International Journal of Advanced Manufacturing Technology最新文献

Accurate tracking of modal characteristics is a valuable diagnostic tool for condition monitoring of machine tool spindle units. While experimental modal analysis (EMA) is the conventional method used for machine tool modal identification, it is often impractical to implement in production settings due to the invasive and manual nature of the impact hammer test. In this study, a new technique for operational modal analysis (OMA) based on output-only vibration measurements obtained during a milling operation with variable spindle speed is proposed. Modal identification is performed using two OMA standard methods, namely stochastic subspace identification (SSI) and frequency domain decomposition (FDD). The modal characteristics are compared to values obtained from conventional EMA from impulse hammer testing on the static spindle, and from the operational spindle during cutting using force measurements collected by a table dynamometer. The percentage difference between the natural frequencies identified by the proposed OMA method and frequencies identified by conventional impulse hammer testing was less than 10%, and for the operational spindle during cutting tests, the difference was less than 3%. These results demonstrate the validity of a new modal identification method that can be practically implemented in production.

We present a scalable and environmentally friendly method for fabricating mechanically robust superamphiphobic coatings on aluminum alloy and zinc substrates using a dual-step process combining sandblasting (SB) and steam treatment (ST), followed by surface energy reduction with fluorinated molecules. This approach creates hierarchical micro/nano structures essential for omniphobic performance. On Al-alloy SB + ST surfaces we measured static contact angles of 162.0° (water), 156.1° (ethylene glycol), and 154.4° (peanut oil), while the corresponding Zn surfaces reached 160.1°, 156.0°, and 152.8°, respectively, with sliding angles below 5° across all tested liquids. The coatings retained high repellency after 50 tape-peeling cycles and 100 cm of sandpaper abrasion under a 500 g load (e.g., ethylene glycol > 140° and peanut oil ≈ 120°). They also showed resistance to water jet impact, excellent self-cleaning, and anti-fogging performance. Compared to conventional hot water treatment or chemical etching, this ST-based method enables faster, cleaner fabrication and significantly enhances mechanical durability making it a promising candidate for large-scale applications in anti-fouling, anti-corrosion, and protective surface technologies.

In heavy industries like oil and gas, and shipbuilding, maintaining process quality is challenging. These sectors face inconsistent manual procedures and a shortage of skilled operators regarding thermal cutting and bevelling for welding preparation tasks. Manual fitting and repetitive quality control modifications, especially during thermal cutting, significantly increase time consumption and hinder productivity. Traditional thermal cutting methods are prone to human error, resulting in inconsistent cut quality, and demand high expertise leading to variability in cut precision, increased rework, and material wastage. The objective of this work is to address these challenges by introducing real-time ultrasonic sensing into a robotic plasma cutting control system to automate the steel plate bevelling process. The ultrasonic sensor enables the system to dynamically adapt to variations in steel plate thickness before cutting, ensuring precise and consistent results. The solution begins by presenting an automated method for measuring thickness and computing bevel distance per sample. Secondly, it proposes adaptive adjustments to cutting parameters per sample, leveraging the ultrasonic sensor data to enhance accuracy and reduce the need for manual intervention. Finally, the approach introduces adaptive robotic path generation for cutting and utilizing real-time ultrasonic sensor data to optimize cutting paths. The outcome of this study is the successful development and validation of an adaptive robotic plasma cutting system for steel plate bevel applications, which leverages real-time ultrasonic sensor data to automate the parameter input process and robotic motion planning, demonstrating improved accuracy and efficiency compared to traditional approaches. The results demonstrate that ultrasonic-driven robotic cutting significantly reduces the average error cut percentage to 4.47% with deviations ranging from 0.13 to 0.23° for the bevel angle and 14.27% with deviations between 0.02 and 0.05 mm for root face deviation, compared to the standard cutting approach which has an average error of 18% with deviations ranging from 0.10 to 0.38 mm and 77.1% with deviation between 0.48 to 0.90°, respectively. This paper highlights the benefits of using advanced sensing technology, particularly ultrasonic sensors, to automate plasma bevel cutting for metal plates in the steel fabrication and welding sectors.

Coating sprays play a crucial role in extending the capabilities of optical measuring systems, especially when dealing with reflective surfaces, where excessive reflections, caused by incident light hitting the object surface, lead to increased noise and missing data points in the measurement results. This work focuses on metal additively manufactured parts, and explores how the application of a sublimating matting spray on the measured surfaces can improve measurement performance. The use of sublimating matting sprays is a recent development for achieving temporary coatings that are useful for measurement, but then disappear in the final product. A series of experiments was performed involving measurement by fringe projection on a selected test part pre- and post-application of a sublimating coating layer. A comparison of measurement performance across the experiments was run by computing a selected set of custom-developed point cloud quality indicators: rate of surface coverage, level of sampling density, local point dispersion, variation of selected linear dimensions computed from the point clouds. In addition, measurements were performed using an optical profilometer on the coated and uncoated surfaces to determine both thickness of the coating layer and changes of surface texture (matte effect) due to the presence of the coating layer.

The deep drawing process in the automotive industry generates up to 45% material waste. To address this issue, the folding-shearing process was developed as a drop-in solution, enabling the formation of parts in pure shear with minimal thickness variation. This process involves folding a blank while collecting the excess material in a region called the 'beak', which is subsequently sheared in-plane using a single set of tools moving in one forming direction. This paper investigates the extent to which the curvature of the geometry of the beak influences the resulting thickness distribution. A combination of physical and numerical trials demonstrates that a beak design with a negative Gaussian curvature reduces the maximum thickening by 65%. This reduction in thickening helps minimise the forming loads and tool wear, thereby improving the overall robustness of the process. An analytical model is proposed to predict the resulting thickness distribution and demonstrates accuracy within a 12.5% deviation from experimental results. Finally, a design map is proposed to instantly identify the optimal beak design parameters without the need for extensive numerical or physical validations while ensuring a minimal thickness change.

Electric-field assisted (EA) manufacturing is a promising hybrid manufacturing technique, offering significant advantages over conventional manufacturing methods. Extensive experimental and numerical studies have demonstrated that the application of electric current reduces flow stress in metals and alloys, thereby improving their manufacturability. This enhancement is attributed to the synergistic effects of electroplasticity and Joule heating, both induced by the applied current during processing. Several key manufacturing processes have garnered substantial interest from the research community for their potential enhancement through electric fields. Here, we present a comprehensive review of recent developments in EA manufacturing over the last decade. The findings of various researchers investigating different EA manufacturing processes are discussed, accompanied by detailed tables summarizing the materials and electric current parameters employed in each process.

The growing complexity of manufacturing processes and the increasing diversity of decision-making tools present challenges in selecting effective approaches for process optimisation. Many existing tools are either too narrowly focused or inconsistently applied across sectors, limiting their broader impact. Additionally, the lack of clear integration strategies often hinders their full implementation in industrial settings. This systematic review examines decision-making tools that enable comparative assessments applied at the unit process level in manufacturing, covering both the selection between competing manufacturing routes and the optimisation of specific processes. A total of 37 journal articles were selected through a structured database search and evaluation process. The review analyses commonly used tools such as Multi-Criteria Decision Analysis (MCDA), Life Cycle Assessment (LCA), and Direct Comparison, highlighting their applications, benefits and limitations. Findings show that MCDA offers robust, multi-dimensional evaluations but is often constrained by complexity and data demands. In contrast, simpler methods like Direct Comparison provide more accessible insights but with a limited scope. Advanced tools such as Deep Learning and Computational Simulations hold promise but face challenges in scaling beyond the process level. Notably, there is limited integration of sustainability metrics within process-level decision-making. To address this, the study proposes a structured framework to guide future research and implementation, focusing on data management, AI integration and tool scalability. The results highlight the need for hybrid approaches that combine different tools to balance trade-offs and support long-term sustainability and operational efficiency in manufacturing systems.

Subtractive manufacturing is undergoing a transformative shift towards sustainability and zero-defect manufacturing. This shift is driving the need for more efficient machining strategies such as dynamic milling. The real-time implementation of dynamic milling toolpaths, composed of circular and cycloidal curve patterns, is challenging due to the kinematic constraints in computer numerically controlled machine tools. Resulting from a rigorous analytical analysis of kinematics, the limitations of current approaches to finite impulse response (FIR) interpolation of circular arc (G02/G03) motion are addressed. A novel hybrid FIR interpolation method is presented which modifies the interpolation style depending on the fundamental geometry of commanded circular motion. The method globally satisfies kinematic constraints and tool centre point position tolerances during circular motion and allows consideration of machine dynamics (i.e., resonant frequencies) within the interpolation strategy. The proposed method outperformed current state-of-the-art methods during benchmarking tests which included a high-performance machine tool and two commercial controllers. Reductions of up to 38% in manufacturing cycle times were demonstrated when interpolating high-speed trochoidal toolpaths with the proposed method.

The UK is the twelfth-largest manufacturing nation globally, yet its adoption of digital manufacturing technologies (DMTs) lags behind other European countries. In an era where industrial automation and digital transformation are essential for maintaining competitiveness, understanding the human factors influencing the acceptance and implementation of these technologies is critical. This study examines the perceptions of 313 UK manufacturing employees regarding the usefulness, ease of use, and workplace impact of DMTs. Findings indicate that while employees recognise the potential benefits of DMTs such as increased productivity, improved product quality, and enhanced competitiveness, concerns remain regarding ease of use, workforce upskilling, and physical interaction with new technologies. Notably, employees with lower educational qualifications expressed greater scepticism about the applicability of DMTs. Furthermore, those working in companies that had already implemented digital technologies reported more positive perceptions compared to non-users, emphasising the role of experience in shaping attitudes. The study highlights the need for targeted training and change management strategies to facilitate smoother workforce adaptation to digital advancements. These findings provide insights for policymakers, industry leaders, and system designers aiming to integrate human-centric approaches in the transition to Industry 4.0 and beyond.

Wire arc additive manufacturing (WAAM) is suitable for building large-scale engineering structures with high deposition rates and relatively low costs. However, in a typical plasma transferred arc (PTA)-based WAAM process using an inclined wire and vertical torch, keyhole defects can occur due to the high arc pressure, and the process is sensitive to the wire-feeding position with respect to the workpiece. Therefore, in this study, a PTA-based WAAM process with a new configuration employing a vertical wire and an inclined plasma torch was investigated for the potential of mitigation of keyhole formation and improvement of process tolerance. In particular, detailed investigations were carried out on the metal transfer mechanisms and bead formation characteristics under various processing conditions. The results show that the new configuration significantly reduces the likelihood of keyhole formation compared with the conventional approach due to the changes in arc pressure and heat distribution. Systematic analysis reveals that process parameters, including wire feed speed, arc current, and plasma gas flow rate, strongly influence droplet transfer stability, melt pool dynamics, and final bead morphology, which offer guidance for future process optimisation.