Jizhuang Hui

To improve the pressure distribution uniformity on the workpiece surface and enhance the flow efficiency of the slurry during the chemical mechanical polishing (CMP) process, a polyurethane polishing tool featuring involute groove microstructures with a 60-mm base circle radius was designed in this study. First, drawing upon classical reactor theory, a modified Preston material removal equation was derived. This formulation establishes a theoretical model for slurry flow and stress transmission that synergistically couples fluid dynamics with mechanical contact mechanics. Subsequently, a three-dimensional volume of fluid (VOF) multiphase flow simulation model was constructed using Fluent software to numerically analyze the slurry distribution of the involute-grooved polishing tool with a 60-mm base circle radius during the CMP process. Simulation results indicate that this polishing tool significantly enhances the flow and distribution uniformity of the slurry. Finally, polyurethane polishing tools featuring the proposed structural design were fabricated, and CMP verification experiments were performed on silicon carbide (SiC) workpieces using a double-sided lapping and polishing machine. Experimental results demonstrate that the involute-grooved polishing tool with a 60-mm base circle radius exhibits excellent machining performance: the surface roughness ( Ra ) of the processed SiC was reduced to 0.87 μm, and the material removal rate ( MRR ) increased to 0.34 mg/min. This study demonstrates that the involute microstructure design can effectively regulate slurry distribution within the machining zone and significantly enhance the ultra-precision surface quality of hard and brittle materials.

To comprehensively investigate the nanoscopic material removal mechanisms of monocrystalline silicon in ultraprecision grinding, this research develops a large molecular dynamics simulation framework. The simulation outcomes reveal that atoms pile up at the leading edge of the abrasive particle to generate chips, whereas the underlying atomic layers are compressed downwards to create the finalized machined surface. Furthermore, both tangential and normal cutting forces exhibit a continuous upward trend as the depth of cut increases. Concurrently, intensified frictional interactions lead to elevated internal stresses and temperatures, thereby expanding the subsurface damage depth and aggravating structural phase transitions. Once the local stress exceeds a critical threshold, atomic bonds rupture, initiating dislocation nucleation. Ultimately, these microscopic insights offer essential theoretical references for optimizing the ultraprecision grinding operations of monocrystalline silicon.

Pronounced thermal gradients inherent to the Selective Laser Melting (SLM) process frequently give rise to considerable residual stresses within fabricated components, which may result in deformation and defects. To mitigate these challenges, a three-dimensional thermo-mechanical coupled finite element model of AlSi10Mg produced via SLM was developed to examine the influence of various scanning strategies on the temperature distribution and stress evolution during multi-layer, multi-pass fabrication. Four representative scanning strategies—grid reciprocating, interlayer reversal, block, and annular—were systematically compared under consistent processing parameters. The model incorporated temperature-dependent material properties and utilized a Gaussian heat source to simulate melting and solidification phenomena. Findings reveal that the choice of scanning strategy markedly impacts heat accumulation and stress distribution patterns. Specifically, block scanning effectively diminishes heat buildup and promotes more uniform stress distribution, whereas interlayer reversal and annular scanning tend to intensify stress concentrations. These insights offer valuable guidance for optimizing scanning protocols and managing residual stresses in SLM-fabricated components.

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling.

The manufacture of damping alloy parts with stable damping properties and high mechanical performances in the selective laser melting (SLM) process is influenced by temperature evolution and residual stress distribution. Choosing an appropriate scanning strategy, namely the specific trajectory along which the laser head scans powders within given area, is crucial, but clearly defined criteria for scanning strategy design are lacking. In this study, a three-dimensional finite element model (FEM) of the SLM process for manufacturing a WE43 alloy component was established and validated against the published experimental data. Eleven different scanning strategies were designed and simulated, considering variables such as scanning track length, direction, Out–In or In–Out strategy, start point, and interlayer variation. The results showed that scanning strategy, geometry, and layer number collectively affect temperature, melt pool, and stress outputs. For instance, starting scanning at a colder part of the powder layer could lead to a high peak temperature and low melt pool depth. A higher layer number generally results in lower cooling rate, a lower temperature gradient, a longer melt pool life, and larger melt pool dimensions. Changing the start point between scanning circulations helps mitigate detrimental residual stress. This work highlights the potential of analyzing various scanning strategy-related variables, which contributes to reducing trial-and-error tests and selecting optimal scanning strategies under different product quality requirements. This article can assist in the design of appropriate scanning strategies to prevent defects such as element loss due to evaporation, poor bonding, and deformation or cracking from high residual stress. Additionally, identifying stress concentration locations and understanding the effects of geometry and layer number on thermal and mechanical behaviors can assist in geometry design.

Additive manufacturing of conductive layers on a dielectric substrate has garnered significant interest due to its promise to produce printed electronics efficiently and its capability to print on curved substrates. A considerable challenge encountered is the conductive layer’s potential peeling due to inadequate adhesion with the dielectric substrate, which compromises the durability and functionality of the electronics. This study strives to facilitate the binding force through dielectric substrate surface modification using concentrated sulfuric acid and ultraviolet (UV) laser treatment. First, polyetheretherketone (PEEK) and nanoparticle silver ink were employed as the studied material. Second, the surface treatment of PEEK substrates was conducted across six levels of sulfuric acid exposure time and eight levels of UV laser scanning velocity. Then, responses such as surface morphology, roughness, elemental composition, chemical bonding characteristics, water contact angle, and surface free energy (SFE) were assessed to understand the effects of these treatments. Finally, the nanoparticle silver ink layer was deposited on the PEEK surface, and the adhesion force measured using a pull-off adhesion tester. Results unveiled a binding force of 0.37 MPa on unmodified surface, which escalated to 1.99 MPa with sulfuric acid treatment and 2.21 MPa with UV laser treatment. Additionally, cross-approach treatment investigations revealed that application sequence significantly impacts results, increasing binding force to 2.77 MPa. The analysis further delves into the influence mechanism of the surface modification on the binding force, elucidating that UV laser and sulfuric acid surface treatment methods hold substantial promise for enhancing the binding force between heterogeneous materials in the additive manufacturing of electronics.

Aiming at the problems of densely distributed parts, long detection time and low path efficiency of traditional path planning RRT algorithm (Rapidly Exploring Random Tree) in detecting the bottom of railway freight car, an improved RRT path planning method is proposed to improve detection efficiency. Firstly, the end of the manipulator is kept a certain distance from the target to be detected by improving the random point extension time choice strategy, so as to ensure the safety of the detection process so as to make the detection processes safer; Then, on the basis of adding target bias, the path search method at the starting and ending points of the random tree was optimized, so that the path search process was concentrated in the center of the path; Finally, redundant nodes are removed from the searched path to shorten the planned path. Simulations were conducted on MATLAB and VREP platforms, and the results showed that the improved RRT path planning algorithm was more helpful than traditional RRT algorithms and could effectively complete detection tasks.

Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.