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Failure of Automotive Steels Resistance Spot Welds under Mode I
, M.Sc. Thesis Sharif University of Technology ; Pouranvari, Majid (Supervisor)
Abstract
Automotive steels are dominant material for the manufacturing of automotive structures and components. Since an automotive body is mostly assembled by spot welding, spot weld failure in different loading conditions has a great influence on the crashworthiness of vehicle. Therefore, investigation of microstructure and failure behavior of resistance spot welded automotive steels is an important issue. The first part of the research is dedicated to microstructural evolution and fusion zone hardness of spot welded automotive steels based on optical and SEM micrographs and hardness measurements. In the FZ of automotive steels, except for austenitic steels, a mainly martensitic microstructure was...
Optimizing a Flowshop Model with the Objective of Minimizing Total Weighted Tardiness while Considering due Dates on All Machines
, M.Sc. Thesis Sharif University of Technology ; Ghassemi Tari, Farhad (Supervisor)
Abstract
Minimizing completion time of tasks in a flowshops has always attracted attention of researchers since it was introduced by Johnson, 1954. If all jobs are processed in the same order, the sequence is called a permutation sequence. Tardiness factor is one of important factors in permutation flowshop sequencing problem. Minimizing this factor brings about increase in service level of the shop and is helpful in meeting customer’s needs. Since, minimizing tardiness in PFSP in among NP-hard problems, computational efforts in this area has focused on heuristic approaches. However, the researches has always considered due dates only on the last machine. It means, always it has been assumed that due...
Deep learning for caries detection: A systematic review
, Article Journal of Dentistry ; Volume 122 , 2022 ; 03005712 (ISSN) ; Motamedian, S. R ; Rohban, M. H ; Krois, J ; Uribe, S. E ; Mahmoudinia, E ; Rokhshad, R ; Nadimi, M ; Schwendicke, F ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Objectives: Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection. Data: We selected diagnostic accuracy studies that used deep learning models on dental imagery (including radiographs, photographs, optical coherence tomography images, near-infrared light transillumination images). The latest version of the quality assessment tool for diagnostic accuracy studies (QUADAS-2) tool was used for risk of bias assessment. Meta-analysis was not performed due to heterogeneity in the studies methods and their performance measurements....
Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study
, Article Korean Journal of Orthodontics ; Volume 52, Issue 2 , 2022 , Pages 112-122 ; 22347518 (ISSN) ; Motamadian, S. R ; Nadimi, M ; Hassanzadeh Samani, S ; Minabi, M. A. S ; Mahmoudinia, E ; Lee, V. Y ; Rohban, M. H ; Sharif University of Technology
Korean Association of Orthodontists
2022
Abstract
Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two...