Advances in Manufacturing最新文献

As an essential link in ultra-precision machining technology, various new surface polishing technologies and processes have always attracted continuous in-depth research and exploration by researchers. As a new research direction of ultra-precision machining technology, magnetorheological polishing technology has become an important part. The polishing materials and magnetorheological fluids involved in the process of magnetorheological polishing are reviewed. The polishing principle, equipment development, theoretical research and process research of magnetorheological polishing technologies, such as the wheel-type, cluster-type, ball-type, disc-type and other types, derived from the magnetorheological polishing process, are reviewed. The above magnetorheological polishing technologies are analyzed and compared from the perspective of processing accuracy, processing efficiency and application range. The curvature adaptive magnetorheological polishing technology with a circulatory system is proposed to achieve high efficiency and high-quality polishing.

Precipitation is a common phenomenon that occurs during heat treatments. There is internal stress around the precipitate owing to the lattice misfit between the precipitate and matrix. This internal stress has a significant influence not only on the precipitation kinetics but also on the material properties. The misfit stress can be obtained by numerically solving the mechanical equilibrium equations. However, this process is complex and time-consuming. We developed a new approach based on deep learning to accelerate the solution process. The training data were first generated by a phase-field model coupled with elastic mechanical equilibrium equations, which were solved using the finite difference method. The obtained precipitate morphologies and corresponding stress distributions were input data for training the physics-informed (PI) UNet model. The well-trained PI-UNet model can then be applied to predicting stress distributions with the precipitate morphology as the input. Prediction accuracy and efficiency are discussed in this study. The results showed that the PI-UNet model was an appropriate approach for quickly predicting the misfit stress between the precipitate and matrix.

Fiber laser micromachining is found extensive applications at industrial level because it is cheap and simple to use. Due to its high strength and low conductivity titanium is difficult to machine with conventional methods. In this investigation, micro holes were fabricated using a 30 W fiber laser on 2 mm thick α-titanium (Grade 2) and the process parameters were optimized through response surface methodology (RSM) and teaching learning-based optimization (TLBO) approach. Experimental runs were designed as per rotatable central composite design (RCCD). Material removal rate (MRR), hole circularity (HC), deviation in diameter (DEV) and heat affected zone (HAZ) were selected as output. A third-order polynomial prediction model was established using RSM. Analysis of variance (ANOVA) suggested that the developed model was 93.5% accurate. The impact of input factors on responses were studied by 3D surface plots. RSM desirability indicates that optimum micro drilling conditions are scan speed 275.43 mm/s, frequency 24.61 kHz, power 36.23% and number of passes 49.75. TLBO indicates that optimum micro drilling conditions are scan speed 100 mm/s, frequency 20 kHz, power 20% and number of passes 50. Comparison between RSM and TLBO suggested that TLBO provided better optimization results. Surface morphology of the fabricated micro holes were analyzed with scanning electron microscopy (SEM).

Al-Si alloys manufactured via high-pressure die casting (HPDC) are suitable for a wide range of applications. However, the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties, thus leading to a complicated microstructure-property relationship that is difficult to capture. Hence, a computational framework incorporating machine learning and crystal plasticity method is proposed. This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure. Firstly, we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information. Subsequently, based on 160 samples obtained via the Latin hypercube sampling method, representative volume elements are constructed, and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties. Next, the yield strength, elastic modulus, strength coefficient, and strain-hardening exponent are used to characterize the stress-strain curve, and Gaussian process regression models and microstructural variables are developed. Finally, sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy. The results show that the Gaussian process regression models exhibit high accuracy (R² greater than 0.84), thus confirming the viability of the proposed method. The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties. Furthermore, the proposed framework can not only be transferred to other alloys but also be employed for material design.

To provide a broad processing window with a high deposition rate, a comprehensive analysis of single-track geometrical characteristics over a wide range of laser energies and mass inputs in laser cladding is necessary. The formation of a single cladding track of Inconel 718 on a substrate by coaxial laser cladding, with a wide range of laser power from 1 200 W to 3 900 W and a powder feeding rate from 5 g/min to 35 g/min, was studied from both theoretical and experimental points of view. A quantitative model of powder concentration distribution was developed based on the powder transport morphology obtained by high-speed photography. Linear regression models were established between nine geometrical characteristics and the combined process parameters of laser power and powder feeding rate, written as P^αF^β, to quantitatively analyze the geometrical characteristics of the clad. These were confirmed by large correlation coefficients and analysis of residuals. From the findings we deduced that more energy input enhanced the outward direction of Marangoni convection, leading to the melt pool undergoing evolution from shallow dilution and flat dilution to fluctuating dilution. An almost linear relationship was found between the cladding width, W, and the laser power, indicating that laser energy accumulation was a major factor in the evolution of W. The increase ratio of the cladding height, h_c, ranged from 640% to 360% along with an increase in the powder feeding rate, implying that the evolution of h_c, was dominated by the powder feeding rate. The total area of the cross-section, A; the area of the clad, A_c; the area of the molten substrate, A_m; the total height of the cross-section, H; the penetration depth, h_m; the dilution ratio, D; and the wetting angle, θ, were determined by a complex coupling of energy input and mass accumulation, and they are proportional to P^0.5F^0.2, P^0.2F^0.5, P^0.5/F^0.2, P^0.3F, P^0.5/F^0.2, P^0.2/F^0.2, and P^0.2/F^0.2, respectively. This research aims to provide general knowledge on the influence of energy input and mass addition on the geometrical characteristics of the clad and its related influence mechanism. Such information could provide a reference and basis for promoting the practical application of laser cladding technology.

In the context of increasingly prominent product personalization and customization trends, intelligent manufacturing-oriented turnkey projects can provide manufacturers with fast and convenient turnkey services for manufacturing systems. Their key characteristic is the transformation of the traditional design process into a configuration process. However, the scope of configuration resources in existing research is limited; the cost and time required for manufacturing system construction are overlooked; and the integration of the system layout configuration is rarely considered, making it difficult to meet the manufacturing system configuration requirements of turnkey projects. In response, this study establishes a multi-factor integrated rapid configuration model and proposes a solution method for manufacturing systems based on the requirements of turnkey projects. The configuration model considers the system construction cost and duration and the product manufacturing cost and duration, as optimization objectives. The differences in product feature-dividing schemes and configuration of processes, equipment, tools, fixtures, and layouts were considered simultaneously. The proposed model-solving method is a three-layer hybrid optimization algorithm framework with two optimization algorithm modules and an intermediate algorithm module. Four hybrid configuration algorithms are established based on non-dominated sorting genetic algorithm-III (NSGAIII), non-dominated sorting genetic algorithm-II (NSGAII), multi-objective simulated annealing (MOSA), multi-objective neighborhood search (MONS), and tabu search (TS). These algorithms are compared and validated through a hydraulic valve block production case, and the TS and NSGAIII (TS-NSGAIII) hybrid algorithm exhibits the best performance. This case demonstrates the effectiveness of the proposed model and solution method.

In multistage machining processes (MMPs), a clear understanding of the error accumulation, propagation, and evolution mechanisms between different processes is crucial for improving the quality of machining products and achieving effective product quality control. This paper proposes the construction of a machining error propagation event-knowledge graph (MEPEKG) for quality control in MMPs, inspired by the application of knowledge graphs to data, information, and knowledge organization and utilization. Initially, a cyber-physical system (CPS)-based production process data acquisition sensor network is constructed, and process flow-oriented process monitoring is achieved through the radio frequency identification (RFID) production event model. Secondly, the process-related quality feature and working condition data are preprocessed; features are extracted from the distributed CPS nodes; and the production event model is used to achieve the dynamic mapping and updating of feature data under the guidance of the MEPEKG schema layer. Moreover, the mathematical model of machining error propagation based on the second-order Taylor expansion is used to quantitatively analyze the quality control in MMPs based on the support of MEPEKG data. Finally, the efficacy and reliability of the MEPEKG for error propagation analysis and quality control of MMPs were verified using a case study of a specially shaped rotary component.

Electrical discharge-induced ablation machining utilizes the significant chemical energy released by the combustion of oxygen with metals to remove materials, thereby greatly enhancing the material removal rate (MRR). However, in the case of discharge ablation machining of silicon carbide particle-reinforced aluminum matrix composites (SiCp/Al), the effect of oxygen can easily result in the formation of poorly conductive oxides, which in turn affect the machining stability and adversely impact the MRR and quality of the machining surface. To address this problem, this study proposes the use of sodium carbonate (Na₂CO₃) solution as the atomization medium to chemically dissolve the oxide during processing to achieve the effect of atomized discharge ablation-chemical composite processing. The study found that the Na₂CO₃ solution facilitated high-temperature chemical etching behavior in the SiCp/Al atomized discharge ablation process. The Na₂CO₃ solution reacted chemically with and etched away the recalcitrant oxide that formed in the SiCp/Al process area during machining, thereby ensuring efficient and continuous electrical discharge ablation machining. We applied the atomized discharge ablation-chemical composite machining method to mill SiCp/Al. The experimental results showed that the MRR was 2.66 times higher than that of electrical discharge machining (EDM) and 1.98 times higher than that of conventional atomized discharge ablation milling. Moreover, the relative electrode wear ratio was reduced by 76.01% compared with that of EDM and 82.30% compared with that of conventional atomized discharge ablation machining.