International Journal of Advanced Manufacturing Technology最新文献

Abstract

Closed-die forging preforms are usually made by open die forging operations, which are subject to significant variabilities. A sensitivity study covering a wide range of influencing parameters has highlighted the predominant influence of the initial billet geometry. The forging die strokes were also highly influential, while their fidelity is sufficient to use them as control parameters in order to compensate the geometrical dispersions of the billet. Consequently, their optimization was performed by taking a nominal preform geometry as the target. Polynomial surrogate models have been constructed to enable real-time optimization. A specific preform was used as a demonstrator in this study, while the approach was generic. The surrogate models were built using data from finite element simulations, which were first validated with an experimental campaign. On the one hand, this approach introduced agility by allowing changes in the billet geometry, and on the other hand, it allowed individual customization of the specific route to each billet.

The forming grinding process is a crucial method for helical flute grinding of the end mill and screw tap, and the grinding wheel profile is the key to ensure the precision of the helical flute, which is constrained by the grinding wheel posture. However, the existing methods have some deficiencies such as the invalidity or uneven wear problems. In this paper, an optimized calculation method of the grinding wheel profile for the forming grinding of the helical flute is proposed, including the suitable grinding posture correspondingly. Firstly, the parametric models of the helical cutting edge and the helical flute surface are established. Secondly, the contact condition between the grinding wheel and the flute is deduced to calculate the grinding wheel profile by the envelop theory. Thirdly, an optimization method for grinding wheel profile is proposed, which could predict of the solution interval, avoid the profile intersection, and improve wear resistance. Finally, the method was verified by a series of simulations and experiments, and the results show that the method could meet the grinding precision requirements and expand the application range of forming grinding technology in helical flute.

The inclusion of additive manufacturing (AM) as an automated repair method leads to a sustainable remanufacturing process, which is known as additive repair. Despite its potential in improving the efficiency of repair and restoration, additive repair remains in its infancy and requires a thorough investigation on part design and process parameters. The major concern raised in additive repair is the capability to create perfect bonding between two metals, which will affect the mechanical properties of the complete repaired part. Hence, performing evaluation from the beginning is crucial to validate the feasibility of the process through appropriate structural analysis and to obtain deformation and stress results. Brake caliper housing is selected as a remanufacturable component for case exemplary purposes. Prior to analysis, the potential damages and failures of the brake caliper component were initially evaluated through literature surveys and direct interviews with industry experts where two types of damages were identified, namely, cracks and broken or fractured parts. Then, the validation focuses on comparative analysis of two different conditions of the brake caliper housing: original, and repaired caliper model using finite element analysis in ANSYS. Results indicate that the strength of the repaired caliper model shows equal and higher strength compared with the original model. This result confirms that the repair process through AM can retain or improve the quality of the remanufactured brake caliper housing. Therefore, this paper provides a systematic framework for the evaluation of mechanical properties in multi-metal additive repair with the integration of failure analysis techniques.

Ball-end milling cutters are commonly used in the finishing processes of curved-side milling for titanium alloys; however, several issues arise during machining, such as poor cutting conditions at the bottom of the end teeth, low cutting speeds, and limited chip space. Given the above issues, the research on the design and manufacture of conical arc side-edge milling cutter for titanium alloy processing was carried out in this paper; the mathematical model of the vital structure of conical arc side-edge milling cutter was established; the grinding trajectory equations of tool front and flank were deduced; the tool-workpiece kinematics of ultrasonic vibration applied to conical arc side edge was studied; and the comparative experimental study of the conical arc side-edge milling cutter cutting titanium alloy with and without ultrasonic vibration was carried out. The experiment results indicate that in comparison to conventional milling techniques, ultrasonic vibration cutting significantly decreases cutting force, plastic deformation of the chip, and wear rate of the flank face. The tool wear band is both longer and more uniform, bonding phenomena in titanium alloys are distinctly reduced, and tool performance is improved.

Accurate tool wear prediction is of great significance to improve production efficiency, ensure product quality and reduce machining cost. This paper proposes a hybrid physics data-driven model-based fusion framework for tool wear prediction to improve low prediction accuracy of physical model and poor interpretation of data-driven model. In this framework, physical information and local features of sensor measurement signals are used as inputs to build a hybrid physics data-driven (HPDD) model. And data mining and physics principles are effectively integrated by using unlabeled samples for data expansion. Piecewise prediction is introduced to reduce difficulty in parameter estimation. Then, in order to manage prediction uncertainty of physical information and HPDD method, two prediction results are gradually combined based on Bayesian fusion mechanism to eliminate prediction error. Finally, the effectiveness of the proposed method is verified by experiment. Compared with existing methods, this method significantly improves prediction. The mean values of root mean square error (RMSE) and mean relative error (MARE) for tool wear prediction results are respectively 2.28 and 1.85.

Owing to low machining efficiency, poor machining accuracy, and surface quality in traditional electrical discharge machining (EDM) of TC4 titanium alloy holes, an EDM trepanning method was developed using a deionized water medium and a flushing liquid in tube electrodes (denoted by EDM-TFD). Several experiments and research mechanisms were conducted on EDM-TFD. Compared with traditional EDM, ED milling, and EDM trepanning, EDM-TFD featured increased hole machining efficiency by more than five times, improved machining taper by more than 57%, and improved surface quality by more than 40%. The optimal processing technology was determined through process experiments using copper tube electrodes, a flushing liquid with a speed of 2 m/s, pulse width (T_on) of 150 μs, pulse interval (T_off) of 150 μs, and peak current (I_p) of 15 A. Under the optimized process, the hole exhibited a feed rate of 1.1 mm/min, machining efficiency of 38 mm³/min, machining taper of 40 μm, and surface roughness of 5.2 μm.

Shaft sleeve assembly is a common task in industrial manufacturing. The fitting approach for shaft sleeve assembly is usually interference fit, which requires significant contact forces. Conventional assembly methods, though focused on safety, often struggle to achieve high efficiency. Reinforcement learning can effectively select appropriate assembly actions through interaction with the environment, making it well-suited for shaft sleeve assembly tasks. Firstly, a comprehensive workflow for shaft sleeve assembly is formulated, including system initialization, insertion, push, and completion. Our research focuses mainly on the insertion process. Secondly, the core control algorithm adopts a deep reinforcement learning method based on the Actor-Critic architecture. The reward function includes safety reward, step length reward, and step reward. Safety reward ensures assembly security, while step length and step reward enhance assembly efficiency from different perspectives. Finally, real-world experiments on shaft sleeve assembly are conducted, including ablation experiments, parameter tuning experiments on reward function, and comparative experiments with conventional methods. The results of the ablation experiments and parameter tuning experiments indicate that after combining safety reward, step length reward, and step reward, the assembly effect achieves the best, verifying the effectiveness of the proposed reward function. Comparative experimental results demonstrate that our approach not only enhances safety compared to conventional methods but also significantly improves assembly efficiency, indicating the feasibility of this method.

Ensuring first-time-right on-site repair of critical structures is a key challenge for additive manufacturing (AM)–based repair solutions. Fast thermal simulations are thus needed to plan efficient and error-free AM processes. This paper addresses a fast thermal simulation method for a novel subsea wire arc additive manufacturing (SWAAM) repair procedure. Current commercial finite element (FE) codes for typical welding and AM are computationally expensive and slow. The presented 2D finite difference approach can be used to simulate SWAAM on a damaged plate with around 70 times acceleration compared to real welding times, without the use of parallelization. Although not being able to accurately represent the temperature in close vicinity of the welding torch, the approach shows excellent correspondence with FE simulations and experiments in regions of the plate where the temperature has assumed a distribution that is largely two-dimensional. Compared with FE simulations, the approach is experimentally verified to be accurate to 10 °C within 7 s after the welding torch has passed a point on the plate. Thus, the approach can provide a measure of the global temperature field in a thin-walled structure during repair. The thermal simulation is preceded by a welding path planner, which generates appropriate paths based on slicing of a 3D surface scan of the damage that is to be repaired. Damages to equipment or non-ideal welding conditions are prevented by automatically pausing the welding if the calculated temperature in the path ahead of the welding torch exceeds a predefined interpass temperature limit.

Abstract

Due to the inevitable deviation of the casting process, the dimensional error of the turbine blade is introduced. As a result, the location datum of the film cooling holes is changed, which has an impact on the machining accuracy. The majority of pertinent studies concentrate on the rigid location approach for the entire blade, which results in a modest relative position error of the blade surface but still fails to give the exact position and axial direction of the film cooling holes of the deformed blade. In this paper, the entire deformation of the blade cross-section curve is divided into a number of deformation combinations of the mean line curve based on the construction method of the blade design intent. The exact location of the film cooling holes in the turbine blade with deviation is therefore efficiently solved by a flexible deformation of the blade that optimises the position and axial direction of the holes. The verification demonstrates that the novel method can significantly reduce both the contour deviation of the blade surface and the location issue of the film cooling holes. After machining experiments, the maximum position deviation of the holes is reduced by approximately 80% compared to the rigid location method of the entire blade, and the average value and standard deviation are also decreased by about 70%.

Abstract

Green manufacturing is one of the most important development directions in mechanical processing field. Electrical discharge machining (EDM), one of the non-traditional machining, is increasingly used. However, there were hardly any studies on the evaluation of energy consumption and carbon emissions in EDM. In this study, a quantitative assessment model of carbon emission in EDM was built based on the emission factor method. The tool electrode wear, harmless treatment of residual tool electrodes and working fluid, and electrical energy consumed by the equipment were considered in this assessment model. EDM drilling experiments were conducted to verify the effectiveness of the proposed model. The effects of pulse width, pulse interval, and peak current on machining time, surface roughness, energy consumption, and carbon emissions were analyzed. The CNC system, cooling system, and power supply consumed about 95% of the total energy. In small hole EDM drilling, the total carbon emissions from the preparation and waste residue treatment of workpiece and tool electrode were almost negligible due to the small material removal volume. The carbon emissions generated by electrical energy consumption account for about 50% of the total carbon emissions. Carbon emissions can be minimized to 72 g and energy consumption can be reduced to a minimum of 37.48 Wh when processing a small hole with the diameter of 1 mm and the depth of 6 mm by EDM drilling.