scholarly journals Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Fei Yang ◽  
Zhen Li ◽  
Qing Wang ◽  
Beibei Jiang ◽  
Biaojie Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Ti Alloys

scholarly journals First principles computation of composition dependent elastic constants of omega in titanium alloys: implications on mechanical behavior

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
R. Salloom ◽  
S. A. Mantri ◽  
R. Banerjee ◽  
S. G. Srinivasan
Keyword(s):  
Mechanical Properties ◽  
Young’S Modulus ◽  
First Principles ◽  
Young's Modulus ◽  
Group Vi ◽  
Group V ◽  
Β Phase ◽  
Ti Alloys ◽  
Ω Phase ◽  
Stabilizer Concentration

AbstractFor decades the poor mechanical properties of Ti alloys were attributed to the intrinsic brittleness of the hexagonal ω-phase that has fewer than 5-independent slip systems. We contradict this conventional wisdom by coupling first-principles and cluster expansion calculations with experiments. We show that the elastic properties of the ω-phase can be systematically varied as a function of its composition to enhance both the ductility and strength of the Ti-alloy. Studies with five prototypical β-stabilizer solutes (Nb, Ta, V, Mo, and W) show that increasing β-stabilizer concentration destabilizes the ω-phase, in agreement with experiments. The Young’s modulus of ω-phase also decreased at larger concentration of β-stabilizers. Within the region of ω-phase stability, addition of Nb, Ta, and V (Group-V elements) decreased Young’s modulus more steeply compared to Mo and W (Group-VI elements) additions. The higher values of Young’s modulus of Ti–W and Ti–Mo binaries is related to the stronger stabilization of ω-phase due to the higher number of valence electrons. Density of states (DOS) calculations also revealed a stronger covalent bonding in the ω-phase compared to a metallic bonding in β-phase, and indicate that alloying is a promising route to enhance the ω-phase’s ductility. Overall, the mechanical properties of ω-phase predicted by our calculations agree well with the available experiments. Importantly, our study reveals that ω precipitates are not intrinsically embrittling and detrimental, and that we can create Ti-alloys with both good ductility and strength by tailoring ω precipitates' composition instead of completely eliminating them.

Fabrication of Titanium / Biodegradable-Polymer FGM for Medical Application

2009 ◽  
Vol 631-632 ◽  
pp. 199-204 ◽  
Author(s):  
Yoshimi Watanabe ◽  
Yoshimi Iwasa ◽  
Hisashi Sato ◽  
Akira Teramoto ◽  
Koji Abe
Keyword(s):  
Mechanical Properties ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Medical Application ◽  
Hot Water ◽  
Porous Ti ◽  
Ti Alloys ◽  
Plasma Sintering ◽  
Biodegradable Poly ◽  
Spark Plasma

Ti and Ti alloys are widely used as metallic implants, because of their good mechanical properties and nontoxic behavior. However, they have problems as the implant-materials, namely, high Young’s modulus comparing that of bone and low bonding ability with bone. There is a need to develop the Ti and Ti alloys with lower Young’s modulus and good bonding ability. In previous study, Ti composite containing biodegradable poly-L-lactic-acid (PLLA) fiber has been fabricated to improve these problems. However, this composite has low strength because of the imperfect sintering of Ti matrix. To improve its strength, sintering of Ti matrix should be completed. In this study, Ti-NaCl composite material was fabricated by spark plasma sintering (SPS) method using powder mixture of Ti and NaCl to complete the sintering of Ti matrix. To obtain porous Ti samples, Ti-NaCl composite were put into hot water of 100 oC. The porous Ti was dipped into PLLA melt in order to introduce PLLA into the pores of porous Ti. Finally, Ti-PLLA composite was obtained, and PLLA plays a role as reinforcement of Ti matrix. It was found that the Ti-PLLA composite has gradient structure and mechanical properties.

Fabrication of Be-Ta Ti Alloys without Pre-Alloyed Powders via SLM

2021 ◽  
Vol 1016 ◽  
pp. 1797-1801
Author(s):  
Mitsuharu Todai ◽  
Takeshi Nagase ◽  
Takayoshi Nakano
Keyword(s):  
Young’S Modulus ◽  
Melting Temperature ◽  
Crystallographic Texture ◽  
Crystal Orientation ◽  
Single Phase ◽  
Young's Modulus ◽  
Laser Melting ◽  
Ti Alloys

In this study, we sucsess the fabrication of dense compornent of Ti-20at.%X (X = Cr and Nb) alloys by Selected laser melting (SLM) pwocess, from a mixture of poweder element powders. The volume rasio of pore and non-molten particles is dependent of the enegy density. The difficulty of fabrication of Ti-X alloy comporment is dependent of melting temperature of X element. Thus, Ti-20at.%Cr alloys, which has the lowest melting temperature of X is easier to monufacture of dense comporment. The Ti-20at.%Cr alloys and Ti-20at.%Nb comprise β-Ti single-phase components without any non-molten particles and macroscopic defects. In addtion, the {001}〈100〉 crystallographic texture of these Ti-Cr and Ti-Nb alloys can be controlled effectively by optimizing the SLM parameters. This means that the SLM is key techmelogy of controlling of Young’s modulus and shape at the same time because Young's modulus of be-ta phase in Ti alloys is strongly related to the crystal orientation.

scholarly journals Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

2020 ◽  
Vol 12 (5) ◽  
pp. 1880 ◽  
Author(s):  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin Elkatatny ◽  
Dhafer Al Shehri
Keyword(s):  
Machine Learning ◽  
Young’S Modulus ◽  
Transit Time ◽  
Young's Modulus ◽  
Wellbore Stability ◽  
Learning Models ◽  
Static Young’S Modulus ◽  
Data Points ◽  
Sandstone Formation ◽  
Machine Learning Models

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

scholarly journals Control of crystallographic orientation by metal additive manufacturing process of β-type Ti alloys based on the bone tissue anisotropy

2020 ◽  
Vol 321 ◽  
pp. 05002
Author(s):  
Takayoshi Nakano ◽  
Takuya Ishimoto ◽  
Aira Matsugaki ◽  
Koji Hagihara ◽  
Yuichiro Koizumi ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Young’S Modulus ◽  
Bone Tissue ◽  
Young's Modulus ◽  
Stress Shielding ◽  
Scanning Strategy ◽  
Ti Alloys ◽  
Specific Direction ◽  
Tissue Anisotropy ◽  
Metal Additive

Variation in the scanning strategy for β-type Ti alloys during additive manufacturing (AM) enables the fabrication of a singlecrystal-like microstructure possessing a crystallographic texture, in which the low-Young’s modulus-<100> direction is aligned along a specific direction. Thus, metal biomaterial with low elasticity, comparable to the bone Young’s modulus, can be developed by AM, which will contribute to suppress the stress shielding of bone and prevent degradation of bone tissue anisotropy.

Predict Geomechanical Parameters with Machine Learning Combining Drilling Data and Gamma Ray

2021 ◽  
Author(s):  
Mattia Martinelli ◽  
Ivo Colombo ◽  
Eliana Rosa Russo
Keyword(s):  
Machine Learning ◽  
Bulk Density ◽  
Young’S Modulus ◽  
Gamma Ray ◽  
Young's Modulus ◽  
Geomechanical Parameters ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Drilling Data ◽  
Sonic Logs

Abstract The aim of this work is the development of a fast and reliable method for geomechanical parameters evaluation while drilling using surface logging data. Geomechanical parameters are usually evaluated from cores or sonic logs, which are typically expensive and sometimes difficult to obtain. A novel approach is here proposed, where machine learning algorithms are used to calculate the Young's Modulus from drilling parameters and the gamma ray log. The proposed method combines typical mud logging drilling data (ROP, RPM, Torque, Flow measurements, WOB and SPP), XRF data and well log data (Sonic logs, Bulk Density, Gamma Ray) with several machine learning techniques. The models were trained and tested on data coming from three wells drilled in the same basin in Kuwait, in the same geological units but in different reservoirs. Sonic logs and bulk density are used to evaluate the geomechanical parameters (e.g. Young's Modulus) and to train the model. The training phase and the hyperparameter tuning were performed using data coming from a single well. The model was then tested against previously unseen data coming from the other two wells. The trained model is able to predict the Young's modulus in the test wells with a root mean squared error around 12 GPa. The example here provided demonstrates that a model trained with drilling parameters and gamma ray coming from one well is able to predict the Young Modulus of different wells in the same basin. These outcomes highlight the potentiality of this procedure and point out several implications for the reservoir characterization. Indeed, once the model has been trained, it is possible to predict the Young's Modulus in different wells of the same basin using only surface logging data.

Change in Mechanical Strength and Bone Contact Ratio of Beta-Type TNTZ Subjected to Mechanical Surface Modification

2016 ◽  
Vol 879 ◽  
pp. 2371-2376
Author(s):  
Toshikazu Akahori ◽  
Tomokazu Hattori ◽  
Hisao Fukui ◽  
Mitsuo Niinomi
Keyword(s):  
Young’S Modulus ◽  
Vickers Hardness ◽  
Fine Particles ◽  
Young's Modulus ◽  
Solution Treatment ◽  
Contact Ratio ◽  
Bone Contact ◽  
Ti Alloys ◽  
Metastable Beta ◽  
Mechanical Surface

Ti-29Nb-13Ta-4.6Zr (TNTZ), which is one of metastable beta-type Ti alloys, has developed as one of representative biomedical and dental Ti alloys in Japan. TNTZ subjected to solution treatment shows Young’s modulus of 60 GPa, which is close to that of cortical bone. In addition, TNTZ has very low cytotoxicity and good bone biocompatibility as well. Heat treatment like solution treatment and aging (STA) is mainly used for improving the mechanical properties of metastable beta-type Ti alloys because of alpha precipitates, while Young’s modulus also rises drastically. This study was investigated the effects of mechanical surface modifications such as fine particle bombarding (FPB) with steel and hydroxyapatite particles or friction stir processing (FSP) on the mechanical strength of TNTZ in order to maintain low Young’s modulus. The relative bone contact ratios between the cancellous bones of Japanese white rabbits and column-shaped TNTZ subjected to FPB of steel particles were also evaluated. Vickers hardness (HV) of TNTZ subjected to FPB with fine particles of steel and hydroxyapatite particles increased by HV30 to 200 at the edge of the specimen surface to around 100 to 300 mm in depth as compared with that of TNTZ subjected to solution treatment. The hydroxyapatite layer was formed on the specimen surface by FPB with fine particles of hydroxyapatite particles, although the trend was not significant by FPB with steel particles. Furthermore, the fatigue strength in high cycle fatigue region of TNTZ subjected to FPB with steel particles was improved and the fatigue limit showed around 400 MPa, although that of TNTZ subjected to FPB with fine particles of hydroxyapatite particles were around 60 MPa higher than that to TNTZ subjected to solution treatment (230 MPa). TNTZ with a rough surface texture (Ra: 0.65 μm) showed a relative bone contact ratio of more than 80% after undergoing FPB with fine particles of steel particles; this value was significantly higher than that of TNTZ with a surface texture (Ra: 0.07 μm). Lastly, the microstructure of TNTZ subjected to FSP showed the recrystallization area by the frictional heating with very fine equiaxed beta phase with an average grain diameter of 3.0 μm. The change in Vickers hardness of TNTZ subjected to FSP was almost identical to that of Young’s modulus and showed the almost same trend of FPB.

scholarly journals Predicting Young's modulus of oxide glasses with sparse datasets using machine learning

2019 ◽  
Vol 524 ◽  
pp. 119643 ◽  
Author(s):  
Suresh Bishnoi ◽  
Sourabh Singh ◽  
R. Ravinder ◽  
Mathieu Bauchy ◽  
Nitya Nand Gosvami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Oxide Glasses

A METALLURGICAL OVERVIEW OF TI – BASED ALLOY IN BIOMEDICAL APPLICATIONS

2015 ◽  
Vol 76 (7) ◽  
Author(s):  
Nur Hidayatul Nadhirah Elmi Azham Shah ◽  
Mazyan Yahaya ◽  
Maheran Sulaiman ◽  
Muhammad Hussain Ismail
Keyword(s):  
Young’S Modulus ◽  
Biomedical Application ◽  
Young's Modulus ◽  
Stress Shielding ◽  
Young Modulus ◽  
Processing Technique ◽  
Shielding Effect ◽  
Β Phase ◽  
Ti Alloys ◽  
Α Phase

Titanium (Ti)-based alloys are prominently used in biomedical application. This review paper emphasizes on some of the important aspects of the Ti-alloys in terms of metallurgical aspects, manufacturing routes and biocompatibility. Two kinds of structure are reviewed namely dense and porous, both differs in terms of purpose and satisfies different needs. This advancement of materials and equipment helps to improve the quality of life for patients and alleviate their health problems. Metallic materials, mainly Ti-based alloys have been used commercially as bone implant owing to its promising mechanical properties, biocompatibility and bioactivity. The outmost important issue in manufacturing  of  this  alloy  is  the  impurity  contents,  specifically  oxygen  and  carbon  which contribute   to decreasing in material performance of the alloy attributed from the formation of unwanted  oxide compounds such as TiO2 and  TiC. Another issue is the mismatch value of the Young’s modulus between the metallic implant and bone that result in stress shielding effect.  The structure of Ti-based  alloy is  mainly comprised of α-phase, β-phase or a combination of  both that result in variation of Young’s modulus ranging from 45 -110 GPa. Compared to α-phase Ti alloy, the β-phase rich alloys may exhibit lower value of Young modulus through the right processing technique. Therefore, the development of β-phase Ti-alloys has been researched progressively in line with the need of low Young’s modulus that suit for implant applications.

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