Non-destructive measurements of high aspect ratio microscale features, especially those with internal geometries such as micro-holes, remain a challenging metrology problem that is increasing in difficulty due to the increasing requirement for more complexity and higher tolerances in such structures. Additionally, there is a growing use of functional surface texturing for improving characteristics such as heat transfer and wettability. As a result, measurement techniques capable of providing dimensional form and surface finish for these features are of intense interest. This review explores the state-of-the-art inspection methodologies compatible with high-aspect-ratio structures and their suitability for extracting three-dimensional surface data based on identified high-aspect ratio structure types. Here, the abilities, limitations, challenges, and future requirements for the practical implementation and acceptance of these measurement techniques are presented.

The growing use of composites in automotive and aerospace fields highlights the need for effective joining of dissimilar materials. Adhesive bonding offers significant advantages over traditional methods. Therefore, comprehensively exploring the relationship between multiple design variables and joint strength, and subsequently achieving accurate prediction of joint strength based on this understanding, is essential for enhancing the effectiveness and efficiency of adhesive joint structural optimization. However, the joint—the critical yet weakest part—has strength governed by complex structural variables that are not fully understood, limiting optimization potential. Based on the effectiveness of finite element simulation in tensile fracture mechanics, this study developed a deep neural network (DNN). Combining the DNN model with a genetic algorithm (GA), both single-objective and multi-objective optimization were conducted. The single-objective optimization focused solely on maximizing joint strength, while the multi-objective GA further quantified the Pareto optimal trade-offs between joint strength and bond area, identifying compromise solutions. The effectiveness of the optimized parameters was validated, demonstrating higher efficiency and accuracy compared to traditional optimization methods such as response surface methodology (RSM). This integrated approach provides a robust framework for predicting joint strength and achieving effective optimization of bonded structures.

Human hands have the unique ability to classify material properties, such as hardness, using mechanoreceptors and tactile information. Previous studies have demonstrated hardness classification using Commercial Off-The-Shelf (COTS) sensors but lacked robotic integration considerations. This study explores the integration of multiple COTS sensors, inspired by mechanoreceptors, for classifying material hardness. The sensors were used to classify objects into three categories—hard, soft, and flexible—based on the qualitative Shore hardness scale. The aim was to identify the optimal sensor topology configuration that delivers high accuracy, using machine learning algorithms provided in the literature. The results suggest that the Random Forest Classifier is the most suitable algorithm, showcasing accuracies ranging from 90% to 98.7%, across various sensor topologies. The ‘PFV’ topology, comprising a potentiometer (P), force sensor (F), and vibration sensor (V), achieved the highest accuracy of 98.7%, while the ‘FPV’ and ‘FVP’ recorded accuracies between 96% and 97.5%. The topology of FPV and FVP have the most closely related configuration to that of mechanoreceptors; however, the results show that PFV outperforms this configuration. While the PFV topology marginally outperforms the mechanoreceptor-inspired configurations, the results demonstrate that bio-inspired sensor arrangements provide a robust solution for hardness classification in robotics. The PFV topology performs better than FPV in terms of prediction speed, with an average prediction time of 8.31 ms (millisecond) for PFV versus 13.93 ms for FPV. PFV and FPV achieved 12 and 13 correct predictions, respectively, out of 18 objects. The faster prediction times of PFV make it particularly advantageous for applications requiring quick and accurate decision-making for robotic applications.