Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Materials science & Computer science. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Multi Response Optimization and Process Capability Analysis of Fused Filament Fabrication and Chemical Vapor Smoothing Operations for Rapid Casting of Biomedical Implants

Abstract: Despite numerous applications of Fused Filament Fabrication (FFF), the poor surface finish, being an inherent defect, is major obstruction against utilization of prototypes for rapid casting applications. The conventional finishing methods are effective, however, the dimensional stability is compromised which is completely unacceptable in case of biomedical implants as minute dimension variations may lead to post-operative complications. The paper explores possibilities to produce chemically finished plastic replicas of hip implant through FFF which can be further used as patterns for investment casting. An advanced post-processing technique i.e. Chemical Vapor Smoothing (CVS) is tested to improve surface characteristics and dimensional accuracy of ABS replicas. The repeatability and consistency of coupled CVS and FFF processes is tested through experimentation and statistical analysis in order to endorse an alternative process for mass production of biomedical implants. The Taguchi L18 DOE was used to perform experiments which measures impact of two input parameters of FFF i.e. orientation angle and density, and four parameters of Chemical Vapor Smoothing i.e. pre-cooling time, smoothing time, smoothing time and number of cycles. The multi response optimization technique was employed to acquire optimum set of parameters yielding best surface finish, dimensional accuracy and surface hardness and hence, the overall desirability of 0.7891 was achieved. The process capability and reliability was tested at optimum settings by manufacturing twenty replicas by measuring surface roughness, hardness and dimensional accuracy at different locations of parts. It was observed that values of Cp for all the response parameters was greater than 1.33 while Cpk was greater than 1. The analysis of histograms and capability indices reveal that the FFF-CVS process carried out at optimized conditions can be defined as “statistically controlled” for fabrication of ABS replicas for biomedical applications.

An Ann Based Approach For Software Fault Prediction Using Object Oriented Metrics

Abstract: During recent years, the enormous increase in demand for software products has been experienced. High quality software is the major demand of users. Predicting the faults in early stages will improve the quality of software and apparently reduce the development efforts or cost. Fault prediction is majorly based on the selection of technique and the metrics to predict the fault. Thus metrics selection is a critical part of software fault prediction. Currently techniques been evaluated based on traditional set of metrics. There is a need to identify the different techniques and evaluate them on the bases of appropriate metrics. In this research, Artificial neural network is used. For classification task, ANN is one of the most effective technique. Artificial neural network based SFP model is designed for classification in this study. Prediction is performed on the basis of object-oriented metrics. 5 object oriented metrics from CK and Martin metric sets are selected as input parameters. The experiments are performed on 18 public datasets from PROMISE repository. Receiver operating characteristic curve, accuracy, and Mean squared error are taken as performance parameters for the prediction task. Results of the proposed systems signify that ANN provides significant results in terms of accuracy and error rate.