Machine learning is reshaping gel-based additive manufacturing by enabling accelerated material design and predictive process optimization. This review provides a comprehensive overview of recent progress in applying machine learning across gel formulation development, printability prediction, and real-time process control. The integration of algorithms such as neural networks, random forests, and support vector machines allows accurate modeling of gel properties, including rheology, elasticity, swelling, and viscoelasticity, from compositional and processing data. Advances in data-driven formulation and closed-loop robotics are moving gel printing from trial and error toward autonomous and efficient material discovery. Despite these advances, challenges remain regarding data sparsity, model robustness, and integration with commercial printing systems. The review results highlight the value of open-source datasets, standardized protocols, and robust validation practices to ensure reproducibility and reliability in both research and clinical environments. Looking ahead, combining multimodal sensing, generative design, and automated experimentation will further accelerate discoveries and enable new possibilities in tissue engineering, biomedical devices, soft robotics, and sustainable materials manufacturing.

This study developed a new metallography–property relationship neural network (MPR-Net) to predict the relationship between the microstructure and mechanical properties of 316L stainless steel built by laser powder bed fusion (LPBF). The accuracy R² of MPR-Net was 0.96 and 0.91 for tensile strength and Vickers hardness predictions, respectively, based on optical metallurgy images. Feature visualisation methods, such as gradient-weighted class activation mapping (Grad-CAM) and clustering, were employed to interpret the abstract features within the MPR-Net, providing insights into the molten pool morphology and grain formation mechanisms during the LPBF process. Experimental results showed that the optimal process parameters—190 W laser power and 700 mm/s scanning speed—yielded a maximum tensile strength of 762.83 MPa and a Vickers hardness of 253.07 HV_0.2 with nearly full densification (99.97%). The study marks the first application of a convolutional neural network (MPR-Net) to predict the mechanical properties of 316L stainless steel samples manufactured through laser powder bed fusion (LPBF) based on metallography. It innovatively employs techniques such as gradient-weighted class activation mapping (Grad-CAM), spatial coherence testing, and clustering to provide deeper insights into the workings of the machine learning model, enhancing the interpretability of complex neural network decisions in material science.

Hydrogel additive manufacturing underpins soft tissue models, biointerfaces, and soft robotics. The coupled choices of formulation, rheology, and process conditions limit the progress. This review maps how artificial intelligence links composition to printability across direct ink writing, inkjet, vat photopolymerization, and laser-induced forward transfer, and how vision-guided control improves fidelity and viability during printing. Interpretable predictors connect routine rheology to strand stability, data-driven classifiers chart droplet regimes, and optical dose models with learning enhance voxel accuracy. Polymer informatics, including BigSMILES based representations, supports generative screening of precursors and crosslinkers. Bayesian optimization and active learning reduce experimental burden while honoring biological constraints, and emerging autonomous platforms integrate in situ sensing with rapid iteration. A strategic framework outlines a technological progression from current open-loop data gathering toward real-time closed-loop correction and ultimately predictive fault prevention through digital twins. The synthesis provides quantitative routes from formulation through process to function, establishing a practical foundation for predictive, reproducible hydrogel manufacturing and application-oriented design.

Laser powder bed fusion (LPBF) is an advanced additive manufacturing technology that is gaining increasing interest for biomedical implants because it can produce dense, patient-specific metallic components with controlled microstructures. This study investigated the LPBF fabrication of 316L stainless steel, which is widely used in orthopedic and dental implants, and examined the effects of laser power and scanning speed on the microstructure and mechanical properties relevant to biomedical applications. The study achieved 99.97% density and refined columnar and cellular austenitic grains, with optimized molten pool morphology. The optimal LPBF parameters, 190 W laser power and 700 mm/s, produced a tensile strength of 762.83 MPa and hardness of 253.07 HV_0.2, which exceeded the values of conventional cast 316L stainless steel. These results demonstrated the potential of optimized LPBF 316L stainless steel for functional biomedical applications that require high mechanical integrity and biocompatibility.

Although laser drilling of carbon fibre-reinforced polymer (CFRP) composites offers the advantages of zero tool-wear and avoidance of fibre delamination compared with mechanical drilling, it consumes considerably more energy during the drilling process. This research shows that by using a new, stepped parameter parallel ring laser hole drilling method, an energy saving of 78.10% and an 18.37 gCO₂ reduction for each hole, while improving productivity by more than 300%, can be achieved in laser drilling of 6 mm diameter holes in CFRP sheets of 2 mm in thickness, compared with previous laser drilling methods under the same drilling quality. The key reason for this is an increase in energy input to the inner rings enabling more rapid removal of the material, while the lower energy input for the outer ring provides a shielding trench to reduce the heat loss into the parent material. The results are compared with single-ring laser drilling and multiple-ring laser drilling with constant processing parameters, and a discussion is given on comparing with mechanical drilling and future prospects, including a combined mechanical drilling and laser pre-scribing process.

This paper proposes a comprehensive deep-learning framework, SentiStack, for Bitcoin price forecasting and trading strategy evaluation by integrating multimodal data sources, including market indicators, macroeconomic variables, and sentiment information extracted from financial news and social media. The model architecture is based on a Stacking-LSTM ensemble, which captures complex temporal dependencies and non-linear patterns in high-dimensional financial time series. To enhance predictive power, sentiment embeddings derived from full-text analysis using the DeepSeek language model are fused with traditional numerical features through early and late data fusion techniques. Empirical results demonstrate that the proposed model significantly outperforms baseline strategies, including Buy & Hold and Random Trading, in cumulative return and risk-adjusted performances. Feature ablation experiments further reveal the critical role of sentiment and macroeconomic inputs in improving forecasting accuracy. The sentiment-enhanced model also exhibits strong performance in identifying high-return market movements, suggesting its practical value for data-driven investment decision-making. Overall, this study highlights the importance of incorporating soft information, such as investor sentiment, alongside traditional quantitative features in financial forecasting models.