There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review

Smithells Metals Reference Book

3D printing: Principles and pharmaceutical applications of selective laser sintering.

Ti6Al4V alloy has received a great deal of attention in medical applications due to its biomechanical compatibility. However, the human bone stiffness is between 10 and 30 GPa while solid Ti6Al4V is several times stiffer, which would cause stress shielding with the surrounding bone, which can lead to implant and/or the surrounding bone’s failure.,In this work, the effect of selective laser melting (SLM) process parameters on the characteristics of Ti6Al4V samples, such as porosity level, surface roughness, elastic modulus and compressive strength (UCS), has been investigated using response surface method. The examined ranges of process parameters were 35-50 W for laser power, 100-400 mm/s for scan speed and 35-120 µm for hatch spacing. The process parameters have been optimized to obtain structures with properties very close to that in human bones.,The results showed that the porosity percentage of a SLM component could be increased by reducing the laser power and/or increasing the scan speed and hatch spacing. It was also shown that there was a reverse relationship between the porosity level and both the modulus of elasticity and UCS of the SLM part. In addition, the increased laser power was resulted into a substantial decrease of the surface roughness of SLM parts. Results from the optimization study revealed that the interaction between laser process parameters (i.e. laser power, laser speed, and the laser spacing) have the most significant influence on the mechanical properties of fabricated samples. The optimized values for the manufacturing of medical implants were 49 W, 400 mm/s and 99 µm for the laser power, laser speed and laser spacing, respectively. The corresponding porosity, surface roughness, modulus of elasticity and UCS were 23.62 per cent, 8.68 µm, 30 GPa and 522 MPa, respectively.,Previous investigations related to additive manufacturing of Ti alloys have focused on producing fully dense and high-integrity structures. There is a clear gap in literature regarding the simultaneous enhancement and adjustment of pore fraction, surface and mechanical properties of Ti6Al4V SLM components toward biomedical implants. This was the objective of the current study.

/pdf/optimization-of-slm-process-parameters-for-ti6al4v-medical-5bmbl0fh5d.pdf

Optimization of SLM Process Parameters for Ti6Al4V Medical Implants

Additive Manufacturing Technologies for Drug Delivery Applications.

/pdf/manufacturing-of-ti-6al-4v-micro-implantable-parts-using-30c9r14v6o.pdf

Manufacturing of Ti–6Al–4V Micro‐Implantable Parts Using Hybrid Selective Laser Melting and Micro‐Electrical Discharge Machining

Cellular structures are lightweight-engineered materials that have gained much attention with the development of additive manufacturing technologies. This paper introduces a precise approach to predict the mechanical properties of additively manufactured lattice structures using deep learning approaches. Diamond shaped nodal lattice structures were designed by varying strut length, strut diameter and strut orientation angle. The samples were manufactured using laser powder bed fusion (LPBF) of Ti-64 alloy and subjected to compression testing to measure the ultimate strength, elastic modulus, and specific strength. Machine learning approaches such as shallow neural network (SNN), deep neural network (DNN), and deep learning neural network (DLNN) were developed and compared to the statistical design of experiment (DoE) approach. The trained DLNN model showed the highest performance when compared to DNN, DoE and SNN with a mean percentage error of 5.26%, 14.60%, and 9.39% for the ultimate strength, elastic modulus, and specific strength, respectively. The DLNN model was used to create process maps, and was further validated. The results showed that although deep learning is preferred for big data, the optimised DLNN model outperformed the statistical DoE approach and can be a favourable tool for lattice structure prediction with limited data.

/pdf/controlling-the-properties-of-additively-manufactured-478vaabz9x.pdf

Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches

A key advantage of additive manufacturing (AM) is that it allows the fabrication of lattice structures for customized biomedical implants with high performance. This paper presents the use of statistical approaches in design optimization of additively manufactured titanium lattice structures for biomedical implants. Design of experiments using response surface and analysis of variance was carried out to study the effect design parameters on the properties of the AM lattice structures such as ultimate compression strength, specific compressive strength, elastic modulus, and porosity. In addition, the lattice dimensions were optimized to fabricate a diamond cellular structure with properties that match human bones. The study found that the length of a diamond-shaped unit cell strut is the most significant design parameter. In particular, the porosity of the unit cell increases as the strut length increases, while it had a significant reverse effect on the specific compressive strength, elastic modulus, and ultimate compression strength. On the other hand, increasing the orientation angle was found to reduce both the specific compressive strength and modulus of elasticity of the lattice structure. An optimized lattice structure with strut diameter of 0.84 mm, length of 3.29 mm, and orientation angle of 47° was shown to have specific compressive strength, elastic modulus, ultimate compression strength, and porosity of 37.8 kN m/kg, 1 GPa, 49.5 MPa, and 85.7%, respectively. A cellular structure with the obtained properties could be effectively applied for trabecular bone replacement surgeries.

Mahmoud Ahmed El-Sayed

Papers

Optimization of SLM Process Parameters for Ti6Al4V Medical Implants

Additive Manufacturing Technologies for Drug Delivery Applications.

Manufacturing of Ti–6Al–4V Micro‐Implantable Parts Using Hybrid Selective Laser Melting and Micro‐Electrical Discharge Machining

Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches

Design optimization of additively manufactured titanium lattice structures for biomedical implants