Advances in Manufacturing最新文献

Ultrasonic rolling is an advanced non-cutting surface strengthening method that combines traditional rolling with ultrasonic vibration. In this research, the experiment of orthogonal end milling-ultrasonic rolling composite process has been carried out. The surface integrity refactoring changes and its mechanism of Ti-17 titanium alloy during the milling- ultrasonic rolling composite process has been studied and analyzed by the test and analysis of the surface geometric characteristics, residual stress, microhardness and microstructure before and after ultrasonic rolling. The residual stress and microhardness gradient distribution were characterized by cosine decay function and exponential decay function. All indicators of surface integrity were significantly improved after ultrasonic rolling. The study demonstrates that the reduction effect of the surface roughness by ultrasonic rolling process is inversely proportional to the initial surface roughness value. The ultrasonic rolling can only change the distribution form of the surface topography when the initial surface roughness is small. In addition, the improvement effect of ultrasonic rolling on surface compressive residual stress and microhardness decreased with the increase of initial milled surface roughness and surface compressive residual stress due to the factors such as energy absorption efficiency and mechanical properties changes of surface materials. A better ultrasonic rolled surface can be obtained by controlling the roughness and residual compressive stress of the initial milling surface to a small level.

In micro electrochemical machining (ECM) processes, stray corrosion causes undesired metal dissolution and the deterioration of shape accuracy. Adopting a sidewall-insulated electrode is an effective approach to suppressing stray corrosion. Most sidewall-insulated electrodes are made of metal substrate and non-metallic thin films. Nevertheless, the thin-film insulating materials attached to a metal substrate are susceptible to damage in an electrolytic environment. This study presents a novel concept of the conductive-material-filled electrode for better sidewall-insulation performance. The micro-scale quartz tube serves as the insulating substrate. Commercially available conductive fillers including metal wire, molten metals, and silver powder are filled inside the working cathode of the quartz tube. Consequently, the metal-wire-filled electrode, molten-metal-filled electrode, and nano-powder-filled electrode are designed and fabricated. From the verification results of electrode toughness, material removal rate, and surface topography, the metal-wire-filled electrode and molten-metal-filled electrode exhibit the same performance as a traditional metal-based electrode and much better durability. By contrast, the nano-powder-filled electrode is unable to withstand long-term ECM processes because of the loss of cured powder particles. In ECM experiments, microstructures with steep sidewalls (taper angle <9.7°) were machined using the metal-wire-filled electrode and molten-metal-filled electrode, which could replace the traditional electrode, achieving a longer service life and superior sidewall-insulation performance.

Micro-texturing has been widely proven to be an effective technology for achieving sustainable machining. However, the performance of micro-textured tools under different cooling conditions, especially their coupling effect on machined surface integrity, was scarcely reported. In this paper, the non-textured, linear micro-grooved, and curvilinear micro-grooved inserts were used to turn aluminum alloy 6061 under dry, emulsion, and liquid nitrogen cryogenic cooling conditions. The coupling effects of different micro-textures and cooling conditions on cutting force, cutting temperature, and machined surface integrity, including the surface roughness, work hardening, and residual stress, were revealed and discussed in detail. Results indicated that the micro-grooved tools, especially the curvilinear micro-grooved tools, not only reduced the cutting force and cutting temperature, but also improved the machined surface integrity. In addition, the micro-grooved tools can cooperate with the emulsion or liquid nitrogen to reduce the cutting force, cutting temperature, and improve the machined surface integrity generally, although the combination of emulsion cooling condition and micro-grooved tools generated negative coupling effects on cutting forces and surface work hardening. Especially, the combination of curvilinear micro-grooved cutting tools and cryogenic cooling condition resulted in the lowest cutting force and cutting temperature, which generated the surface with low roughness, weak work hardening, and compressive residual stress.

Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

The dynamic force load in grinding process is considered as a crucial factor affecting the quality of parts, and a better understanding of the mechanism of force generation is conducive to revealing the evolution of material microstructure more precisely. In this study, an iterative blending integrating grinding force model that comprehensively considers the impact of grain size and dislocation density evolution of the material is proposed. According to the grinding kinematics, the interaction of grit-workpiece is divided into rubbing, plowing, and chip formation stages in each grinding zone. On this basis, the evolution of material microstructure in the current chip formation stage will affect the rubbing force in the next grinding arc through flow stresses, which in turn will influence the total grinding force. Therefore, the flow stress models in rubbing and chip formation stages are firstly established, and then the dislocation density prediction model is established experimentally based on the characteristics of grain size. The effects of the evolution of grain size and dislocation density on the grinding forces during the grinding process are studied by means of iterative cycles. The results indicate that the implementation of an iterative blending scheme is instrumental in obtaining a higher accurate prediction of the grinding force and a deeper insight of the influence mechanisms of materials microstructure on grinding process.

Fast drilling electrical discharge machining (EDM) is widely used in the manufacture of film cooling holes of turbine blades. However, due to the various hole orientations and severe electrode wear, it is relatively intricate to accurately and timely identify the critical moments such as breakout, hole completion in the drilling process, and adjust the machining strategy properly. Existing breakout detection and hole completion determination methods are not suitable for the high-efficiency and fully automatic production of film cooling holes, for they almost all depend on preset thresholds or training data and become less appropriate when machining condition changes. As the breakout and hole completion detection problems can be abstracted to an online stage identification problem, in this paper, a kurtosis-based stage identification (KBSI) method, which uses a novel normalized kurtosis to denote the recent changing trends of gap voltage signals, is developed for online stage identification. The identification accuracy and generalization ability of the KBSI method have been verified in various machining conditions. To improve the overall machining efficiency, the influence of servo control parameters on machining efficiency of each machining stage was analyzed experimentally, and a new stage-wise adaptive control strategy was then proposed to dynamically adjust the servo control parameters according to the online identification results. The performance of the new strategy is evaluated by drilling film cooling holes at different hole orientations. Experimental results show that with the new control strategy, machining efficiency and the machining quality can be significantly improved.

In this study, a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented. The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process. The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals, thus the interference of process disturbance is solved. Furthermore, the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals, and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model. Finally, convergence analysis of the suggested identification scheme is derived using stochastic process theory. The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.

Angular rolling technology can overcome the size limitation of plate mill equipment and product heavy steel plate with large unit weight or improve the production efficiency of small width spreading ratio product. With the DEFORM software, the numerical simulation study of the angular rolling process was carried out, and the relation laws of the width, rolling force and strain of the rolled piece under different angular rolling process conditions were obtained. The simulation results show that with the rotation angle increasing, the width of the rolled piece increases. Comparing with the conventional rolling process, the rolling force changes gradually during the biting and throwing stage of the angular rolling pass. With the rotation angle increasing, both the equivalent strains in the thickness direction and in the width direction gradually increase. According to the pattern and dimension’s changing formula in the double-pass angular rolling process, the prediction model of angle, reduction and width spreading is built. The opening value of side guide is set for the rotation angle controlling. For one 5 000 mm heavy plate mill, the automation control system was modified, and the angular rolling technology was applied online. The absolute deviation of target width does not exceed ± 20 mm and the relative deviation does not exceed 1%. The large unit weight plate that cannot be rolled with traditional process, can be produced now, and the annual output increases by 10 000 t.

Although incremental sheet forming (ISF) is an efficient way to manufacture customized parts, the forming performance and geometric accuracy of formed parts need to be improved to meet industrial application. One feasible solution for these problems is to adopt proper heat treatment strategies for the sheet material both before and during the forming process. In this paper, the effects of heat treatment before forming and heat-assisted forming on the formability and performance of formed parts were experimentally investigated. First, TA1 sheets were heat-treated at different temperatures before forming, and then the sheets were incrementally formed into the target shape with variable angles at different temperatures. After heat treatment, the strength of sheets was decreased due to the occurrence of recrystallization and the growth of grains. Meanwhile, the surface quality of formed parts was also improved with pre-heat treatment before forming. During the heat-assisted forming process, the sheet was softened and the deformation resistance was reduced with the increase of temperature. Therefore, the axial forming force was decreased obviously and the formability of the sheet was increased obviously. Furthermore, by adopting both heat treatment and heat-assisted forming, it was found that the forming force could be further reduced and the formability of the sheet and surface quality could be further improved. As for geometric accuracy, heat treatment has a good effect on improving it, while heat-assisted forming has adverse effect. These findings provide an effective heat treatment strategy for improving the geometric accuracy and surface quality of the incrementally formed parts with lower forming force.

Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.