Advances in Manufacturing最新文献

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding (RSW) by leveraging machine learning and transfer learning methods. Initially, low-fidelity (LF) data were obtained through finite element numerical simulation and design of experiments (DOEs) to train the LF machine learning model. Subsequently, high-fidelity (HF) data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques. The accuracy and generalization performance of the models were thoroughly validated. The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials, and provide an effective and valuable method for predicting critical process parameters in RSW.

The stress-life curve (S–N) and low-cycle strain-life curve (E–N) are the two primary representations used to characterize the fatigue behavior of a material. These material fatigue curves are essential for structural fatigue analysis. However, conducting material fatigue tests is expensive and time-intensive. To address the challenge of data limitations on ferrous metal materials, we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous materials. In addition, a data-augmentation framework is introduced using a conditional generative adversarial network (cGAN) to overcome data deficiencies. By incorporating the cGAN-generated data, the accuracy (R²) of the Random Forest Algorithm-trained model is improved by 0.3–0.6. It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

Laser welding is an efficient and precise joining method widely used in various industries. Real-time monitoring of the welding process is important for improving the quality of the weld products. This study provides an overview of the optical diagnostics of the laser welding process. The common welding defects and their formation mechanisms are described, starting with an introduction to the principles of laser welding. Optical signal sources are divided into radiated and external active lights, and different monitoring systems are summarized and classified. Also, the applications of artificial intelligence techniques in data processing, weld defect prediction and classification, and adaptive welding control are summarized. Finally, future research and challenges in real-time laser welding monitoring technology based on optical diagnostics are discussed. This study demonstrated that optical diagnostic techniques could acquire substantial information about the laser welding process and help identify welding defects.

The exoskeleton is employed to assist humans in various domains including military missions, rehabilitation, industrial operation, and activities of daily living (ADLs).Walking is a fundamental ADL, and exoskeletons are capable of reducing the activation and metabolism of lower extremity muscles through active assistance during walking. To improve the performance of active assistance strategy, this article proposes a framework using an active hip exoskeleton. Subsequently, it correlates to an already established Bayesian-based human gait recognition algorithm, with a particular focus on linear and circular walking within industrial and ADL contexts. In theorizing this strategy for exoskeletons, this study further reveals, in part, the activation characteristics of human hip muscles for the instruction and regulation of active assistance duration and onset timing. This proposed active assistance strategy provides new insights for enhancing the performance of assistive robots and facilitating human robot interaction within the context of ADLs.

Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets. Accurate roping prediction and rating are essential for industrial applications. Recently, the authors introduced an artificial neural network (ANN) model to efficiently forecast roping behavior across the thickness of large regions with texture gradients. In this study, the previously proposed ANN model for roping prediction is briefly reviewed, and a few-shot learning (FSL)-based method is developed for roping grading with limited samples. To consider the directionality of the roping patterns, the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization. A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm. A new component-focused representation is also implemented for data-processing, exploiting the close correlation between roping and power distribution in the frequency domain. The ultimate FSL method achieved an optimal accuracy of 95.65% in roping classification with only five training samples per class, outperforming four typical FSL methods. This FSL approach can be applied to grade the roping morphologies predicted by the ANN model. Consequently, the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.

Wax pattern fabrication in the investment casting of hollow turbine blades directly determines the dimension accuracy of subsequent casting, and therefore significantly affects the quality of final product. In this work, we develop a machine learning-based multi-objective optimization framework for improving dimension accuracy of wax pattern by optimizing its process parameters. We consider two optimization objectives on the dimension of wax pattern, i.e., the surface warpage and core offset. An active learning of Bayesian optimization is employed in data sampling to determine process parameters, and a validated numerical model of injection molding is used to compute objective results of dimension under varied process parameters. The collected dataset is then leveraged to train different machine learning models, and it turns out that the Gaussian process regression model performs best in prediction accuracy, which is then used as the surrogate model in the optimization framework. A genetic algorithm is employed to produce a non-dominated Pareto front using the surrogate model in searching, followed by an entropy weight method to select the most optimal solution from the Pareto front. The optimized set of process parameters is then compared to empirical parameters obtained from previous trial-and-error experiments, and it turns out that the maximum and average warpage results of the optimized solution decrease 26.0% and 20.2%, and the maximum and average errors of wall thickness compared to standard part decrease from 0.22 mm and 0.051 7 mm using empirical parameters to 0.10 mm and 0.035 6 mm using optimized parameters, respectively. This framework is demonstrated capable of addressing the challenge of dimension control arising in the wax pattern production, and it can be reliably deployed in varied types of turbine blades to significantly reduce the manufacturing cost of turbine blades.

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.