CIRP Journal of Manufacturing Science and Technology最新文献

This article presents a hybrid model to predict the positions of the ball screw drive system of machine tool and then modify the trajectory through constructing a pre-compensation method to reduce servo errors in machine motion axes. To achieve this objective, a flexible control model is initially developed to characterize the ball screw drive system, and by leveraging this model, a high-bandwidth controller is constructed, with its physical representation, i.e. the state-space equation, being derived. Subsequently, a data-driven hybrid model is proposed to predict the positions of the ball screw drive system concerning the next multiple time steps from the current time step, and then the predicted positions associated with these steps are utilized as initial conditions to adjust and compensate for the physical model’s prediction errors corresponding to these multiple time steps. As a result, a compensated trajectory with high tracking accuracy is generated. Finally, experiments confirm that the proposed prediction method offers superior prediction accuracy and enhanced adaptability, and the pre-compensated trajectory leads to reduced tracking errors.

This paper aims to quantitatively analyze the relationship between forces acting on the tool tip and tool movement during drilling operations. The study encompasses axial and lateral vibrations superimposed on the nominal tool movement, arising from rigid body motion (rotational and axial velocities). Specifically, only forces attributed to the cutting process are considered, excluding considerations of indentation forces around the chisel edge. The research adopts a generalized approach, spanning from tool measurements to establishing the force model. The investigation involves measuring cutting forces and correlating them with the varying rake and inclination angles of the drill’s cutting edges. An analytical model is proposed to describe the distribution of all local force components along drill edges, considering the evolution of forces and geometry. The dynamic coefficient matrix is evaluated by using the identified cutting coefficient and tool geometry. Validation of the proposed methodology is demonstrated through drilling experiments on Ti6Al4V alloy, utilizing three solid carbide drills with distinct geometries. The proposed procedure allows complete identification of the dynamic characteristics from the measurements taken at the entrance stage of hole drilling operation. Moreover, the influence of tool geometry on cutting coefficients and dynamic coefficient matrices are discussed.

The Hot Isostatic Pressing (HIP) process is based on the combined action of high levels of pressure and temperature. In general, such a process is used for reducing or eliminating microporosities in the component, especially when it is produced by additive manufacturing (AM) or casting. In the present work HIP is used for compacting TI6Al4V-ELI powders by means of a pressurized Argon gas acting at high temperature on a sealed can under vacuum in which Argon is previously inflated; thus, a subsequent Solid-State Foaming (SSF) heat treatment allows to produce a Titanium foam without any melting by exploiting gas entrapment. Samples extracted from billets produced setting different HIP parameters (size of Titanium particles and pressures) have been investigated in this work by means of heat treatments in furnace for the SSF: the temperature was kept constant (1020 °C), but the duration was varied in the range 1 - 6 h; the samples were finally analysed by Light Microscopy. Finally, the Response Surface Method (RSM) was used to determine the conditions able to increase both the size and the percentage area of the pores in order to fully control both the involved processes (HIP and SSF). Experimental results revealed that the porosity determined by the SSF is strongly affected by HIP parameters and by the SSF duration: the highest dimension of Ti particles and the highest level of Argon pressure determined the largest values of porosity, in terms of both percentage and pore dimension. The investigated process for producing porous titanium structures can be properly and efficiently combined with manufacturing processes able to create highly customised parts, not only in terms of geometry but also in terms of porosity.

A novel friction processing method is used to join Al-Cu sheets in lap configuration without any significant deformation or thickness depletion of parent metals. A pin less, flat shouldered tool is rotated and plunged against a sacrificial top sheet to initiate localised melting at the joint interface. Effect of addition of SiC particles at the joint interface on resulting weld microstructure, interface strength, and joint electrical resistance is studied in this work. Diffusion reaction of SiC particles with Al and Cu results in melting of the interface at around 530 °C. The peak temperature in weld zone with SiC particles is significantly lower than the Al-Cu eutectic temperature and melting points of Al, Cu. Cross-sectional scanning electron micrographs, fractographs, electron dispersive spectroscopy, X-ray diffraction, lap shear, T peel tests, and joint electrical resistance measurements are used to investigate pure Al-Cu and Al-SiC-Cu weld joints for different SiC particle concentrations. SiC particles are found to enhance joint strength through generation of fine eutectic microstructures with sub-micron lamellar spacing and through formation of uniformly distributed nano-precipitates. The highest peel strength achieved with the SiC interlayer is around 70 % higher than that for corresponding pure Al-Cu welds. Despite formation of thick hypereutectic region towards Cu side, with larger volume percentage of “lumps of intermetallics”, there is a clear diversion of fracture path from the intermetallics rich region towards the SiC-eutectic boundary of the interface region in Al-SiC-Cu welds. Additionally, SiC interlayer is seen to result in lower percentage rise in joint resistance with temperature. However, SiC interlayer results in a marginal increase in joint electrical resistance. Thus, addition of SiC particles at Al-Cu joint interface is recommended for significantly enhancing the joint strength with no significant change in joint electrical resistance.

High-entropy alloys (HEAs) are newly developed materials that have many excellent properties, such as a high strength-to-weight ratio and excellent tensile properties. If high-entropy alloys and stainless steel are joined by welding, the advantages of their properties can be balanced. In this paper, dissimilar lap joining of Al_0.3CoCrFeNi-HEA with 304 stainless steel was achieved using gas tungsten arc welding (GTAW) with different heat inputs. Macroscopic morphology, microstructure analysis and mechanical property tests of the welded joints were carried out. The results showed that the macroscopic morphology of the dissimilar welded joints is well-formed under different heat inputs. The penetration and width of the weld seam increased with the heat input, and the lap area of the welded joint also increased. There was the same microstructure in the weld seam with different heat inputs, including columnar dendrites near the fusion line and equiaxed dendrites at the weld centre. The ultimate shear strength of the welded joints increased from 442 MPa to 560 MPa with increasing heat input, and the elongation of the welded joints increased from 26 % to 41 %. With increasing heat input, the average microhardness of the weld zone (WZ) was approximately 145 HV.

The electrically assisted processing has advantages in reducing manufacturing difficulty and improving forming accuracy, and the rational application of electroplastic effect promotes the development of material forming and advanced manufacturing. The effects of electroplasticity, temperature and strain rate on the flow behavior are investigated by electrically-assisted isothermal tensile and furnace isothermal tensile experiments under different stress states. A neural network-based evolving plasticity model is combined with inverse engineering method to characterize coupling effects. The results show that the electric pulse induces Joule heating effect and electroplastic effect to reduce deformation resistance and improve formability. The non-monotonic effect of temperature and strain rate on flow behavior is attributed to dynamic strain aging, and electrical pulses suppress negative strain rate effects. The combination of artificial neural network (ANN) model and traditional constitutive model can accurately capture the mapping relationship of strain, strain rate, temperature and current density to stress. The calibration results by the inverse engineering method are regarded as the input set of the ANN model to achieve the prediction of plastic behavior at large strain. Analytical parameter calculation of the pDrucker function can accurately describe the difference and evolution of the plastic response under different stress states. The simulation of the ANN model based on the DF2014 fracture model accurately reflects the plastic response under different conditions and provides accurate predictions in the forming simulation of the cap beam.

Glass is a widely used material in key fields such as Micro-Electro-Mechanical Systems (MEMS) due to its excellent properties. The existing non-traditional glass machining methods have problems such as high pollution, difficult operation, and poor sustainability, this article utilizes the effective combination of electrochemical discharge machining and grinding (named electrochemical discharge grinding, ECDG), by using NaHCO₃ solution as electrolyte to achieve green machining. Utilizing ultrasonic vibration and multi-hole tube electrode to achieve precise and stable machining. Modeling and simulation analysis were conducted on the material removal rate and grinding force during the machining process, which profoundly revealed the joint improvement mechanism of spark discharge and ultrasonic vibration on grinding quality. First, a single factor experiment was used to preliminarily determine the machining threshold. Second, the Plackett-Burman experiment was used to screen key machining parameters. Then, Box-Behnken experiment was conducted on key machining parameters, and multi-objective and multi-factor optimization was performed to obtain the optimal combination of machining parameters. Compared with normal ECDG with cylindrical grinding electrode, the overcut is reduced by 8.3 %, the edge damage is reduced by 17.5 % and the surface roughness value is reduced by 70.6 %. Finally, by using the optimized combination of machining parameters, high-quality and stable machining of typical microchannel structures was achieved. The milling depth of the microchannel is 400 µm. The machining width is 1175 ± 5 µm. The surface roughness of the measurement area is 0.375 µm. The green, high-quality and stable machining of micro glass micro components further demonstrates the potential application of this compound technology.

Lattice metamaterials, a class of structures utilized for geometric lightweighting and enhancing structural integrity, feature interconnected elements arranged in specific patterns. These patterns confer unique properties beneficial for improving stiffness, damping, energy absorption, and strength across various applications. However, computational simulation of lattice structures for design and optimization presents significant challenges due to nonlinear and elastic/inelastic effects like instabilities, contacts, rate-dependence, and plasticity. Current methods heavily rely on finite element analysis (FEA), yet they entail high computational complexity and do not fully align with experimental observations. Moreover, additive manufacturing (AM) technologies introduce additional computational complexity due to process-induced anisotropic behaviors. This paper proposes a computational model to construct an effective descriptor by integrating metamaterial topological information with AM conditions. This descriptor serves as a surrogate for FEA models. Additionally, a database is established to correlate the descriptor with experimentally acquired mechanical responses of additively manufactured metamaterials. The bidirectional operation of the envisaged descriptor fulfills two objectives: informing the mechanical response of novel metamaterial models and guiding design and AM processes based on desired outcomes. Model validation demonstrates significant concurrence between predicted and experimental results, evidencing the model's capability to capture inherent nonlinearity in both design and process.

In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints.

Irregular (i.e. non-spherical) morphology powder is more cost-efficient to produce than spherical shaped powder. However, its reduced levels of flowability limit the wide application ranges of laser beam powder bed fusion (LPBF). To address this issue, a novel powder sheet additive manufacturing concept (MAPS) is proposed. Herein, a pre-manufactured metal particle-polymer binder composite (i.e. powder sheet) feedstock is employed as a raw material. The printability of irregularly shaped powder particles in sheet (MAPS) format was physically investigated using a range of different process parameters. The manufacturing process was observed by high-speed imaging. Microstructural and chemical element characterisations of the irregularly shaped powder particle print were then compared against those prints which were conducted using sheet-based (MAPS) spherical powder morphologies. The results indicated that the geometric accuracy and density of irregular powder morphology sheet printing improved when using a negative defocus strategy of the laser beam. A negative defocusing strategy allowed for the melting mode of the material to be transformed from keyhole to a more favourable conduction state. High speed imaging revealed that more spatter and vapour plume were observed with the increase in the magnitude of the negative defocus. The multi-morphology 304 L stainless steel (SS304) samples were printed in a single printer using an efficient method for the first time, i.e. printing spherical SS304 material on top of irregular SS304. EDX results indicated an insignificant change of chemical elements between the spherical and irregular prints. EBSD results revealed that columnar grains could grow through the irregular-spherical transition zone and similar grain size can form between spherical and irregular prints. The results of this study provide insights into the optimum printing configurations for powder sheet additive manufacturing using a cost-effective solution of irregular morphology material.