Search Results (86)

Large-scale, high-density flatness measurement is critical for manufacturing reference surfaces in ultra-precision machine tools. Traditional methods exhibit degradation in both accuracy and efficiency as measurement points and area size increase. In order to overcome these limitations to meet the requirements for integrated in-process measurement and machining of structural components in ultra-precision machine tools, this paper proposes a novel nine-probe third-order matrix system that integrates the Fine Sequential Three-Point (FSTRP) method with automated scanning path planning. The system utilizes a multi-probe error separation algorithm based on the FSTRP principle, combined with real-time adaptive sampling, to decouple machine tool motion errors from intrinsic workpiece flatness deviations. This system breaks through traditional multi-probe 1D straightness measurement limitations, enabling direct 2D flatness measurement (with X/Y error decoupling), higher sampling density, and a repeatability standard deviation of 0.32 μm for large precision machine tool components. This high-efficiency, high-precision solution is particularly suitable for automated flatness inspection of large-scale components, providing a reliable metrology solution for integrated measurement-machining of flatness on precision machine tool critical components. Full article

Due to insufficient absolute positioning accuracy, industrial robots frequently face challenges in efficiently performing drilling and riveting operations during the assembly of aircraft and other large-scale workpieces. To enhance the absolute positioning accuracy of industrial robots, this paper proposes a novel kinematic calibration method for industrial robots based on the Improved Grey Wolf Optimization (IGWO) algorithm. Specifically, the method employs an enhanced selection and update strategy to avoid convergence stagnation and local optimum traps. The proposed method features a novel boundary search strategy, which leverages the Dimension-oriented Learning (DL) search strategy to enhance search speed and stability. Through parameter identification and calibration experiments, the effectiveness of the method was validated using an ABB IRB4600 industrial robot and a Leica laser tracker. Additionally, compared with the Levenberg–Marquardt (LM) algorithm, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the IGWO algorithm demonstrates faster convergence and superior optimization performance. According to the calibration experimental results, by applying the IGWO algorithm, the absolute positioning accuracy of the industrial robot is ultimately improved from 1.918 mm to 0.475 mm and the absolute positioning accuracy is improved by 75.2%. Full article

To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts. 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

The edge effect refers to what occurs when an object undergoes elastic contact with the edge of a material. This is common in practical applications, but the understanding of this phenomenon is not yet mature enough, and understanding the microscopic characteristics of the material regarding this phenomenon is necessary. This article investigates the edge effects of single-crystal silicon at different indentation positions through molecular dynamics simulations. The results indicate that the edge effect of the indentation is influenced by the indentation position and depth. The closer the indentation head is to the edge of the workpiece, the more particles are extruded from the side of the workpiece and the wider the collapse range of the indentation surface. At the same time, the indentation position also affects the distribution of the von Mises stress and phase transition area. When the edge effect occurs, the von Mises stress and phase transition region tend to be concentrated near the workpiece edge. This study demonstrates the atomic-scale deformation mechanism of single-crystal silicon under varying indentation positions. Full article

Surface roughness is a critical indicator for assessing the quality and characteristics of workpieces, the accurate prediction of which can significantly enhance production efficiency and product performance. Data-driven methods are efficient ways for predicting surface roughness in polishing processes, which generally depend on large-scale samples for model training. However, obtaining an adequate amount of training data during the polishing process can be challenging due to constraints related to cost and efficiency. To address this issue, a novel surface roughness prediction model, named VSG-FC, is proposed in this paper that integrates Genetic Algorithm-driven Virtual Sample Generation (GA-VSG) and Genetic Programming-driven Feature Construction (GP-FC) to overcome data scarcity. This approach optimizes the feature space through sample augmentation and feature reconstruction, thereby enhancing model performance. The VSG-FC method proposed in this paper has been validated using data from two polishing experiments. The results demonstrate that the method offers significant advantages in both the quality of the generated virtual samples and prediction accuracy. Additionally, the proposed model is explainable and could successfully identify key influencing machining factors. Full article

This study addresses the critical challenge of precise control over active abrasive particles in magnetorheological polishing (MRP) through innovative core–shell particle engineering. A sol–gel synthesized CIP@SiO₂ magnetic composite abrasive with controlled SiO₂ encapsulation (20 nm shell thickness) was developed using tetraethyl orthosilicate (TEOS) as the silicon precursor, demonstrating significant advantages in optical-grade fused silica finishing. Systematic polishing experiments reveal that the core–shell architecture achieves a remarkable 20.16% improvement in surface quality (Ra = 1.03 nm) compared to conventional CIP/SiO₂ mixed abrasives, with notably reduced surface defects despite a modest 8–12% decrease in material removal rate. Through synergistic analysis combining elastic microcontact mechanics modeling and molecular dynamics simulations, we establish that the SiO₂ shell mediates stress distribution at tool–workpiece interfaces, effectively suppressing deep subsurface damage while maintaining nano-scale material removal efficiency. The time-dependent performance analysis further demonstrates that extended polishing durations with CIP@SiO₂ composites progressively eliminate mid-spatial frequency errors without introducing new surface artifacts. These findings provide fundamental insights into designed abrasive architectures for precision finishing applications requiring sub-nanometer surface integrity control. Full article

The removal of rust from large equipment such as trains and ship hulls poses a significant challenge. Traditional methods, such as chemical cleaning, flame rust removal, and laser rust removal, suffer from drawbacks such as high energy consumption, operational complexity, and poor mobility. Sandblasting and high-pressure water jet rust removal face issues such as high consumable costs and environmental pollution. Existing robotic grinding systems often rely on precise measurement of the workpiece surface geometry to perform deburring and polishing tasks; however, they lack the sufficient adaptability and robustness required for rust removal operations. To address these limitations, this study proposes a floating grinding actuator scheme based on compound force-position fuzzy control. By implementing simplified path-point planning, continuous grinding and rust removal can be achieved without requiring the pre-measurement of workpiece geometry data. This solution integrates force and laser displacement sensors to provide real-time compensation for path deviations and ensures adaptability to complex surfaces. A fuzzy derivative-leading PID algorithm was employed to control the grinding force, enabling adaptive force regulation and enhancing the control precision. Rust removal test results demonstrate that under varying advancing speeds, fuzzy derivative-leading PID control can significantly reduce fluctuations in both the grinding force and average error compared to traditional PID control. At a speed of 40 mm/s, excellent control performance was maintained, achieving a rust removal rate of 99.73%. This solution provides an efficient, environmentally friendly, and high-precision automated approach to rust removal using large-scale equipment. Full article

Additive manufacturing (AM) technology has found extensive applications in aerospace, medical, and automotive fields. Defect detection technology remains a research focus in AM process monitoring. While machine learning and neural network algorithms have recently achieved significant advancements in innovative applications for AM defect detection, practical implementations still face challenges, including insufficient detection accuracy and poor system robustness. To address these limitations, this study proposes the YOLOv5-CAD defect detection model. Firstly, the convolutional block attention module (CBAM) is introduced into the core feature extraction module C3 of the backbone network to enhance attention to critical information and improve multi-scale defect target adaptability. Secondly, the original CIoU loss function is replaced with the Alpha-IoU loss function to accelerate network convergence and strengthen system robustness. Additionally, a fully decoupled detection head substitutes the original coupled head in the YOLOv5s model, separating the object classification and bounding box regression tasks to improve detection accuracy. Finally, a polarization technology-based visual monitoring system is developed to acquire defect images of laser AM workpieces, establishing the model’s training sample database. Compared with YOLOv5, the proposed model demonstrates a 2.5% improvement in precision (P), 2.2% enhancement in recall (R), 3.1% increase in mean average precision (mAP50), and 3.2% elevation in mAP50-95. These quantitative improvements confirm the model’s capability to provide robust and real-time technical solutions for industrial AM quality monitoring, effectively addressing current limitations in defect detection accuracy and system reliability. Full article

Rotary friction welding is a solid-state welding process that can manufacture high-integrity joints between similar and dissimilar materials with short weld times. However, access to expensive and complex industrial-grade friction welding machines is not always possible. This study explores the design process and functionality of a laboratory-scale friction welding setup following the fundamentals of large-scale machinery. The proposed setup is designed to be easily manufactured, employing the use of a calibrated drill press and load cell, thus ensuring welding parameters such as rotational speed and applied axial load are monitored. The decision to investigate rotary friction welding of aluminium bronze Ca104 to austenitic stainless steel AISI316 was taken to explore the limitations of this bespoke friction welding machine for prospective applications in the nuclear energy sector. The workpieces were friction welded at four sets of rotational speeds with constant friction and forging pressures. The microstructural evolution and mechanical properties of the dissimilar material welds were investigated via optical and scanning electron microscopy with energy dispersive spectroscopy, 4-point bend testing and microhardness measurements. Results show a change in the hardness along the weld interface and evidence of metallic diffusion between the dissimilar materials, demonstrating the successful application of the small-scale experimental setup. Full article

Mechanical belt sanding is critical in the manufacturing of bamboo and bamboo products, where surface roughness is commonly used to quantitatively evaluate the surface quality. In this study, flattened bamboo workpieces were sanded using P80 and P120 abrasive belts to create different surfaces. The linear roughness parameters, namely Rz, Ra, Rq, Rsk, Rku, and Rmr(c), were measured using both a stylus profilometer and a 3D profilometer. Statistical t-tests were conducted to determine the significance of differences between the two methods. Additionally, roughness profiles were analyzed in the frequency domain using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods. A Random Forest (RF) regression model was also developed to predict the roughness values and figure out the dominant factors between granularity and measurement methods. The results revealed that both the stylus and 3D profilometers provided reliable comparisons of Rz, Ra, Rq, and Rmr (50%) for different grit sizes. However, resolution differences between the two methods were found to be critical for accurately interpreting roughness values. Variations in Rsk and Rku highlighted differences in sensitivity and detection range, particularly at finer scales, between the two methods. The stylus profilometer, with its higher spatial resolution and finer sampling density, demonstrated greater sensitivity to finer surface details. This was consistent with the FFT and PSD analyses, which showed that the stylus profilometer captured higher-frequency surface components more effectively. Furthermore, the RF model indicated that the choice of measurement method had negligible impact on the evaluation of the selected roughness parameters, suggesting that standardizing measurement techniques may not be essential for consistent roughness assessments of sanded bamboo surfaces. Full article

Conventional lubricant testing methods focus on lab-scale constant contact conditions, which cannot represent the scenarios in actual hot-stamping processes. In recent studies, the concept of the ‘digital characteristics (DC)’ of metal forming has been proposed by unveiling the intrinsic nature of the specific forming, which presents a timely solution to address this challenge. In this work, the transient behaviours of three dedicated lubricants during the hot stamping of AA6111 material were investigated considering the effects of various contact conditions using an advanced friction testing system, and the interactive friction modelling was established accordingly. The lubricant limit diagram (LLD) of each lubricant was then generated to quantitatively evaluate the lubricant performance following the complex tool–workpiece interactions based on the tribological DCs, and a detailed investigation on the lubricant failure regions was conducted based on the interactive friction modelling. Finally, the industrial application index (IAI) was proposed and defined as a comprehensive evaluation of lubricant applications in the industry, and the most suitable lubricant was identified among the three candidates for mass production. Full article

Mg-Gd-Y-Zn-Mn (MVWZ842) is a kind of high rare earth magnesium alloy with high strength, high toughness and multi-scale strengthening mechanisms. After heat treatment, the maximum tensile strength of MVWZ842 alloy is more than 550 MPa, and the elongation is more than 5%. Because of its great mechanical properties, MVWZ842 has broad application potential in aerospace and rail transit. However, the addition of high rare earth elements makes the deformation resistance of MVWZ842 alloy increase to some extent. This leads to the difficulty of direct plastic processing forming and large structural part shaping. Friction stir welding (FSW) is a convenient fast solid-state joining technology. When FSW is used to weld MVWZ842 alloy, small workpieces can be joined into a large one to avoid the problem that large workpieces are difficult to form. In this work, a high-quality joint of MVWZ842 alloy was achieved by FSW. The microstructure and properties of this high-strength magnesium alloy after friction stir welding were studied. There was a prominent onion ring characteristic in the nugget zone. After the base was welded, the stacking fault structure precipitated in the grain. There were a lot of broken long period stacking order (LPSO) phases on the retreating side of the nugget zone, which brought the effect of precipitation strengthening. Nano-α-Mn and the broken second phase dispersed in the matrix in the nugget zone, which made the grains refine. A relatively complete dynamic recrystallization occurred in the nugget zone, and the grains were refined. The welding coefficient of the welded joint exceeded 95%, and the hardness of the weld nugget zone was higher than that of the base. There were a series of strengthening mechanisms in the joint, mainly fine grain strengthening, second phase strengthening and solid solution strengthening. Full article

Laser machining by ultra-short (sub-ps) pulses at high intensity offers high precision, high throughput in terms of area or volume per unit time, and flexibility to adapt processing protocols to different materials on the same workpiece. Here, we consider the challenge of optimization for high throughput: how to use the maximum available laser power and larger focal spots for larger ablation volumes by implementing a fast scan. This implies the use of high-intensity pulses approaching ∼PW/cm² at the threshold where tunneling ionization starts to contribute to overall ionization. A custom laser micromachining setup was developed and built to enable high speed, large-area processing, and easy system reconfiguration for different tasks. The main components include the laser, stages, scanners, control system, and software. Machining of metals such as Cu, Al, or stainless steel and fused silica surfaces at high fluence and high exposure doses at high scan speeds up to 3 m/s were tested for the fluence scaling of ablation volume, which was found to be linear. The largest material removal rate was 10 mm³/min for Cu and 20 mm³/min for Al at the maximum power 80 W (25 J/cm² per pulse). Modified surfaces are color-classified for their appearance, which is dependent on surface roughness and chemical modification. Such color-coding can be used as a feedback parameter for industrial process control. Full article

GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the workpiece surface can enhance its properties further. Through-mask electrochemical micromachining (TMEMM) is a promising and potential processing method for nickel-based superalloys. It can effectively solve the problem that traditional processing methods are difficult to achieve large-scale, high-precision and efficiency processing of surface micro-structure. This study explores the feasibility of electrochemical machining (ECM) for GH4169 using roll-print mask electrochemical machining with a linear cathode. Electrochemical dissolution characteristics of GH4169 alloy were analyzed in various electrolyte solutions and concentrations. Key parameters including cathode sizes, applied voltage and corrosion time were studied in the roll-print mask electrochemical machining. A qualitative model for micro-pit formation on GH4169 was established. Optimal parameters were determined through experiments: 300 μm mask hole and cathode size, 10 wt% NaNO₃ electrolyte, 12 V voltage, 6 s corrosion time. The results demonstrate that the micro-pits with a diameter of 402.3 μm, depth of 92.8 μm and etch factor (EF) of 1.81 show an excellent profile and localization. Full article