International Journal of Advanced Manufacturing Technology最新文献

Productively reducing the time required to cut numerous through holes in automotive workpieces is crucial for enhancing parts manufacturing in the 3D laser cutting process. However, the conventional cutting strategy, in which the laser beam maintains a stationary posture along the hole path, lacks flexibility and fails to effectively leverage processing tolerances. In this study, we conduct a thorough analysis of the kinematics of a six-axis redundant laser cutting machine and resolve through a decoupling method with singularity management. We propose an innovative conic posture cutting strategy for 3D laser hole-cutting with thin materials. This approach adopts the geometry of a cone as the posture while cutting the hole path. In order to obtain the optimal vertex of the cone while minimizing the taper error generated by the conic posture and kinetic energy consumption of the actuators during motion, we formulate a multi-objective optimization problem and solve it using a genetic algorithm. Furthermore, we enhance the optimization by adopting a time minimization approach. Through the implementation of a B-pillar workpiece cutting experiment, we have successfully validated the credibility of our proposed cutting strategy, thereby demonstrating an enhancement of time on 26 hole-cutting paths.

Reliable prediction of the grinding force is essential for improving the grinding efficiency and service life of the grinding head. To better optimize and control the grinding process of the grinding head, this paper proposes a grinding force prediction method of the grinding head that combines surface measurement, statistical analysis, and finite element method (FEM). Firstly, a grinding head surface measurement system is constructed according to the principle of focused imaging. The distribution model of abrasive grains in terms of size, spacing, and protruding height has been established by measuring and counting the characteristics of abrasive grains on the surface of a real grinding head. Then, the undeformed chip thicknesses when the abrasive grains are cut are analyzed in depth, the material model of abrasive grains and workpiece is established, and the cutting process of abrasive grains with different characteristics on the surface of the grinding head is analyzed by finite element simulation. A single abrasive grain grinding force model is obtained. Finally, the grinding force prediction of the grinding head was realized by combining finite element simulation with grinding kinematics analysis. In addition, grinding experiments with different grinding parameters were conducted to verify the grinding force prediction model. The results show that the predicted grinding force of the grinding head is in good agreement with the experimental values. The average error of tangential grinding force is 7.42%, and the average error of normal grinding force is 9.77%. This indicates that the grinding force prediction method has good accuracy and reliability.

The demand for surface wear-resistant metal components is increasing, but the current traditional preparation method of surface heat treatment for forgings and castings can hardly satisfy the trend of green development. In this study, we developed wire arc additive manufacturing laser cladding (WAAM-LC) hybrid manufacturing technology for the integrated preparation of 304 component with Ni60B reinforced coating. The microstructure and mechanical properties of 304 WAAM entity and Ni60B coating were systematically investigated. The results show that a good metallurgical bond is achieved between the Ni60B coating and 304 substrate. Both γ-Fe and δ-Fe phases appear in the 304 WAAM region, and γ-(Ni,Fe), FeNi₃, (Cr,Fe)_mC_n, and Ni_mSi_n phases are detected in the Ni60B LC coating. Slender columnar crystals and cellular crystals coexist in the 304 bonding region, while the bottom of the Ni60B coating consists of plane crystal and columnar crystals. Both molten pools appear to have a haphazardly growing dendrite structure inside. Compared to the 304 substrate, the hardness of Ni60B coating increases by 2.76 times, and the friction and wear mass loss is only 10.54% of the 304, significantly improving the wear resistance of the 304 substrate. This work indicates that WAAM-LC hybrid technology is expected to become a promising new solution for manufacturing surface wear-resistant metal components.

Graphical Abstract

Hollow microneedles (HMNs) are minimally invasive needle-like microfeatures usually arranged in arrays designed for drug delivery and body fluid collection in a painless manner. In a recent work, we demonstrated a novel methodology to mass produce hollow polymer microneedles at a low cost. This methodology combines ultrashort pulse laser ablation to create inverse needle shapes in moulds and replication through polymer injection moulding. For a HMN to be functional, it should effectively pierce the skin at a low force and enable fluidic passage through the skin without leakage. This study investigates the impact of different laser scanning strategies on the cavity morphology and analyses how the various geometrical characteristics of the needle influence the penetration efficacy. To assess the penetration behaviour of the replicated HMNs, a combination of agarose gel and Parafilm® is employed as an in vitro testing platform. Furthermore, a correlation between HMN geometry, penetration performance, and modification of polymer material and holding pressure during injection moulding is established. The results indicate that a certain needle length is essential for effective penetration. Moreover, minimising the tip area, a factor significantly affecting penetration force, can be achieved by increasing the eccentricity of the scan-free area and expanding the scanning diameter. However, it is important to consider other functional needle features such as the ridge height or full lumen, which come at a cost to the tip sharpness. This work highlights the multiple interactions between the scanning strategy, the injection moulding process parameters, the needle geometry, and the penetration force. This study provides insights into optimization of the HMN design and the fabrication for enhanced penetration efficacy of functional injection-moulded polymeric HMNs.

Additive manufacturing (AM) of injection moulding (IM) tools has attracted significant interest in the polymer manufacturing industry for quite some time. However, hybrid manufacturing (HM) using directed energy deposition (DED), which involves concurrent additive and subtractive manufacture, has not been a commonly used process for IM tooling manufacture. This is apparent despite several advantages over the prevalent laser-powder bed fusion (L-PBF) alternative, including higher build rate, lower cost and integrated machining to directly achieve higher tolerances and surface finish. A key reason for this low utilisation is the limited ability of DED processes to produce circular channel profiles typically used in IM tooling, due to stricter constraints on the manufacturability of overhanging geometry. To address this, a range of self-supporting IM cooling channel profiles suited for hybrid laser and powder-based DED manufacture are proposed in this work. Numerical and experimental evaluations are conducted of the cooling performance of several non-circular conformal cooling channel (NCCC) profiles to identify a profile which achieves the maximum heat transfer for a constant cross-sectional area and coolant flow rate. Experimental studies included AM builds to evaluate the DED manufacturability of the selected NCCC profile on a conformally cooled HM benchmark model, followed by cooling performance characterisation, including a comparison against a reference L-PBF manufactured benchmark model. In conclusion, a shape correcting factor is obtained using response surfaces. This factor is used to convert thermal performance calculations for non-circular profiles to a conventional circular channel profile to simplify the DED manufacturing process for non-circular IM cooling channels.

Predicting a component’s fatigue life requires information on not only the number of stress cycles the component will undergo but also the kind and frequency of those stress cycles, as well as information about the surrounding environment and the intended purpose of the component. Models that can forecast lifespan by utilizing available experimental data are preferred since fatigue investigations are costly and time-consuming. Therefore, this work focuses on fatigue testing of S355 mild steel specimens under multiaxial loading conditions involving bending and torsion. The impact of phase-shifted loading conditions on material behavior, considering in-phase and with angles 0°, 45°, and 90°, has been studied. Then, the data of fatigue tests involve variations in loading amplitudes, and configurations used in different ML algorithms such as lazy k-nearest neighbors (Lazy-KNN), linear regression (LR), and random forest (RF) were employed for predictive modeling. These models are evaluated based on their ability to predict nominal stresses, torsion, and bending moments under varying loading configurations. The predictive modeling results are visually presented, showcasing the effectiveness of Lazy-KNN in accurately predicting material responses. Quantitative analyses further confirm the robustness of Lazy-KNN in predicting bending, nominal stress, and torsion under different loading conditions. The study provides valuable insights into the fatigue behavior of S355 mild steel and highlights the significance of considering multiaxial loading configurations in material testing and design.

Abstract

Electrical discharge drilling of blind holes has been a challenging task due to the inherent difficulties in removing debris from the discharging gap. This paper investigates the working mechanism and effects of new stepped electrodes which are used in conjunction with injection flushing in drilling deep blind holes. A series of theoretical simulations and comparative experiments were conducted using cylindrical electrodes and two types of stepped electrodes. Pulse waveforms were captured to analyse the discharge status. Surface topography and machining quality were analysed using scanning electron microscope (SEM) images. The machining performance was evaluated by studying the material removal rate (MRR) and tool wear ratio (TWR). Experiment results show that internal flushing caused the debris to circulate in the machining zone and led to abnormal discharges, disrupting the formation of the plasma channel. The MRR was increased by 75% and 82% when using cylindrical electrodes with pressures of 120 psi and 40 psi, respectively. In contrast, the MRR with injection flushing was about 80% of that without injection flushing when using stepped electrodes. Regardless of the type of electrode, the application of injection flushing resulted in the increase in the maximum effective machining depth.

In this study, a new method of ultrasonic vibration processing of high-speed EDM ultra-fine copper tube electrode is proposed, and the deformation process of copper tube during processing is simulated by finite element analysis, and a prediction model of wall thickness change of ultrasonically processed copper tube electrode is established, and the predicted values are in good agreement with the experimental measurement data. The surface quality of copper tube electrode after ultrasonic processing was improved, and the roughness was reduced from Ra1.28 to Ra0.43 µm, which is important for the automated production of high-quality ultra-fine copper tube electrode.

In this paper, the geometric error modeling and compensation methods of macro–micro composite five-axis turn-milling composite machining center is studied. Firstly, the machine topological structure of the branches, intermediates, and terminal bodies of the macro–micro composite multi-body system is analyzed. Then, the tool chain transformation matrix and the end position of the workpiece chain are established to obtain the geometric error model. Based on the error compensation theory of traditional machine tool structure, the compensation mechanism of macro-level compensation and micro-level sub-micron compensation is proposed. Then, the compensation model of micro-axis error is given. Furthermore, the macro–micro composite error compensation experiment is setup; the laser interferometer is used to judge the positioning accuracy and straightness before and after compensation. The results show that the accuracy of the micro-motion platform after compensation reaches the sub-micron level, which verifies the compensation method, and the machining accuracy of the micron level is achieved through the cutting experiment.

In order to achieve an optimal system performance, decision makers are continually faced with the responsibility of making choices that will enhance availability and reduce failures cost. To realize this goal, it is crucial to ensure the timely maintenance of equipment, which often poses a significant challenge. However, the adoption of predictive maintenance (PdM) technology can offer a solution by enabling real-time maintenance, resulting in various benefits such as reduced downtime, cost savings, and enhanced production quality. Machine learning (ML) techniques are increasingly being used in the field of predictive maintenance to predict failures and calculate estimated remaining useful life (RUL) of equipment. A case study is proposed in this research paper based on a maintenance dataset from the aerospace industry. It experiments and compare multiple combination of feature engineering techniques and advanced ML models with the aim to propose the most efficient techniques for prediction. Moreover, future research papers can focus on the challenge of validating this proposed model in different industrial environments.