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Nowadays, there has been continuous development of metallic biomaterials to meet special needs in the manufacturing of biomedical implants, units and systems so as to function well in the required environment. Developed biomaterials which possess exceptional properties in terms of biocompatibility and biomechanical compatibility require precision processing and machining to obtain the desired dimensional tolerances. Electrical discharge machining (EDM) is the noncontact or nontraditional process of machining that suits the precision machining of biomaterials. In this work, an effort was made to optimize the EDM parameters during machining of titanium-based biomaterials Ti-6AL-4V, so that the multi-objective responses could be obtained. The response surface method was used in designing the experiment, while the grey relational method was used to analyze the effect of multiple objectives into a single unit. The electrical parameters that were considered in this study include peak current, gap voltage, pulse turn-on and duty cycle. These parameters were set within the acceptable limits of the equipment. Three responses were studied, which are tool wear rates (TWRs), material removal rate (MRR) and surface roughness (SR). Using the signal-to-noise ratio and ANOVA optimum tool/electrode wear rate (TWR) is obtained at $5\times 10^{-5}$g/min with process parameters $\mathrm{\text{Ip}}=6$ A, $\mathrm{\text{Vg}}=30$V, $\mathrm{\text{Ton}}=200$$\mu$s, $D=65$%. Optimum values of material removal rate (MRR) are obtained as 0.01035g/min with process parameters $\mathrm{\text{Ip}}=6$ A, $\mathrm{\text{Vg}}=60$V, $\mathrm{\text{Ton}}=140$$\mu$s, $D=50$%. Optimum SR is observed as 2.258$\mu$m with EDM process parameters $\mathrm{\text{Ip}}=6$ A, $\mathrm{\text{Vg}}=90$V, $\mathrm{\text{Ton}}=200$$\mu$s, $D=65$%. Surface characteristics are verified with SEM micrographs. Whereas, grey relation analysis predicted the multi-objective optimum response characteristics. Based on the grey relation grade, experiment number 7 ($\mathrm{\text{Ip}}=6$A, $\mathrm{\text{Vg}}=90$V, $\mathrm{\text{Ton}}=200$$\mu$s, $D=65$%) secured the first rank among the experiments/trails.

The material composition and surface structure of dental and orthopaedic implants influence the integration in bone, and, at the same time, the release of the constituent elements of the implants into the surrounding tissues. Titanium, titanium alloys (Ti-6Al-4V) and other materials which are considered to be biocompatible materials, release metal ions into the surrounding tissues. This may affect the appropriate biological response of the tissues to the implanted materials. The toxicity of some of the constituent elements of these implants, e.g. vanadium, is a matter of serious concern. In this paper, the experimental data on application of PIXE to biomedical-implant research are presented. Two groups of samples were prepared for PIXE analysis. The first group consisted of the specimens that were retrieved from the tissues around implants in 28 rats. The second group consisted of specimens that were sliced and polished to a thickness of a few 10 µm, while keeping the interface between the implant, cortical bone, bone marrow, and soft tissues intact. The implants of the second group, 10 specimens with between 6 and 20 measurement points on each specimen, were used for specifically obtaining the distribution of the released elements with reference to the implant tissue interface, using ion micro beam. The implantation time ranged from 12 weeks to 52 weeks.

Keeping power consumption low in implantable neuro-stimulators such as Cochlear Implants or Vision Prostheses is one of the major design challenges in their circuit design. Usually electrode impedance and stimulation currents required to elicit physiological responses mandates the use of large stimulation voltages, again dictating the use of high-voltage integrated circuit technologies. Power consumption in the stimulating circuits and associated supply generation circuits are the major contributors to overall system power dissipation. In this paper we present circuit design techniques that address power consumption in both stimulating circuits and power supply circuits. First, our power supply design approach is to recycle currents between the two low-voltage power supply needed for the stimulating circuits, whereby power consumption in these circuits can be close to halved. Second, our stimulating circuits design approach is to use very small quiescent currents, fast turn-on time and pre-stimulating dynamic calibration which allow the delivery of charge balanced bi-phasic stimulation pulses with very good power efficiency. A variation of this include passive charge recovery for further power reduction. In combination, significant implant power consumption reduction is achieved.

As power consumption is one of the major issues in biomedical implantable devices, in this paper, a novel quantization method is proposed for successive approximation register (SAR) analog-to-digital converters (ADCs) which can save 80% power consumption in contrast to conventional structure for electroencephalogram (EEG) signal recording systems. According to the characteristics of neural signals, the principle of the proposed power saving technique was inspired such that only the difference between current input sample and the previous one is quantized, using a power efficient SAR ADC with fewer resolutions. To verify the proposed quantization scheme, the ADC is systematically modeled in Matlab and designed and simulated in circuit level using 0.18$\mu$m CMOS technology. When applied to neural signal acquisition, spice simulations show that at sampling rate of 25kS/s, the proposed 8-bit ADC consumes 260nW of power from 1.8V supply voltage while achieving 7.1 effective number of bits.