This study investigates the complex relationships between process parameters and material properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We examine the effects of key parameters, including biochar content (BC), layer thickness (LT), raster angle (RA), infill pattern (IP), and infill density (ID), on the tensile, flexural, and impact strengths of FDM-printed pure PLA and biochar-reinforced PLA composites. Mechanical testing was used to measure the ultimate tensile strength (UTS), flexural strength (FS), and impact strength (IS) of the 3D-printed samples. The extreme gradient boosting (XGB) algorithm was used to build a predictive model based on the data collected from mechanical testing. Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) techniques were implemented to understand the effects of the interactions of key parameters on mechanical properties such as UTS, FS, and IS. Prediction by XGB was accurate for UTS, FS, and IS, with R-squared values of 0.96, 0.95, and 0.85, respectively. The explanation showed that infill density has the most significant influence on UTS and FS, with SHAP values of +2.75 and +5.8, respectively. BC has the most significant influence on IS, with a SHAP value of +2.69. PDP reveals that using 0.3 mm LT and 30° RA enhances mechanical properties. This study contributes to the field of the application of artificial intelligence in additive manufacturing. A novel approach is presented in which machine learning and XAI techniques such as SHAP, LIME, and PDP are combined and used not only for optimization but also to provide valuable insights about the interaction of the process parameters with mechanical properties.

Wear is induced when two surfaces are in relative motion. The wear phenomenon is mostly data-driven and affected by various parameters such as load, sliding velocity, sliding distance, interface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles. The machine learning (ML) approach has not only made it possible to establish the relation between the operating parameters and wear but also helps in predicting the behavior of the material in polymer tribological applications. In this study, an attempt is made to apply different machine learning algorithms to the experimental data for the prediction of the specific wear rate of glass-filled PTFE (Polytetrafluoroethylene) composite. Orthogonal array L25 is used for experimentation for evaluating the specific wear rate of glass-filled PTFE with variations in the operating parameters such as applied load, sliding velocity, and sliding distance. The experimental data are analysed using ML algorithms such as linear regression (LR), gradient boosting (GB), and random forest (RF). The R² value is obtained as 0.91, 0.97, and 0.94 for LR, GB, and RF, respectively. The R² value of the GB model is the highest among the models, close to 1.0, indicating an almost perfect fit on the experimental data. Pearson’s correlation analysis reveals that load and sliding distance have a considerable impact on specific wear rate as compared to sliding velocity.

Humans’ basic needs include food, shelter, and clothing. Cotton is the foundation of the textile industry. It is also one of the most profitable non-food crops for farmers around the world. Different diseases have a significant impact on cotton yield. Cotton plant leaves are adversely affected by aphids, army worms, bacterial blight, powdery mildew, and target spots. This paper proposes an encoder decoder model for generating captions in English and Marathi language to describe health of cotton plant from aerial images. The cotton disease captions dataset (CDCD) was developed to assess the effectiveness of the proposed approach. Experiments were conducted using various convolutional neural network (CNN) models, such as VGG-19, InceptionResNetV2, and EfficientNetV2L. The quality of generated caption is evaluated on BiLingual evaluation understudy (BLEU) metrics and using subjective criteria. The results obtained for captions generated in English and Marathi language are comparable. The network combination of EfficientNetV2L and long short-term memory (LSTM) has outperformed the other combinations.

Solar components are prone to breakdowns and deterioration from lighting impacts. The article investigates the electronics deterioration brought on by jet potential in a sipv photovoltaic module. Ordinary discharge surge energies with pin 3 were applied to the Photovoltaic cell. These thunderbolt surge voltage tests' highest voltage. There were carried out two sets of trials. Test subgroup one samples have been subjected to thunderbolt shock voltage measurements. They showed their Contemporary properties. A Solar panel in test groups of two underwent thermal decomposition until air pockets started to emerge in its intermediate layer. Then, this instrument was put through the same test procedure as test group one's thunderbolt impulse current test. Testing groups one and two are contrasted. The instrument breaks down as a result of the sample being severely degraded by pockets brought on by the thunderbolt applied voltages.

Physically examining each plant to determine its state of health and determining the disease if plant is affected due to it, is challenging. The encoder - decoder approach is proposed for describing health of cauliflower plant in English, Hindi and Marathi languages from aerial images. Experiments are performed with different CNN models and LSTM combinations. The Multilanguage Cauliflower Captions Dataset (MCCD) is developed to evaluate the performance of the model. The dataset contains 1213 images where each image is described in 3 different languages. The dataset contains images of cauliflower plant affected due to bacterial spot rot, black rot and downy mildew diseases. It also contains images of healthy plant. The objective metrics such as BLEU scores and subjective criteria are used to decide the quality of the generated description.

In the face of escalating urbanization, air quality is an integral concern, critically influencing both human health and environmental well-being. As urban area In the face of escalating urbanization, air quality is an integral concern, critically influencing both human health and environmental well-being. As urban area