Advances in Manufacturing最新文献

The real-time monitoring of the weld pool during deposition is important for automatic control in plasma arc additive manufacturing. To obtain a high deposition accuracy, it is essential to maintain a stable weld pool size. In this study, a novel passive visual method is proposed to measure the weld pool length. Using the proposed method, the image quality was improved by designing a special visual system that employed an endoscope and a camera. It also includes pixel brightness-based and gradient-based algorithms that can adaptively detect feature points at the boundary when the weld pool geometry changes. This algorithm can also be applied to materials with different solidification characteristics. Calibration was performed to measure the real weld pool length in world coordinates, and outlier rejection was performed to increase the accuracy of the algorithm. Additionally, tests were carried out on the intersection component, and the results showed that the proposed method performed well in tracking the changing weld pool length and was applicable to the real-time monitoring of different types of materials.

The exploration of component states for optimizing maintenance schedules in complex systems has garnered significant interest from researchers. However, current literature usually overlooks the critical aspects of system flexibility and reconfigurability. Judicious implementation of system reconfiguration can effectively mitigate system downtime and enhance production continuity. This study proposes a dynamic condition-based maintenance policy considering reconfiguration for reconfigurable systems. A double-layer decision rule was constructed for the devices and systems. To achieve the best overall maintenance effect of the system, the remaining useful life probability distribution and recommended maintenance time of each device were used to optimize the concurrent maintenance time window of the devices and determine whether to reconfigure them. A comprehensive maintenance efficiency index was introduced that simultaneously considered the maintenance cost rate, reliability, and availability of the system to characterize the overall maintenance effect. The reconfiguration cost was included in the maintenance cost. The proposed policy was tested through numerical experiments and compared with different-level policies. The results show that the proposed policy can significantly reduce the downtime and maintenance costs and improve the overall system reliability and availability.

An axial wrinkle defect was observed in the inner wall of the sinking zone of a thick-wall steel tube processed by cold radial forging. Wrinkles can evolve into fissures. The present study focuses on the evolution of wrinkles and the effects of process parameters on them using a three-dimensional radial forging process finite element model, radial forging experiments, and surface morphology observations. The results indicated that the vertical section angle of the hammer die and the size of the tube blank significantly affect the evolution of wrinkles. The height-to-width ratio λ was introduced to describe the morphology of wrinkles. It had an approximately linear relationship with the radius reduction in the sinking zone. There was a linear correlation between the growth slope of λ and the axial to circumferential strain ratio |ε_r/ε_θ|, which can predict the λ under few process parameters. For the 30SiMn2MoVA steel, at the junction of the forging and sinking zones, the threshold of λ of the wrinkle that can evolve into a fissure is approximately 1.123.

Model design and slicing contour generation in additive manufacturing (AM) data processing face challenges in terms of efficiency and scalability when stereolithography files generated by complex functionally graded structures have millions of faces. This paper proposes a hybrid modeling and direct slicing method for AM to efficiently construct and handle complex three-dimensional (3D) models. All 3D solids, including conformal multigradient structures, were uniformly described using a small amount of data via signed distance fields. The hybrid representations were quickly discretized into numerous disordered directed lines using an improved marching squares algorithm. By establishing a directional HashMap to construct the topological relationship between lines, a connecting algorithm with linear time complexity is proposed to generate slicing contours for manufacturing. This method replaces the mesh reconstruction and Boolean operation stages and can efficiently construct complex conformal gradient models of arbitrary topologies through hybrid modeling. Moreover, the time and memory consumption of direct slicing are much lower than those of previous methods when handling hybrid models with hundreds of millions of faces after mesh reconstruction.

Currently, simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing systems. Simultaneous allocation problems have been solved previously to satisfy economic requirements; however, owing to the progress of green manufacturing, energy conservation and environmental protection have become increasingly crucial. Therefore, an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems, subject to the total buffer capacity, total service rate, and predefined energy efficiency. The energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression tree. The former algorithm searches for Pareto-optimal solutions of sufficient quality. The latter algorithm builds prediction models to rapidly calculate the throughput, energy consumption, and energy efficiency. Numerical examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported approaches. Furthermore, the prediction models can rapidly evaluate manufacturing systems with sufficient accuracy. This study benefits the multi-objective optimization of green manufacturing systems.

The vibration disturbance from an external environment affects the machining accuracy of ultra-precision machining equipment. Most active vibration-isolation systems (AVIS) have been developed based on static loads. When a vibration-isolation load changes dynamically during ultra-precision turning lathe machining, the system parameters change, and the efficiency of the active vibration-isolation system based on the traditional control strategy deteriorates. To solve this problem, this paper proposes a vibration-isolation control strategy based on a genetic algorithm-back propagation neural network-PID control (GA-BP-PID), which can automatically adjust the control parameters according to the machining conditions. Vibration-isolation simulations and experiments based on passive vibration isolation, a PID algorithm, and the GA-BP-PID algorithm under dynamic load machining conditions were conducted. The experimental results demonstrated that the active vibration-isolation control strategy designed in this study could effectively attenuate vibration disturbances in the external environment under dynamic load conditions. This design is reasonable and feasible.

Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts. Traditional embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the part. In this paper, a non-contact, full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is proposed. Using the proposed method, an average temperature monitoring accuracy of 97% is achieved in various heating patterns. In addition, this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part. This method was experimentally validated during the self-resistance electric heating process, in which the monitoring accuracy reached 93.1%. This method can potentially be applied to automated manufacturing and process control in the composites industry.

An effective and reliable prediction of the remaining useful life (RUL) of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance, avoid machine shutdowns and increase system stability. This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions. The data-driven approach took advantage of bidirectional long short-term memory (BLSTM) and convolutional neural networks (CNN). A pre-trained lightweight CNN-based network, WearNet, was re-trained to classify the wear states of workpiece surfaces with a high accuracy, then the classification results were passed into a BLSTM-based regression model as inputs for RUL estimation. The experimental results demonstrated that this approach was able to predict the RUL values with a small error (below 5%) and a low root mean square error (RMSE) (around 1.5), which was more superior and robust than the other state-of-the-art methods.

In this study, a new method was developed to realize two-dimensional (2D) figure correction of grazing-incidence X-ray mirrors using a one-dimensional (1D) ion-beam figuring system. A mask of holes was specifically designed to generate removal functions at different widths and extend the figuring capability over a wide area. Accordingly, a long mirror could be manufactured. Using this method, the surface height root-mean-square (RMS) error of the center area of 484 mm ×16 mm was reduced from 11.49 nm to 2.01 nm, and the 1D meridional RMS error reached 1.0 nm. The proposed method exhibits high precision and cost effectiveness for production of long X-ray mirrors.

Production logistics (PL) is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing systems. To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits, this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain (MTDC). First, the manufacturing task chain (MTC) is defined to characterize the discrete production process of a product. To handle manufacturing big data, the MTC data paradigm is designed, and the MTDC is established. Then, the logistics trajectory model is presented, where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC. Based on this, a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the PL. Finally, a case study is applied to verify the proposed method, and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment, which can assist managers in implementing the optimization decisions.