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.

Friction Welding for Functional Prototypes of PA6 and ABS with Al Powder Reinforcement

Abstract: In the present work, 3D-printed cylindrical discs of dissimilar thermoplastics [polyamide (PA6) reinforced with Al powder and acrylonitrile butadiene styrene (ABS) reinforced with Al powder] have been successfully joined by friction welding (FW) for structural applications such as joining of pavement sheets and assembly of pipe lines. The melt flow index of PA6 + Al and ABS + Al matrix was maintained in a suitable range by varying the proportions of Al in PA6 and ABS matrix. After fixing proportions of Al powder in PA6 and ABS matrix, these matrix proportions were used for preparation of feed stock filament of fused deposition modelling (FDM) filament by a twin-screw extrusion process. Finally, two FDM filaments of PA6 + Al and ABS + Al were fed into FDM machine independently. The cylindrical discs were printed on commercial FDM (one with filament of PA6 + Al and second with ABS + Al powder). These cylindrical discs of two dissimilar thermoplastic composite materials were processed on FW set-up (on central lathe machine). Finally, under the best parametric conditions of feed, rpm, etc., these polymer matrixes were successfully joined. This study provides a response surface methodology-based mathematical model for enhancing the weldability of dissimilar thermoplastic composites with improved mechanical/morphological properties.

Multi-objective optimization of surface roughness, thrust force, and torque produced by novel drill geometries using Taguchi-based GRA

Abstract: A significant part of today’s chip removal processes are drilling holes. Many parameters such as cutting parameters, material, machine tool, and cutting tool, etc., in the hole-drilling process affect performance indicators such as surface roughness, tool wear, force, torque, energy consumption, and costs etc. While cutting parameters are easily planned by the operator during drilling, the selection and planning of the drill geometry are more difficult. In order to design and produce the new drill geometry, a wide time and engineering research are needed. In this study, the design and fabrication of new drill geometry were performed to improve the hole-drilling performance. The performance of the fabricated drills was judged with regard to surface roughness, thrust force, and drilling torque. In the performance tests, four different drill geometries, four different cutting speed levels, and four different feed rate levels were selected. Holes were drilled on AISI 4140 material. In addition, the optimization was performed in two phases. Firstly, the mono-optimization was carried by using Taguchi’s S/N analysis in which each performance output was optimized separately. Secondly, the multi-objective optimization was employed by using Taguchi-based gray relational analysis (GRA). For the purpose of the study, two different drill geometries were designed and fabricated. Experimental results showed that the designed Geometry 4 is superior to other geometries (geometry 1, geometry 2, and geometry 3) in terms of thrust force and surface roughness. However, in terms of drilling torque, geometry 2 gives better results than other drill geometries. It was found that for all geometries, obtained surface roughness values are lower than the surface roughness values expected from a drilling operation and therefore surface qualities (between 1.2 and 2.4 μm) were satisfactory.

Image dehazing using Moore neighborhood-based gradient profile prior

Abstract: Removing the haze from still images is a challenging issue. Dark Channel Prior (DCP) based dehazing techniques have been used to remove haze from still images. However, it produces poor results when image objects are inherently similar to the airlight and no shadow is cast on them. To eliminate this problem, a Moore neighborhood-based gradient profile prior is designed and developed to efficiently estimate the transmission map and atmospheric veil. The transmission map is also refined by developing a local activity-tuned anisotropic diffusion based filter. Afterward, image restoration is performed using the estimated transmission function. Thus, the proposed technique has an ability to remove haze from still images in an effective manner. The performance of the proposed technique is compared with recently developed seven dehazing techniques over synthetic and real-life hazy images. The experimental results depict the supremacy of the proposed technique in removing haze from still images when compared with several existing techniques. It also reveals that the restored image has little or no artifacts.

COBERT: COVID-19 Question Answering System Using BERT.

Abstract: In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively.