Improving Volumetric Accuracy of AM Part Using Adaptive Slicing of Octree Based Structure

In Additive Manufacturing (AM) processes, the layer-by-layer fabrication of complex geometries may lead to stair casing and thus error resulting in volumetric inaccuracies in the model. Using thinner slices reduces the staircase error and improves part accuracy but there is a tradeoff between number of layers and the build time for manufacturing part. This paper presents a octree based structure to improve the accuracy as well as reduces the build time. In the current work, firstly converting STL file into a modified boundary octree data structure (MBODS) and then calculating the non-uniform slice thicknesses (adaptive slicing) from the octree representation. This slice thickness at any height is computed from the AM machine parameters and the smallest octree size at that available height. After the computation of the variable slice thicknesses has been completed, the part is virtually manufactured and the part errors are calculated. The virtually manufactured part and physical models are inspected to evaluate the volumetric errors. This algorithm uses an octree approach to improve the volumetric accuracy. And build time for the two different case studies are also done.Mechanical Engineerin

Design & Manufacturing of Implant for reconstructive surgery: A Case Study

Additive Manufacturing (AM), also known as 3D printing is an emerging technology in oral & maxillofacial surgery with respect to reconstructive bone surgery. Such treatment protocols often require customized implants to fulfill the functional and aesthetic requirements. Currently, such customized implants are being manufactured using AM technology. This paper describes a mandible defect of oral & maxillofacial surgery. The fracture and defect of the mandible inferior border is one of the serious complications during alignment and fixing of the implant. Reconstruction of such defects is daunting tasks. The case report describes a method based on Computer Aided Design (CAD) and AM for individual design, fabrication and implantation of a mandible inferior border. A 40-year old male meet an accident with rash drive. The patient specific customized implant is designed with patient Computed Tomography (CT) data. The CT images in Digital Imaging and Communication in Medicine (DICOM) file format is used to develop a 3D CAD model of customized implant. The implant is designed to maintain the symmetry of mandible from right to left. The designed implant model is manufactured by Fused Deposition Modelling (FDM) techniques with a biocompatible material. The patient mandible prototype model was manufactured by AM process, which is helpful for pre-planning of surgical procedures. For these pre-planning surgical procedures, a perfect fit obtained during surgery. The patient ultimately regained reasonable mandible contour and appearance of the face.

An on-chip robust Real-time Automated Non-invasive Cardiac Remote Health Monitoring Methodology

This paper introduces a novel Real-time Automated Non-invasive Cardiac Remote Health Monitoring Methodology for the detection of human condition by analyzing the ECG signal under the real-time environment. We proposed a novel System-on-Chip (SoC) architecture which has four folded modeling. After the data sensing, firstly, the classification module uses Hurst exponent as a metric in classifying the condition of the ECG signal. Secondly, if there is an abnormal detection by classifier, the Feature Extraction (FE) extracts QRS complex, P wave and T wave from ECG frames. As there is demand of ECG frames, a robust Boundary Detection (BD) mechanism is introduced to identify such frames. The fragmented-QRS (f-QRS) feature is also introduced as our third contribution to detect the fragmentation in the QRS complex and identify its morphology. The fourth step is compressing the ECG data using Hybrid compression technique for low area implementation and communicating this data to the nearest health care center by incorporating the concept of an Adaptive Rule Engine (ARE) based classifier. The proposed SoC architecture is prototyped on Xilinx Virtex 7 FPGA and it is tested on 100 patient's data from PTBDB (MIT-BIH), CSE and in house IITH DB, the percentage of accuracy obtained is 91% and it is under process for Tape-out