Co-MoS2 composite coatings were deposited by the magnetic jet electrodeposition under different magnetic field intensities. The growth orientation and deposition mechanism of Co-MoS2 composite coatings under magnetic jet electrodeposition were analyzed based on the weak adsorption theory of nanocomposite co-deposition. Research showed that the coating topography changed from a blade-like structure to a cellular structure, and the cellular structure became a granular protrusion structure with increase of the magnetic field intensity. Compared with Co-MoS2 composite coating in jet electrodeposition, the orientation of Co shifted from (110) to (101) as the magnetic field intensity increased in magnetic jet electrodeposition. The adhesion, microhardness, adhesion, corrosion and wear resistance of the composite coatings were enhanced, which was attributed to the influence of the magnetic field on the growth orientation of the composite coating and the coordinated lubrication effect in the Co-MoS2 composite coating.

The machining process of Carbon Fiber Reinforced Polymer/Titanium (CFRP/Ti) stacks presents significant challenges owing to the inherent inhomogeneity of CFRP and the low thermal conductivity of Ti. The drilled hole surface can experience machining-induced damage, such as delamination, fiber pull-out, and burr formation. The present study aimed to examine the influence of cutting parameters and coatings, as well as tool wear mechanisms, on the occurrence of machining-induced damage in CFRP/Ti stacks during dry drilling. Moreover, a new method called MATLAB-Assisted Image Processing (MAIP) is introduced to assess machining-induced damage, specifically delamination and burr formation, in the context of CFRP/Ti stack drilling. The use of advanced MATLAB-assisted image processing improves the precision of MAIP. The coefficient of repeatability is 0.001394. TiAlN-coated drills offer notable advantages, resulting in a 13% decrease in the maximum thrust force for CFRP and a 10% reduction for Ti. Furthermore, there is an 11% decrease in delamination compared to a tool that lacks a coating. The experimental findings revealed a significant correlation between machining-induced damage and the mechanisms of tool wear.

Visual grasping technology plays a crucial role in various robotic applications, such as industrial automation, warehousing, and logistics. However, current visual grasping methods face limitations when applied in industrial scenarios. Focusing solely on the workspace where the grasping target is located restricts the camera’s ability to provide additional environmental information. On the other hand, monitoring the entire working area introduces irrelevant data and hinders accurate grasping pose estimation. In this paper, we propose a novel approach that combines a global camera and a depth camera to enable efficient target grasping. Specifically, we introduce a dual-layer detection mechanism based on Faster R-CNN–GRCNN. By enhancing the Faster R-CNN with attention mechanisms, we focus the global camera on the workpiece placement area and detect the target object within that region. When the robot receives the command to grasp the workpiece, the improved Faster R-CNN recognizes the workpiece and guides the robot towards the target location. Subsequently, the depth camera on the robot determines the grasping pose using Generative Residual Convolutional Neural Network and performs the grasping action. We validate the feasibility and effectiveness of our proposed framework through experiments involving collaborative assembly tasks using two robotic arms.

In this paper, friction stir spot welding (FSSW) technology was introduced to join 7075 Al alloy to polyimide (PI) sheets. The effects of the FSSW parameters and laser textures on the microstructure and tensile shear load of the Al/PI joint were studied. The optimum FSSW parameters were the rotation speed of 2400 r/min, the plunge depth of 0.4 mm, the dwell time of 20 s, and the plunge speed of 10 mm/min. The tensile shear load of the joint increased owing to the laser textures in the Al alloy surface. Cross-sections showed that the mechanical locking was found between the Al alloy and PI. The XPS results revealed that no chemical bond were formed at the welding interface after laser textures. The joining mechanism of Al 7075 and PI was main the mechanical locking.

Joint maintenance and production planning result in enhanced system efficiency and a significant reduction in expensive costs. Therefore, this research proposed an integrated blockchain design of two optimization models for the joint scheduling and sequencing of production orders and maintenance jobs over multiple periods with probabilistic and stochastic parameters for the oil-filling process. In the scheduling model, the objective function was to minimize total production and maintenance costs. Further, the sequencing model aimed to minimize the total overtime cost and the sum start times of jobs and orders. The proposed models were implemented on five production lines of five-liter lube oil bottles. Results showed that the developed optimization models effectively achieved concurrent production and maintenance planning at minimal total cost. In practice, this results in effective resource utilization, and saving costly production and maintenance costs. In conclusion, the developed blockchain design is valuable in developing, managing, and controlling optimal joint production and maintenance planning of oil-filling production lines.

This article addresses the energy consumption optimization problems of the pickling process for titanium strip manufacturing. The hybrid flow shop scheduling schemes for the pickling process of titanium strips are designed, and a novel shop scheduling method based on reinforcement learning is proposed for the pickling process of titanium strips. In the scheduling scheme, the pickling chemical treatment process of titanium strips are described as an asymmetric hybrid flow shop scheduling problem (AHFSP), and a mathematical model containing a temperature structure is established with the optimization objectives of minimizing pickling time and energy consumption. Based on the proposed scheduling scheme, a novel shop scheduling method based on reinforcement learning for the titanium strip pickling process is proposed. First, a mixed integer linear programing model for the mixed flow shop scheduling problem is established. Second, the flow shop scheduling problem with sequential energy consumption decisions is approximated as an asymmetric traveling sales-man problem (ATSP). Finally, the ATSP is described as a Markov decision processes (MDP), and a Q-learning based scheduling method for titanium strip pickling shops is proposed. Finally, the effectiveness of the proposed method is verified by examples, and the scheduling scheme can reduce the energy consumption by 16.61% on average while maintaining the schedule, which improves the productivity and economic efficiency.

Thickness and flatness are important quality indicators for strip. It is important that the rapid and accurate prediction of the exit thickness and flatness for the optimal control of the hot strip rolling process. Due to the fast and long rolling process, there are time delays, non-linearity and strong coupling among the variables, which cause difficulties in the establishment of prediction models. In this paper, the variables related to thickness and flatness are selected by analyzing the rolling process mechanism and data. Based on the data related to the rolling quality, a rolling exit thickness and flatness joint prediction model combined multi-indicator Transformer with embedded sliding window (SW-MTrans) is proposed. First, a sliding window is embedded into the input layer of the model in order to address the effect of the time delay among variables. Then a Transformer network is improved to achieve accurate prediction of thickness and flatness simultaneously. It is verified that the proposed method can predict the thickness and flatness at the same time with higher prediction accuracy and generalization ability compared with other methods through actual production data. The mean absolute error (MAE) for thickness prediction was reduced by 19.37% and MAE for flatness prediction was reduced by 14.03% compared to the existing prediction model.

Of late, aluminum alloy sheets are being increasingly used in the fabrication of automotive, marine, and aircraft parts. Typically, a metal-forming process is used to produce these parts. However, the fracture-free cut surface characteristics of these parts are still limited by the die cutting process, and a secondary operation, such as machining, is needed to overcome this limitation. In this study, the use of cryogenic temperatures in press shaving was investigated. In the shearing operation, the cryogenic temperature influenced the ratios of the die-roll, smooth-surface, and fracture-to-material thickness, particularly for the fracture texture. Applying cryogenics in the shearing process increased the smoothness of the surface by approximately 50%, and the concave feature formed on the sheared workpiece was approximately 45% deep. Additionally, the hardness under cryogenic-temperature condition was approximately 15% higher than that at room temperature. However, the shearing force increased by approximately 30%. With the shaving operation, the volume of the shaving allowance was reduced owing to the deeper concave features. This resulted in a downward movement of the shaving allowance during the shaving operation, allowing easier sliding along the punch face and easier bending underneath the punch face. Consequently, tearing could be prevented, and the shearing phase of the shaving operation could be delayed. The results revealed that compared with the conventional press-shaving process, in which tearing and fracture of approximately 0.393 mm were generated, the application of cryogenic temperature to the press-shaving process delayed the tearing and prevented fracture, thereby achieving a fracture-free cut surface characteristic.

By studying the workpiece-edge-coupling (WEC) in five-axis ball-end milling, the contact characteristics between the workpiece and edge curve are analyzed, and the chip model is extracted and simplified. The edge curve involved in the cutting process of each edge are calculated at each time and a new instantaneous numerical chip thickness model is established. Then the milling forces and stability lobe diagram (SLD) are calculated in following cut process with lead and tilt angle. The milling forces and SLD of lead angle at 15° and tilt angle at −15° are verified by comparing with Otzurk model as references, and it is found the SLD of WEC model can reflect the unstable points more properly than that of Otzurk model. Also the vibration in [Formula: see text] and [Formula: see text] directions show a divergence trend, which proves the high precision for the new algorithm adapted to the stability prediction of five-axis ball-end milling process. In addition, the surface topography is acquired considering lead and tilt angle as well as forced vibration, and the result is consistent with the experiment in the existing literature. It is found that the milling forces, SLD and surface topography show the same variation trend with the increase of lead and tilt angle. Besides, the stability region significantly expands and the surface topography improves by applying positive tilt angle other than the negative. Then, under the conditions of positive lead and tilt angle, increasing lead angle and decreasing tilt angle reduces the milling force, expands the stability region of SLD and improve the surface topography. The optimized tool posture is acquired by the coincident analysis of milling force, SLD and surface topography under different lead and tilt angle.

Lightweight design is an essential topic in aerospace, automotive and other fields. In automobile manufacturing, the engine connecting rod is one of the main components; its lightweight design has a high reference value. And 3D printing provides a feasible solution for designing and manufacturing lightweight structures. Unlike the traditional geometrically homogeneous point design, this study offers a non-homogeneous point design method based on the spatial stress state of additively manufactured components. After numerical simulation of quasi-static stresses on a model of an engine connecting rod, finite element grid cells with different stress values are replaced by lattice cells with different specific stiffnesses at similar local stress levels. The overall specific stiffness of the structure is further improved by continuing the optimized design with the corresponding gradient-type reinforcement of the non-uniform lattice structure. The basic idea of this design is to perform non-uniform adaptive filling of solid parts under localized loading by employing different types of unit cells. Stereolithography 3D printing technology prepares the engine lattice structural parts for quasi-static compression comparison experiments and fracture analysis after failure. The conclusions show that the engine connecting rod members with non-homogeneous lattice have more excellent overall mechanical properties than homogeneous lattice members. This work demonstrates the feasibility of such design methods for 3D printing lightweight structures and optimization.