A review of trends and limitations in hydrogel-rapid prototyping for tissue engineering

Additive manufacturing (AM), also known as 3D printing, has been increasingly adopted in the aerospace, automotive, energy, and healthcare industries over the past few years. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is surface integrity. To improve the surface integrity of additively manufactured parts, a data-driven predictive modeling approach to predicting surface roughness in AM is introduced. Multiple sensors of different types, including thermocouples, infrared temperature sensors, and accelerometers, are used to collect temperature and vibration data. An ensemble learning algorithm is introduced to train the predictive model of surface roughness. Features in the time and frequency domains are extracted from sensor-based condition monitoring data. A subset of these features is selected to improve computational efficiency and prediction accuracy. The predictive model is validated using the condition monitoring data collected from a set of AM tests conducted on a fused filament fabrication (FFF) machine. Experimental results have shown that the proposed predictive modeling approach is capable of predicting the surface roughness of 3D printed components with high accuracy.

Prediction of surface roughness in extrusion-based additive manufacturing with machine learning

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Advanced Modeling and Optimization of Manufacturing Processes

One shortcoming of metals and alloys used to fabricate various components of orthopaedic systems, such as the femoral stem of a total hip joint replacement and the tibial plate of a total knee joint replacement, is well-recognized. This is that the material modulus of elasticity (E′) is substantially larger than that of the contiguous cancellous bone, a consequence of which is stress shielding which, in turn, has been postulated to be implicated in a cascade of events that culminates in the principal life-limiting phenomenon of these systems, namely, aseptic loosening. Thus, over the years, a host of research programs have focused on the synthesis of metallic biomaterials whose E′ can be tailored to match that of cancellous bone. The present work is a review of the extant large volume of literature on these materials, which are called open-cell porous metals/alloys (or, sometimes, metal foams or cellular materials). As such, its range is wide, covering myriad aspects such as production methods, characterization studies, in vitro evaluations, and in vivo performance. The review also includes discussion of seven areas for future research, such as parametric studies of the influence of an assortment of process variables (such as the space holder material and the laser power in the space holder method and the laser-engineered net-shaping process, respectively) on various properties (notably, permeability, fatigue strength, and corrosion resistance) of a given porous metal/alloy, innovative methods of determining fatigue strength, and modeling of corrosion behavior.

Properties of open-cell porous metals and alloys for orthopaedic applications.

Additive Manufacturing of Biomaterials − The Evolution of Rapid Prototyping

Rapid prototyping technologies are capable of directly manufacturing physical objects from CAD models and have been increasingly used in product development, tool and die making and fabrication of functional parts. Solid ground curing (SGC) technology, one of the rapid prototyping technologies, is suitable of building multiple parts with different geometry and dimensions in batch production of rapid prototypes to minimize the cost of prototypes. However, the layout of CAD models in a graphic environment is time-consuming. Because of high cost of the resin, the layout of models in a batch is critical for the success of the SGC operations in any industrial environment. This paper presents the layout optimization using simulated annealing techniques. A software system was developed to assist Cubital operators to layout CAD models with various geometric shapes. The system accepts STL files from any solid modeling environment. Several examples are provided to illustrate the techniques and effectiveness of the approach.

B. Zhou

Papers

Model layout optimization for solid ground curing rapid prototyping processes