Effect of the Processing Parameters on the Porosity and Mechanical Behavior of Titanium Samples with Bimodal Microstructure Produced via Hot Pressing

In-situ particle analysis with heterogeneous background: a machine learning approach

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle-substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

Mechanical performance of porous biomimetic intervertebral body fusion devices: an in vitro biomechanical study

BACKGROUND

Degenerative disc disease is one of the most common ailments severely affecting the quality of life in elderly population. Cervical intervertebral body fusion devices are utilized to provide stability after surgical intervention for cervical pathology. In this study, we design a biomimetic porous spinal cage, and perform mechanical simulations to study its performances following American Society for Testing and Materials International (ASTM) standards before manufacturing to improve design process and decrease cost and consumption of material.

METHODS

The biomimetic porous Ti-6Al-4 V interbody fusion devices were manufactured by selective laser melting (laser powder bed fusion: LPBF in ISO/ASTM 52900 standard) and subsequently post-processed by using hot isostatic pressing (HIP). Chemical composition, microstructure and the surface morphology were studied. Finite element analysis and in vitro biomechanical test were performed.

FINDINGS

The post heat treatment can optimize its mechanical properties, as the stiffness of the cage decreases to reduce the stress shielding effect between two instrumented bodies. After the HIP treatment, the ductility and the fatigue performance are substantially improved. The use of HIP post-processing can be a necessity to improve the physical properties of customized additive manufacturing processed implants.

INTERPRETATION

In conclusion, we have successfully designed a biomimetic porous intervertebral device. HIP post-treatment can improve the bulk material properties, optimize the device with reduced stiffness, decreased stress shielding effect, while still provide appropriate space for bone growth.

CLINICAL SIGNIFICANCE

The biomechanical performance of 3-D printed biomimetic porous intervertebral device can be optimized. The ductility and the fatigue performance were substantially improved, the simultaneously decreased stiffness reduces the stress shielding effect between two instrumented bodies; while the biomimetic porous structures provide appropriate space for bone growth, which is important in the patients with osteoporosis.