Machinability assessment of titanium alloy Ti-6Al-4V for biomedical applications

Titanium alloy (Ti-6Al-4V) has a wide range of application in various fields of engineering. Titanium is mainly used to manufacture aerospace components like landing gear, fuselage, wings, engines etc. and biomedical components like hip joint, knee joint, dental implants etc. Titanium has outstanding material properties such as corrosion resistance, fatigue strength, tensile strength and a very good biocompatibility which makes this material very alluring for biomedical applications. Contrary, the machinability of the material is problematic because of the phase transformations and thus, titanium alloy is a challenge for machining operation. This research is a comparative analysis between the implants manufactured by traditional method of casting and machining. The femoral stem of the hip joint replacement is designed and the component is machined using a five-axis CNC machine.The machined component was subjected to surface roughness testing, tensile testing and bulk hardness testing. The values were compared with the values of titanium implants manufactured by casting. © (2014) Trans Tech Publications, Switzerland.

Titanium in biomedical applications—properties and fabrication: a review

Ti and Ti-based alloys have unique properties such as high strength, low density and excellent corrosion resistance. These properties are essential for the manufacture of lightweight and high strength components for biomedical applications. In this paper, Ti properties such as metallurgy, mechanical properties, surface modification, corrosion resistance, biocompatibility and osseointegration in biomedical applications have been discussed. This paper also analyses the advantages and disadvantages of various Ti manufacturing processes for biomedical applications such as casting, powder metallurgy, cold and hot working, machining, laser engineering net shaping (LEN), superplastic forming, forging and ring rolling. The contributions of this research are twofold, firstly scrutinizing the behaviour of Ti and Ti-based alloys in-vivo and in-vitro experiments in biomedical applications to determine the factors leading to failure, and secondly strategies to achieve desired properties essential to improving the quality of patient outcomes after receiving surgical implants. Future research will be directed toward manufacturing of Ti for medical applications by improving the production process, for example using optimal design approaches in additive manufacturing and investigating alloys containing other materials in order to obtain better medical and mechanical characteristics.

Photocatalytic degradation of dye effluent by titanium dioxide pillar pellets in aqueous solution

Photocatalytic oxidation (PCO) process is an effective way to deal with organic pollutants in wastewater which could be difficult to be degraded by conventional biological treatment methods. Normally the TiO2 powder in nanometre size range was directly used as photocatalyst for dye degradation in wastewater. However the titanium dioxide powder was arduous to be recovered from the solution after treatment. In this application, a new form of TiO2 (i.e. pillar pellets ranging from 2.5 to 5.3 mm long and with a diameter of 3.7 mm) was used and investigated for photocatalytic degradation of textile dye effluent. A test system was built with a flat plate reactor (FPR) and UV light source (blacklight and solar simulator as light source respectively) for investigating the effectiveness of the new form of TiO2. It was found that the photocatalytic process under this configuration could efficiently remove colours from textile dyeing effluent. Comparing with the TiO2 powder, the pellet was very easy to recovered from the treated solution and can be reused in multiple times without the significant change on the photocatalytic property. The results also showed that to achieve the same photocatalytic performance, the FPR area by pellets was about 91% smaller than required by TiO2 powder. At least TiO2 pellet could be used as an alternative form of photocatalyst in applications for textile effluent treatment process, also other wastewater treatment processes.

Machine vision system for automatic inspection of surface defects in aluminum die casting

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Novel titanium foam for bone tissue engineering

Titanium foams fabricated by a new powder metallurgical process have bimodal pore distribution architecture (i.e., macropores and micropores), mimicking natural bone. The mechanical properties of the titanium foam with low relative densities of approximately 0.20-0.30 are close to those of human cancellous bone. Also, mechanical properties of the titanium foams with high relative densities of approximately 0.50-0.65 are close to those of human cortical bone. Furthermore, titanium foams exhibit good ability to form a bonelike apatite layer throughout the foams after pretreatment with a simple thermochemical process and then immersion in a simulated body fluid. The present study illustrates the feasibility of using the titanium foams as implant materials in bone tissue engineering applications, highlighting their excellent biomechanical properties and bioactivity.

Seeding of single crystal superalloys--Role of seed melt-back on casting defects

Single crystal seeds of the nickel-base superalloy CMSX-4 have been partially melted in a temperature gradient and then quenched. Small islands of random orientation are observed throughout the melted-back semi-solid. These random orientations appear to be pinched-off secondary dendrite arms, but there is no evidence that they are transported ahead of the dendrite tips to nucleate stray grains during directional solidification.

Thermal control of high pressure die casting using computational fluid dynamics

The quality of high pressure die castings is a function of many interdependent parameters. It has been observed that many defects detected in the HPDC castings can be tracked back to poor die temperature distribution in the critical areas. It has therefore been recommended that the development of a technique to directly control the critical features - making them less sensitive to thermal related parameters - be very beneficial to the HPDC industry. From the information obtained from thermal image (processing), computational fluid dynamics has been applied to design the layout of internal cooling system and assign the flow conditions such as flow rate and pressure of the cooling water. it is observed that CFD prediction provides an excellent insight into the thermal balance of the high pressure die casting.

Modelling of porosity defects in high pressure die casting with a neural network

High Pressure Die Casting (HPDC) is a complex process that results in casting defects if configured improperly. However, finding out the optimal configuration is a non-trivial task as eliminating one of the casting defects (for example, porosity) can result in occurrence of other casting defects. The industry generally tries to eliminate the defects by trial and error which is an expensive and error -prone process. This paper aims to improve current modelling and understanding of defects formation in HPDC machines. We have conducted conventional die casting tests with a neural network model of HPDC machine and compared the obtained results with the current understanding of formation of porosity. While most of our findings correspond well to established knowledge in the field, some of our findings are in conflict with the previous studies of die casting.

Machine vision system for automatic inspection of surface defects in aluminium die casting

A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.