Publications with Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access Temperature effects on grinding residual stress(Elsevier, 2014) Fergani, Omar; Shao, Yamin; Liang, Steven Y.; Department of Mechanical Engineering; Lazoğlu, İsmail; Faculty Member; Department of Mechanical Engineering; College of Engineering; 179391Residual stress is a key factor that influences the reliability, precision, and life of final products. Earlier studies have alluded to the fact that the grinding process is usually the source of a tensile residual stress on the part surface, while there exists a temperature level commonly referred to as the onset tensile temperature beyond which the tensile profile of residual stresses starts to be generated. In this paper, a physics-based model is proposed to predict the onset temperature as a function of residual stress on an analytical and quantitative basis. The predictive model is based on the temperature distribution function using a moving heat source approach. Then, the thermal stresses are calculated analytically using Timoshenko thermal stress theory [1] followed by an elastic-plastic relaxation condition imposed on these stresses, thus leading to the resulting residual stresses. The model-predicted results have been experimentally validated using data of the grinding of AISI52100 hardened steel with subsequent X-ray and Neutron diffraction measurements. The model was shown to predict the residual stress profile under given process conditions and material properties, therefore providing an analytical tool for grinding process planning and optimization based on the understanding of onset tensile temperature for control of tensile residual stresses. (C) 2014 Elsevier B.V.Publication Open Access Machining forces and tool deflections in micro milling(Elsevier, 2013) Department of Mechanical Engineering; Mamedov, Ali; Khavidaki, Sayed Ehsan Layegh; Lazoğlu, İsmail; Researcher; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 179391The analysis of cutting forces plays an important role for investigation of mechanics and dynamics of cutting process. The importance of force analysis is due to its major role in surface quality of machined parts. Presented force model calculates instantaneous chip thickness by considering trajectory of the tool tip while tool rotates and moves ahead continuously. The model also takes plowing force component into consideration relating it to elastic recovery based on interference volume between tool and workpiece. Based on the mathematical model, distribution of the force acting on the tool is calculated. It is known that this force will create deflection of the tool during cutting, which will result in imperfections of the final part. From this point of view, it is important to predict tool deflections in order to control the cutting process and to avoid failure of the tool. Both force and deflection models are validated on Aerospace Aluminum Alloy (Al-7050), through micro end milling experiments for a wide range of cutting conditions using micro dynamometer and laser displacement sensors. (C) 2013 The Authors. Published by Elsevier B.V.Publication Open Access Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation(BioMed Central, 2016) Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data. (ii) Gene set analysis methods are used to find gene sets on which phenotypes are linearly dependent by bringing prior biological knowledge into the analysis, which may not capture more complex nonlinear dependencies. Thus, formulating an integrated model of gene set analysis and nonlinear predictive modeling is of great practical importance. In this study, we propose a Bayesian binary classification framework to integrate gene set analysis and nonlinear predictive modeling. We then generalize this formulation to multitask learning setting to model multiple related datasets conjointly. Our main novelty is the probabilistic nonlinear formulation that enables us to robustly capture nonlinear dependencies between genomic data and phenotype even with small sample sizes. We demonstrate the performance of our algorithms using repeated random subsampling validation experiments on two cancer and two tuberculosis datasets by predicting important disease phenotypes from genome-wide gene expression data. We are able to obtain comparable or even better predictive performance than a baseline Bayesian nonlinear algorithm and to identify sparse sets of relevant genes and gene sets on all datasets. We also show that our multitask learning formulation enables us to further improve the generalization performance and to better understand biological processes behind disease phenotypes.Publication Open Access A model-based heuristic to the min max K-arc routing for connectivity problem(Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2014) Akbari, Vahid; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We consider the post-disaster road clearing problem with the goal of restoring network connectivity in shortest time. Given a set of blocked edges in the road network, teams positioned at depot nodes are dispatched to open a subset of them that reconnects the network. After a team finishes working on an edge, others can traverse it. The problem is to find coordinated routes for the teams. We generate a feasible solution using a constructive heuristic algorithm after solving a relaxed mixed integer program. In almost 70 percent of the instances generated both randomly and from Istanbul data, the relaxation solution turned out to be feasible, i.e. optimal for the original problem.Publication Open Access A new identification method of specific cutting coefficients for ball end milling(Elsevier, 2014) Department of Mechanical Engineering; Khavidaki, Sayed Ehsan Layegh; Lazoğlu, İsmail; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391The paper presents a new and accurate strategy for estimation of cutting coefficients for ball-end milling of free form surfaces in 3- and 5-axis operations. Since the cutting coefficients are not constant along the tool axis in the ball part of the cutter, the tool is considered by dividing the ball region into thin disks. In order to find the contribution of each disk to resultant cutting force, an experimental setup is designed to cut the workpiece while only that disk is in engaged with the workpiece. It is shown that this method is more efficient than common methods of mechanistic identification of cutting constants that are available in literature. The derivations are improved by considering the helix angle and cutting edge length to enhance the accuracy of the estimated cutting coefficients. Validation of the proposed strategy is demonstrated experimentally by simulation of cutting forces and comparing the results with conventional methods of identification of cutting coefficients that have been proposed in the literature. (C) 2014 Elsevier B.V.