In recent decades, vascular surgery has seen the arrival of endovascular techniques for the treatment of vascular diseases such as aortic diseases (aneurysms, dissections, and atherosclerosis). The 3D printing process by addition of material gives an effector of choice to the digital chain, opening the way to the manufacture of shapes and complex geometries, impossible to achieve before with conventional methods. This chapter focuses on the bio-design study of the thoracic aorta in adults. A bio-design protocol was established based on medical imaging, extraction of the shape, and finally, the 3D modeling of the aorta; secondly, a bio-printing method based on 3D printing that could serve as regenerative medicine has been proposed. A simulation of the bio-printing process was carried out under the software Simufact Additive whose purpose is to predict the distortion and residual stress of the printed model. The binder injection printing technique in a Powder Bed Printer (PBP) bed is used. The results obtained are very acceptable compared with the results of the error elements found.
The most important components used in aerospace, ships, and automobiles are designed with free form surfaces. An impeller is one of the most important components that are difficult to machine because of its twisted blades. This research book is based on the premise that a STEP-NC program can document “generic” manufacturing information for an impeller. This way, a STEP-NC program can be made machine-independent and has an advantage over the conventional G-code-based NC program that is always generated for a specific CNC machine. Rough machining is recognized as the most crucial procedure influencing machining efficiency and is critical for the finishing process. The research work reported in this chapter focuses on introduces a fully STEP-compliant CNC by putting forward an interpolation algorithm for non uniform rational basic spline (NURBS) curve system for rough milling tool paths with an aim to solve the problems of kinematic errors solutions in five axis machine by neural network implementation.
Nowadays, we assist the global extension of reliability optimization problems from the design phase of systems and sub-systems to the design and operational phases, not only of systems and sub-systems, but also of bio functionality design. This chapter investigates the relative performances of particle swarm optimization (PSO) variants when used to find reliability in the total hip prosthesis by finding the maximization of jumping distance (JD) to avoid dislocation and the minimization of system’s stability to offer mobility. Statistical analysis of different cases of head diameters of 22, 28, 36, 40 mm has been conducted to survey the convergence and relative performances of the main PSO variants when applied to solve reliability in the total hip prosthesis.