In this paper, the authors present a physical model developed to simulate accurate external ventricular drain (EVD) placement with realistic haptic and visual feedbacks to serve as a platform for complete procedural training. Insertion of an EVD via ventriculostomy is a common neurosurgical procedure used to monitor intracranial pressures and/or drain CSF. Currently, realistic training tools are scarce and mainly limited to virtual reality simulation systems. The use of 3D printing technology enables the development of realistic anatomical structures and customized design for physical simulators. In this study, the authors used the advantages of 3D printing to directly build the model geometry from stealth head CT scans and build a phantom brain mold based on 3D scans of a plastinated human brain. The resultant simulator provides realistic haptic feedback during a procedure, with visualization of catheter trajectory and fluid drainage. A multiinstitutional survey was also used to prove content validity of the simulator. With minor refinement, this simulator is expected to be a cost-effective tool for training neurosurgical residents in EVD placement.

This paper presents a comprehensive survey on the modeling of tissue cutting, including both soft tissue and bone cutting processes. In order to achieve higher accuracy in tissue cutting, as a critical process in surgical operations, the meticulous modeling of such processes is important in particular for surgical tool development and analysis. This review paper is focused on the mechanical concepts and modeling techniques utilized to simulate tissue cutting such as cutting forces and chip morphology. These models are presented in two major categories, namely soft tissue cutting and bone cutting. Fracture toughness is commonly used to describe tissue cutting while Johnson-Cook material model is often adopted for bone cutting in conjunction with finite element analysis (FEA). In each section, the most recent mathematical and computational models are summarized. The differences and similarities among these models, challenges, novel techniques, and recommendations for future work are discussed along with each section. This review is aimed to provide a broad and in-depth vision of the methods suitable for tissue and bone cutting simulations.

Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique structural properties. These materials are machined to create components that meet the dimensional and surface finish tolerance specifications for various industrial applications. The heterogeneous structure of these materials—resulting from different fiber orientations and their complex multiscale structure—introduces a distinct set of material removal mechanisms that inherently vary over time. This structure has an adverse effect on the surface integrity of machined NFRPs. Therefore, a real-time monitoring approach is desirable for timely intervention for quality assurance. Acoustic emission (AE) sensors that capture the elastic waves generated from the plastic deformation and fracture mechanisms have potential to characterize these abrupt variations in the material removal mechanisms. However, the relationship connecting AE waveform patterns with these NFRP material removal mechanisms is not currently understood. This paper reports an experimental investigation into how the time–frequency patterns of AE signals connote the various cutting mechanisms under different cutting speeds and fiber orientations. Extensive orthogonal cutting experiments on unidirectional flax fiber NFRP samples with various fiber orientations were conducted. The experimental setup was instrumented with a multisensor data acquisition system for synchronous collection of AE and vibration signals during NFRP cutting. A random forest machine learning approach was employed to quantitatively relate the AE energy over specific frequency bands to machining conditions and hence the process microdynamics, specifically, the phenomena of fiber fracture and debonding that are peculiar to NFRP machining. Results from this experimental study suggest that the AE energy over these frequency bands can correctly predict the cutting conditions to ∼95% accuracies, as well as the underlying material removal regimes.

Silicone-based tissue-mimicking phantom is widely used as a surrogate of tissue for clinical simulators, allowing clinicians to practice medical procedures and researchers to study the performance of medical devices. This study investigates using the mineral oil in room-temperature vulcanizing silicone to create the desired mechanical properties and needle insertion characteristics of a tissue-mimicking phantom. Silicone samples mixed with 0, 20, 30, and 40 wt. % mineral oil were fabricated for indentation and needle insertion tests and compared to four types of porcine tissues (liver, muscle with the fiber perpendicular or parallel to the needle, and fat). The results demonstrated that the elastic modulus and needle insertion force of the phantom both decrease with an increasing concentration of mineral oil. Use of the mineral oil in silicone could effectively tailor the elastic modulus and needle insertion force to mimic the soft tissue. The silicone mixed with 40 wt. % mineral oil was found to be the best tissue-mimicking phantom and can be utilized for needle-based medical procedures.

Minimum Quantity Lubrication (MQL) machining involves the application of a minute amount of an oil-based lubricant to the machining process in an attempt to replace the conventional flood coolant system. Understanding the correlations between fluid properties and MQL performance can help in selecting lubricants from a variety of choices without going through extensive machining tests. This study compared nine different MQL fluids in terms of their physical properties, wettability, tribological properties (lubricity and extreme pressure (EP) properties), mist characteristics and machinability to determine the correlation of measured properties and MQL drilling and reaming performance. Results show that low fluid viscosity, high mist concentration, large mist droplet diameter and high wettability were best correlated with good machinability. Although it is difficult to draw strong relationships, the optimal machining in a mild cutting condition was found with the low viscosity fluids, which may also have the highest mist concentration, largest drops and best wettability.