Advances in Manufacturing最新文献

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.

High-pressure die casting (HPDC) is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation. However, the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation. Therefore, a methodology for property prediction must be developed. Material characterization, simulation technologies, and artificial intelligence (AI) algorithms were employed. Firstly, an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy. Moreover, a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results. Additionally, the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model, allowing accurate prediction of the property distribution of the HPDC Al-Si alloy. The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.

Smart structures realize sequential motion and self-assembly through external stimuli. With the advancement of four-dimensional (4D) printing, the programming of sequential motions of smart structures is endowed with more design and manufacturing possibilities. In this research, we present a method for physically programming the timescale of shape change in 4D-printed bilayer actuators to enable the sequential motion and self-assembly of smart structures. The effects of the geometric and printing parameters on the time-dependent behavior of 4D-printed bilayer actuators are investigated. The results show that the thickness of the active layer directly affects the timescale of motion, and increasing the thickness leads to faster motion until the thickness ratio is close to 4:6. Similarly, a higher printing speed results in faster motion. Conversely, a higher printing temperature and a greater layer height result in a slower shape change. The effects of the length-width ratio, line width, and filling ratio on the timescale of motion are not as straightforward. Finally, we demonstrate several smart structures that exhibit sequential motion, including a labyrinth-like self-folding structure that is choreographed to achieve multi-step self-shaping and a flower-shaped structure where each part completes its movement sequentially to avoid collisions. The presented method extends the programmability and functional capabilities of 4D printing.

This paper presents the design and fabrication of an aluminium oxide cutting insert with an internal cooling channel formed through an additive manufacturing method. The formed insert is subjected to a controlled densification process and analysed through a series of characterisation investigations. The purpose of the study is to develop the design concept and analyse the forming and sintering parameters used in the lithographic ceramic manufacturing process. The results validated the feasibility of the geometrical design, providing the required structural conformity with the integrated internal feature using conditional specifications. It is confirmed that the forming parameters would affect the material properties of the green body. Furthermore, the results indicate that the heating rate and temperature variance of the de-binding and thermal treatment regime influences the microstructural growth kinetics and the quality of the densified insert. Using a novel application of liquid gallium as an internal coolant, experimental results showed a decrease in tool wear difference of 36% at (V_{{text{c}}}) = 250 m/min, and 31% in tool wear difference at (V_{{text{c}}}) = 900 m/min between cooling and non-cooling conditions. When external cooling was applied, the results showed at (V_{{text{c}}}) = 250 m/min, the difference between the tool wear rates with the internal coolant relative to the external coolant was 29%. Increasing to (V_{{text{c}}}) = 900 m/min, the results revealed a 16% tool wear difference. The results clearly indicate the potential of liquid gallium as a heat transfer agent in internal cooling applications for cutting inserts, and by extension demonstrable reduction in tool wear.