Extraction of flank wear growth models that correlates cutting edge integrity of ball nose end mills while machining titanium

The application of titanium alloys are increasingly seen at aerospace, marine, bio-medical and precision engineering due to its high strength to weight ratio and high temperature properties. However, while machining the titanium alloys using solid carbide tools, even with jet infusion of coolant lower tool life was vividly seen. The high temperatures generated at the tool–work interface causes adhesion of work-material on the cutting edges; hence, shorter tool life was reported. To reduce the high tool–work interface temperature positive rake angle, higher primary relief and higher secondary relief were configured on the ball nose end-mill cutting edges. However, after an initial working period, the growth of flank wear facilitates higher cutting forces followed by work-material adhesion on the cutting edges. Therefore, it is important to blend the strength, sharpness and surface integrity on the cutting edges so that the ball nose end mill would demonstrate an extended tool-life. Presently, validation of tool geometry is very tedious as it requires extensive machining experiments. This paper illustrates a new feature-based ball-noseend-mill–work interface model with correlations to the material removal mechanisms by which the tool geometry optimization becomes easier. The data are further deployed to develop a multi-sensory feature extraction/correlation model to predict the performance using wavelet analysis and Wagner Ville distribution. Conclusively, this method enables to evaluate the different ball nose end mill geometry and reduces the product development cycle time

Weighted Neighbourhood Preserving Embedding in face recognition

Graph Embedding (GE) along with its linearization outperforms the traditional linear dimension reduction techniques in face recognition, but there is still room for improvement on GE. This paper proposes an eigenvector weighting technique for a realization of linear GE, namely Neighbourhood Preserving Embedding (NPE) in face verification. The proposed method is called Eigenvector Weighting Function - NPE (EWF-NPE). The eigenspace is decomposed into three subspaces: (1) a subspace that is attributed to facial intra-class variations, (2) a subspace comprises of intrinsic facial characteristics, and (3) a subspace that is attributed to sensor and other external noises. Eigenfeatures are weighted differently in these subspaces. The proposed EWF-NPE ensures that only stable face subspace which yields informative data is emphasized, while the other two noise subspaces are deemphasized. Experimental investigations on FRGC and FERET databases demonstrate promising results of the proposed method

Eigenvector weighting function in face recognition

Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF), is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed techniqu

Carbon Nanotube Based Faraday's Cage for RF Circuit Packaging

International audienc

Improved rf isolation using carbon nanotube fence-wall for 3-d integrated circuits and packaging

International audienc

Factors impacting students’ creativity-related self-efficacy in an undergraduate makerspace-based course

The need to cultivate creativity in engineering education calls for opportunities for students to exercise freedom in proposing and pursuing projects aligned with their interests. This paper presents insights into an undergraduate makerspace-based course in terms of factors affecting students’ creativity-related self-efficacy. We conducted a survey on students who come from an engineering and science background to gain their opinions about the impact of this course on enhancing their creativity. To establish if there is a significant difference in the students’ creativity, we performed the non-parametric Wilcoxon signed-rank test comparing the first and second survey, with results showing that there is a statistically significant increase in students’ creativity-related self-efficacy. There was a general increase for all the items, especially students’ perceptions toward the relevance of the course, the conduciveness of the learning environment, opportunities to make and learn from mistakes, and the resourcefulness of their team. Results obtained via quantitative statistical analysis was backed up by qualitative analysis that employed text mining techniques such as automatic key phrase extraction and sentiment analysis on the open-ended responses and the reason(s) for the Likert-scale answer choice. In addition, we used the Spearman’s rho to report correlations between Likert-scale items and determine the variables that are significantly and positively correlated with the creativity-related self-efficacy construct. A multivariate regression model was then constructed to observe the extent to which each highly correlated variable impacts creativity-related self-efficacy; of which, a sense of relevance appears to have the largest effect. Through gaining insights into the factors that may impact students’ creativity-related self-efficacy, this study contributed to a deeper understanding on how this important attribute could be developed through a makerspace-based university course.Ministry of Education (MOE)This research is supported by the Ministry of Education, Singapore, under its MOE Tertiary Education Research Fund (MOE-TRF) Award (MOE2019- TRF-035)