissolved carbides, and lower overall hardness. Conversely, HT2 demonstrated optimal wear resistance, attributed to a unique synergistic toughening effect from a relatively high matrix hardness combined with F-type graphite.

A novel accuracy allocation method for machine tools: From geometric errors to manufacturing tolerances

Advances in material extrusion-based additive manufacturing of 316 L composite abrasive tools for surface machining

Developing advanced hard coatings is crucial for improving machining performance. This study evaluates a newly created (Ti,Al,Ta,Ce)N coating realized by physical vapor deposition. Coated cemented carbide inserts were evaluated in dry longitudinal turning on C45E, benchmarked against TiN and (Ti,Al) at two cutting speeds (90 and 300 m·min −1 ) and two feeds (0.1 and 0.2 mm·rev −1 ). Tool wear, cutting forces, and rake face temperatures were monitored. At the cutting parameters of 300 m·min −1 and 0.2 mm·rev −1 , the (Ti,Al,Ta,Ce)N coating outperformed (Ti,Al)N at a tool life of 750 m, reducing tool wear by 73 % and cutting forces by 11 %. • Comparison of three coating materials: TiN, (Ti,Al)N, (Ti,Al,Ta,Ce)N. • Dry longitudinal turning test with two different cutting speeds and feed rates. • Criteria: flank-wear land width, cutting force, rake face temperature. • (Ti,Al,Ta,Ce)N coatings proved to be superior to (Ti,Al)N and TiN coatings.

Initial-solution-guided topology optimization of high-performance cooling structures for thermal-error control in precision coordinate boring machine tools

As core equipment in high-end manufacturing, computer numerical control machine tools depend critically on the health of their feed systems, which directly affects machining quality and efficiency. To address fault diagnosis challenges under variable-speed and strong-noise conditions, this paper proposes a deep learning model named DARTS-CNN-BiLSTM. The key novelty lies in the first systematic integration of differentiable architecture search (DARTS) with a hybrid CNN-BiLSTM framework. DARTS automatically optimizes the convolutional neural network structure for spatial feature extraction, while the bidirectional long short-term memory (BiLSTM) captures bidirectional temporal dependencies. Global average pooling is used for feature reduction, and a softmax classifier enables end-to-end fault classification. This automated design eliminates the need for manual network tuning and feature engineering. Experimental results on two public datasets and a self-built dataset demonstrate that the proposed method outperforms advanced models such as Inception-BiLSTM and DenseNet. Specifically, our method maintains over 90% diagnostic accuracy under strong noise (signal-to-noise ratio -6dB) and achieves 98.15% average accuracy on a variable-speed dataset. Ablation studies confirm the advantage of automated architecture design over manually tuned counterparts. These results validate the effectiveness and superiority of the proposed method for complex feed system fault diagnosis.

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy.

The article addresses the problem of finishing complex-shaped parts with high surface quality requirements (Ra < 0.05 μm), residual stress level, and surface hardness. Limitations of conventional methods (grinding, manual polishing, electrochemical machining) are analyzed in such industries as mold manufacturing, aircraft engine building, medical implantology, and ultra-clean technologies. It is shown that manual polishing remains dominant but is characterized by low productivity and high labor intensity. The feasibility of using magnetic abrasive machining (MAM) with a tool based on high-energy permanent magnets (Nd-Fe-B) integrated into CNC machine tools is substantiated. Two design schemes are analyzed in detail: end-type heads for flat surfaces and peripheral heads for internal cylindrical and curved surfaces. Experimental results are presented on the influence of the working surface shape of the heads, working gap size, dispersion and shape of magnetic abrasive powders (Feromap, DC, diamond pastes) on the achieved roughness, microhardness, and residual stresses. It has been established that rational parameters (gap ≥ 1.5 mm, rotation speed 510–1400 rpm, mixtures of crushed and rounded powders) allow stable achievement of roughness Ra < 0.05 μm, an increase in surface hardness by a factor of 1.4, and the formation of compressive residual stresses up to –100 MPa. Special features of MAM of internal pipe surfaces of various diameters are considered, in particular using rod-type heads with ring magnets. It is concluded that the use of mobile magnetic abrasive heads on CNC machine tools is a promising direction for automating finishing operations, allowing controlled formation of microgeometry and physical-mechanical properties of the surface layer without manual labor.

ABSTRACT For CNC machine tools of the same type, to eliminate the interference of accidental individual product factors on reliability evaluation results, a reliability modeling method for unequal time-censored multi-sample CNC machine tools based on the AMSAA model is proposed, which integrates the total fault time method (TFTM) and the maximum likelihood method (MLM). Sample data classification is conducted through trend tests and homogeneity tests. Based on the classification results, the TFTM is applied to preprocess the fault data of homogeneous machine tools, while the MLM is used to estimate the parameters of the AMSAA model. The Cramér-von Mises test is adopted to verify the goodness of fit of the model. The Mean Absolute Percentage Error (MAPE) between the point estimates of the instantaneous MTBF and the observed MTBF values is used as an indicator to evaluate the accuracy of the reliability model. An empirical study is carried out using relevant literature data. The results indicate that the proposed reliability modeling method and parameter estimation method yields a lower MAPE than the direct MLM method, and it can estimate the parameters of the unequal time-censored multi-sample AMSAA model more accurately.

ABSTRACT In this study, a pre-election-ranking procedure is developed using the AHP and TOPSIS methods, and the obtained results are analyzed. Secondly, a procedure using combined machine element characteristics in the AHP-TOPSIS methods and comparing these two different perspectives is analyzed for real cases. A final selection model is offered using a trapezoidal fuzzy number integrated TOPSIS-Machining characteristics evaluation combined model to better understand which CNC machine tool is suitable for the manufacturing company, considering its production system characteristics based on the processed materials, production system type, and appropriateness. One of the most important contributions of this paper is that it uses material processing capabilities for the criteria weighting procedure supported by a fuzzy logic perspective, except for the straightforward multi-criteria decision-making (MCDM) models or their fuzzy extensions to rank machining centers based on the catalogue values and expert opinion reflected in the criteria weighting approaches.

Reliability analysis of table rotary-axis positioning accuracy in CNC machine tools

Computer Numerical Control (CNC) inserts are critical components of CNC machine tools, where surface defects can severely compromise machining precision. Traditional manual inspection methods for these defects are inefficient and prone to significant oversight. To address these limitations, this paper presents an automated real-time system for detecting surface defects on inserts. A dedicated dataset of CNC tool inserts was created and annotated with defect categories. We propose an Attention-Augmented Multi-Defect YOLO model (A2MD-YOLO) for surface defect detection on CNC inserts. The development of this model is motivated by key characteristics of the dataset, which include substantial variation in defect sizes, high intra-class appearance variance, and low inter-class variance. A2MD-YOLO achieves higher detection efficiency and accuracy while reducing the rate of missed detections. The A2MD-YOLO model demonstrates a substantial performance improvement, with the [Formula: see text] increasing from 0.529 to 0.571 and the missed detection rate decreasing from 21.4% to 11.8%. Finally, the proposed algorithm was implemented into the hardware system, enabling automated detection of surface defects on CNC inserts.

Accuracy stability comprehensive evaluation of meta-action unit in CNC machine tools

Large overhang milling cutters face challenges, including poor cutting stability and surface quality when machining deep-cavity parts in aerospace and other industries. The combined interactions between overhang and process parameters significantly influence machining performance and the tool wear mechanism. In this study, the coupled effects of tool overhang length and feed per tooth on milling force, surface topography, chip morphology, and tool wear mechanism were systematically investigated under typical large overhang conditions. The tool stiffness decreased with increasing overhangs; the feed force decreased by approximately 32.4%~49.48%; and the chip morphology changed from continuous bands to fractures. The feed force increased by approximately 25.11%~67.34% with increasing the feed per tooth, resulting in reduced surface quality and accelerated tool wear. The novelty of this work lies in quantitatively revealing the coupling mechanism between overhang length and feed rate in large overhang milling, providing a theoretical basis for process optimization. The findings are directly applicable to the optimization of machining parameters for deep-cavity components such as aero-engine casings and optical mold cavities, where tool overhang is a critical factor affecting productivity and surface integrity. This study provides a theoretical foundation and experimental reference for optimizing process parameters when milling titanium alloy with long-overhang milling cutters.

Thermal errors in high-speed electric spindles represent a core bottleneck constraining the machining accuracy of high-end machine tools. Addressing this challenge, this paper proposes thermal error suppression solutions through two independent approaches: “active enhanced heat exchange” and “passive structural constraints.” First, an experimental and numerical model for the A02 electric spindle thermal error was established, validating the reliability and robustness of the simulation model. Regarding cooling system optimization, protrusions were arranged within the flow channels to enhance convective heat transfer efficiency. Results indicate that the thermal economy evaluation metric achieves optimal performance at a blockage ratio of 0.059, reducing the maximum spindle temperature by 5.8% while maintaining pump power. For structural material optimization, carbon fiber-epoxy composite materials were incorporated into the spindle to suppress thermal displacement. A circumferentially uniform arrangement with a volume fraction of 24.7% reduces total spindle displacement by 28.6%. This study contributes by quantifying the improvement effects of microstructure-induced turbulence and composite anisotropy constraints on thermal errors, overcoming the limitations of single-parameter optimization. The established design criteria—such as the optimal blockage ratio—can directly serve structural optimization for high-precision machine tools, providing flexible “dual-path” theoretical support and technical pathways for thermal error management across diverse engineering scenarios.