Advances in Manufacturing最新文献

Precisely discerning the material separation criterion in micro-machining remains challenging yet crucial for accurately predicting cutting forces by accounting for shearing and ploughing effects. This study introduces a novel model, the instantaneous uncut chip thickness (IUCT), to enhance the accuracy of cutting force prediction in micro-milling processes. The model quantitatively integrates instantaneous shearing thickness (IST) and instantaneous ploughing thickness (IPT). The critical determinants of shearing and ploughing effects rely on the material separation point, modeled using the dead metal zone concept, which considers chip fracture caused by incomplete material accumulation. The micro-milling process is categorized into four types based on the proportion of IST and IPT within one revolution. Mechanistic cutting-force models are developed for each type and validated through experiments. The experimental results align closely with theoretical predictions, with peak force errors remaining within 10%, affirming the accuracy of the analytical force models.

Densification and plateau behavior of lattices can be manipulated by selectively grading the cells. Metallic lattices are the conventional choice for energy absorption, while the generated impact has not been the subject of interest. However, this is the crucial requirement for protective applications like mine-blast absorber for armor vehicles. Different gradient approaches have been examined in this study to find the method which not only controls the absorbed energy, but also keeps the impact level below the identified threshold. This includes available density gradients as well as an innovative gradient geometry for the structure. The concept of how each gradient approach influences the plateau behavior was discussed. A novel approach has been presented which enables tracking the impact magnitude during densification. Although, series density-gradient is a common approach to improve energy absorption in industry, the result of this study demonstrates that crushing the denser region of lattice may generate significantly larger impact. Instead, arranging density gradient cells parallelly can absorb higher energy, while the increase in impact is not significant. An innovative design is presented for lattice structure with gradient geometry. It starts absorbing energy at very low impact and ends with significantly higher absorbed energy at full compaction. To expand the domain of application and effectiveness, new gradient approach was proposed by combining geometry and density grading. It was demonstrated that this highly efficient and flexible design configuration could reduce the activation impact by 94% with descending arrangement and double the absorbed energy by ascending arrangement. This was achieved while the impact magnitude was kept at a reasonable level. In addition, design parameters can be adjusted for desired level of energy and impact for particular application.

Computer-aided engineering (CAE) is widely used in the industry as an approximate numerical analysis method for solving complex engineering and product structural mechanical performance problems. However, with the increasing complexity of structural and performance requirements, the traditional research paradigm based on experimental observations, theoretical modeling, and numerical simulations faces new scientific problems and technical challenges in analysis, design, and manufacturing. Notably, the development of CAE applications in future engineering is constrained to some extent by insufficient experimental observations, lack of theoretical modeling, limited numerical analysis, and difficulties in result validation. By replacing traditional mathematical mechanics models with data-driven models, artificial intelligence (AI) methods directly use high-dimensional, high-throughput data to establish complex relationships between variables and capture laws that are difficult to discover using traditional mechanics research methods, offering significant advantages in the analysis, prediction, and optimization of complex systems. Empowering CAE with AI to find new solutions to the difficulties encountered by traditional research methods has become a developing trend in numerical simulation research. This study reviews the methods and applications of combining AI with CAE and discusses current research deficiencies as well as future research trends.

Error compensation is an economical and effective technique for achieving high machining accuracy. However, a new phenomenon has been detected in its application: error-compensation excited vibrations and further decreased surface quality in some cases. The mechanism of this phenomenon is important but remains unclear, and its main influencing factor remains an open question. To reveal this mechanism, a stability and surface quality analysis model of the dynamic milling process that considers the influence of error compensation is proposed for the first time. Error compensation can be considered as a quasi-static, periodic forcing term added to the milling system. The quasi-static part changes the cutting width, whereas the periodic forcing part mainly influences the instantaneous undeformed chip thickness, based on which the milling stability and surface location error are derived. Numerical simulations and milling experiments were conducted to validate the proposed model. The experimental results show that error compensation has little influence on milling stability but may decrease the surface quality when the compensation values between compensation cycles change significantly. The proposed method shows great potential for estimating and optimizing error compensation paths and improving the quality of machined surfaces.

This investigation examines the impact of diverse interatomic potentials on the molecular dynamics simulation results of deformation and microstructural evolution during nanomachining. The results revealed that the application of the Stillinger-Weber (SW) potential led to the occurrence of significant stacking faults and dislocations. Conversely, the Tersoff potential prevented the initiation of dislocations during the loading segment. The Tersoff potential adept representation of the high-pressure phase transformation of monocrystalline silicon throughout the nanoindentation more accurately predicted mechanical parameters when compared with experimental data. Analytical bond-order potential (ABOP) accurately delineated the deformation mechanisms, including dislocation nucleation and amorphization, during nanoscratching. In contrast, the SW potential tended to underestimate the generation of high-pressure phases, with dislocation nucleation predicted by the SW potential dominating the plastic deformation of monocrystalline Si, contradicting the experimental observations. Consequently, this study concludes that the Tersoff potential and ABOP are the preferred choices for investigating the behavior of monocrystalline Si under nanomachining conditions.

The extraction of mechanical properties plays a crucial role in understanding material behavior and predicting performance in various applications. However, the traditional methods for determining these properties often involve complex and time-consuming tests, which may not be practical in certain situations. To address this challenge, we developed a novel machine learning methodology that leveraged multi-fidelity datasets obtained from small punch test (SPT) experiments. SPT is a simple technique in which a localized load is applied to a small specimen, and the resulting deformation is measured. By analyzing the load-displacement data obtained from the SPT, valuable insights into the mechanical properties of the material can be obtained. In this study, we developed a multi-fidelity model capable of predicting the mechanical properties of steel and aluminum alloys. The proposed model considers variations in the material thickness and can effectively predict the mechanical properties of materials with different thicknesses, accommodating practical scenarios in which material samples exhibit varying thicknesses owing to different applications or manufacturing processes. In constructing our model, we synergistically incorporated low-fidelity finite element method (FEM) data and high-fidelity experimental data to predict the material properties. This integration enabled us to optimize and bolster the accuracy of our predictions, thereby facilitating a comprehensive and dependable characterization of the mechanical behavior of the material. By leveraging the advantages of SPT and incorporating multi-fidelity modeling techniques, our approach offers a practical and efficient solution for extracting mechanical properties. The ability to predict the properties of steel and aluminum alloys and materials with varying thicknesses enhances the versatility and applicability of our model in real-world scenarios.

Multiple-surface interferometry with nanoscale accuracy is important in the precise manufacturing of optically transparent parallel plates. To measure the surface profile and thickness variation of the plates simultaneously, the frequencies of the interferometric signal must be estimated from overlaid interferograms. Traditional algorithms typically suffer from issues such as spectrum leakage, reliance on initial iterative values, and the need for prior knowledge. In this study, the time-domain estimation algorithm for multiple-surface interferometry (MSI-TDe) is introduced based on a difference model to improve the accuracy of frequency estimation. The MSI-TDe algorithm is based on a normal equation that is insensitive to environmental noise. Using the algorithm, the frequencies of an interferometric signal can be estimated without prior knowledge and employed for wavefront reconstruction in multi-surface interferometry. Numerical simulation results indicate that the MSI-TDe algorithm has better frequency estimation performance than the discrete Fourier transform (DFT) algorithm. The relative error of the frequency estimation is on the order of 10^–4. Three-surface interferometry was first performed. The root-mean square repeatability standard deviations of 0.07, 0.12 and 0.11 nm for the thickness variation, front surface profile, and rear surface profile, respectively, indicate the stability of the MSI-TDe algorithm. Four-surface interferometry with six frequency components was then performed. The adaptability of the MSI-TDe algorithm is validated by the measurement results.

Suppressed low-temperature toughness mismatch between the fusion zone (FZ) and base metal (BM) was achieved in a Q450NQR1 high-strength weathering steel joint by employing laser-arc hybrid welding (LAHW) with beam oscillation (O-LAHW), thereby avoiding the heat aggregation of conventional LAHW at the center of the molten pool. The O-LAHWed joint exhibited a higher content of acicular ferrite in the FZ, increasing it by 8% compared with the LAHWed joint, reaching the maximum value of 61%. Meanwhile, the O-LAHWed joint demonstrates higher ultimate tensile strength (775 MPa), yield strength (697 MPa), and impact absorption energy (175 J for FZ, at − 40 °C) compared to LAHWed joints, with increases of 3%, 9%, and 35%, respectively. That is, O-LAHW can significantly improve the impact toughness at low temperatures and exhibit a low-temperature toughness matching degree of 118% with BM, surpassing the metal active-gas arc-welded joints reported in the existing literature by more than one time. The key factor contributing to the improved low-temperature toughness of the FZ was the interlocked microstructure with a high dislocation density promoted by the beam stirring effect.

This paper presents an accurate and efficient method for computing machined part geometry and determining cutter-workpiece engagement (CWE) in multi-axis milling. The proposed method is based on volumetric models, with three types of three-level data structures proposed to represent a solid workpiece voxel model for a sparse and memory-efficient implementation. At each cutter location, every coarse workpiece voxel is efficiently updated from the top to the lower level, and the vertex states and edge intersection points inside each bottom-level voxel crossed by the cutter envelope surface continue to be updated using the dynamic marching cube algorithm. Meanwhile, the finest intersecting voxels are projected onto the cutter surface such that the projected engagement patches connect to form the required engagement map. Finally, according to the lookup table, a triangular mesh of the machined part is built by reconstructing and fusing the approximation polygons inside the bottom-level workpiece surface voxels. Quantitative comparisons of the proposed method against the two-level grid and the tri-dexel model demonstrated the high accuracy and considerable ability of the proposed method to provide more significant and stable efficiency improvement without being affected by a large branching factor owing to its more efficient spatial partitioning.