Naderi-Boldaji M.,Shahrekord University |
Naderi-Boldaji M.,University of Tehran |
Alimardani R.,University of Tehran |
Hemmat A.,Isfahan University of Technology |
And 5 more authors.
Soil and Tillage Research | Year: 2014
Horizontal penetrometers have been recently examined as instruments for on-the-go mapping of soil strength/compaction. Since the horizontal penetrometer resistance (PR) provides a composite soil strength parameter, it needs to be characterized with respect to variations in soil physical properties as well as standardized with respect to the operational parameters of the device. In a recent study, a 3D finite element (FE) model was developed for a single-tip horizontal penetrometer-soil interaction (cf. Part I of this study; Naderi-Boldaji et al., 2013b) and the effect of some soil/operational parameters (mechanical properties of soil, model boundary effects, penetrometer tip extension, working depth and soil failure mode ahead of the tine) on PR was investigated. In the second part of this study, two soil bin tests were conducted to evaluate the PR predictability of the FE model. This is a crucial step for finite element modelling of soil compaction reflected by PR as affected by soil water content, bulk density and texture. The soil bin tests were carried out in a clay loam soil at two different water contents of 0.171 and 0.183gg-1 and dry bulk densities of 1.64 and 1.59Mgm-3, respectively, to evaluate the model at two different levels of PR. The soil elastic parameters (i.e. Young's modulus of elasticity and Poisson's ratio) were estimated from oedometer (uniaxial compression) tests on confined and unconfined undisturbed samples taken within the working depth of the penetrometer whilst the plastic parameters (the parameters of the Drucker-Prager constitutive model) were determined by triaxial tests at three levels of confining stress. The results indicated the practical and efficient use of oedometer tests for estimating soil elastic parameters for numerical simulations. The FE model predicted the measured PR with a small error (<12%) when modelling the soil as elastic-perfectly plastic material, whilst the prediction error was found to be significantly higher when soil hardening was included. This may suggest that the confinement of soil around the moving cone is different than in a confined compression test. It is concluded that the FE model presented here and the procedures used for estimation of the model input parameters reflected well the change in soil physical conditions of the two tests. Further evaluations are needed to generalize the model predictions across soil types and characterize PR with respect to soil physical properties. © 2014 Elsevier B.V.
Sanghvi P.,University of Illinois at Urbana - Champaign |
Dankowicz H.,University of Illinois at Urbana - Champaign |
Romig B.E.,Moline Technology Innovation Center
Proceedings of the ASME Design Engineering Technical Conference | Year: 2010
This paper presents a lumped-parameter, planar, physical model for capturing steady-state interactions between a deformable tire and a deformable terrain. The model includes effects due to tangential and normal compliance of the terrain surface as well as radial and circumferential compliance of the tire. In addition, shear failure and dry friction limits are introduced through a distinction between regions along the circumferential direction of the tire that are in local stick or slip, respectively. The time history of interactions between individual infinitesimal patches of the tire and the terrain is then described by a hybrid dynamical system, in which changes between individual phases of motion are triggered by characteristic events. The paper further illustrates the application of a non-linear regression technique for identification of the seven model parameters and, in selected cases additional unknown kinematic variables. Specifically, the model is fit to experimental load-deflection, gross-thrust, and net-pull data demonstrating good quantitative agreement. Copyright © 2010 by ASME.
Edwards G.T.C.,University of Aarhus |
Dybro N.,Moline Technology Innovation Center |
Munkholm L.J.,University of Aarhus |
Sorensen C.G.,University of Aarhus
Biosystems Engineering | Year: 2016
Planning agricultural operations requires an understanding of when fields are ready for operations. However determining a field's readiness is a difficult process that can involve large amounts of data and an experienced farm manager. A consequence of this is that operations are often executed when fields are unready, or partially unready, which can compromise results incurring environmental impacts, decreased yield and increased operational costs. In order to assess timeliness of operations' execution, a new scheme is introduced to quantify the aptitude of farm managers to plan operations.Two criteria are presented by which the execution of operations can be evaluated as to their exploitation of a field's readiness window. A dataset containing the execution dates of spring and autumn operations on 93 fields in Iowa, USA, over two years, was considered as an example and used to demonstrate how operations' executions can be evaluated. The execution dates were compared with simulated data to gain a measure of how disparate the actual execution was from the ideal execution.The presented tool is able to evaluate spring operations better than autumn operations as required data was lacking to correctly parameterise the crop model. The evaluation criteria could be used to identify farm managers who require decisional support when planning operations, or as a means of promoting the use of sustainable farming practices. © 2016 IAgrE.
Hess A.,University of Kaiserslautern |
Hess A.,Fraunhofer Institute for Experimental Software Engineering |
Rombach D.,University of Kaiserslautern |
Rombach D.,Fraunhofer Institute for Experimental Software Engineering |
And 4 more authors.
International Journal of Engineering Education | Year: 2013
An integral part of software engineering curricula at universities are practical classes or projects that enable students to apply theoretical knowledge gained in lectures on concrete practical examples. Practical projects, in particular, defined as university-industry collaborations provide the potential of being very beneficial especially in graduate education: in such realistic project settings, students can experience real-life software engineering challenges and achieve learning objectives that go beyond typical learning objectives of practical assignments during classes or even practical projects defined by facultymembers. However, such collaborative projects havetobeplanned carefully and alsocome with various challenges. In this article, authors from academia and industry share their experiences gained during a history of successfully conducted collaborative projects. These experiences comprise objectives, benefits, challenges and lessons learned both from an educational viewpoint (i.e., students, supervisors), research viewpoint (supervisors), and industry viewpoint (customer). The experiences summarized in this article could serve as motivation and valuable information for other universities and industry companies intending to plan and organize collaborative projects of this kind. © 2013 TEMPUS Publications.
Sahay S.S.,John Deere India Pvt Ltd |
Deshmukh V.,John Deere India Pvt Ltd |
El-Zein M.,Moline Technology Innovation Center
Journal of Materials Engineering and Performance | Year: 2013
Industrial carburizing operations have been traditionally simulated for several decades by solving the diffusion equation. However, using this deterministic approach, it is difficult to capture batch-to-batch variations in the properties attributed to process and chemistry variations. In the current study, a probabilistic approach is used to capture the variations in process parameters and alloy chemistry. The advantage of this approach is illustrated through a case study on reduction in distortion variations as well as its absolute value during the carburizing operation. Finally, some of the opportunities and challenges in the carburizing simulation are discussed. © 2013 ASM International.
Bergerman M.,Carnegie Mellon University |
Van Henten E.,Wageningen University |
Billingsley J.,University of Southern Queensland |
Reid J.,Moline Technology Innovation Center |
Mingcong D.,Tokyo University of Agriculture and Technology
IEEE Robotics and Automation Magazine | Year: 2013
The IEEE Robotics and Automation Society Technical Committee (TC) on Agricultural Robotics and Automation was launched in 2012 with the goal of bringing together researchers and practitioners, academic and industrial, in an informal setting to increase knowledge dissemination in the field. The goal for 2013 is to hold at least eight Webinars and for 2014 to hold one every month. The TC members are rewriting the Handbook of Robotics chapter on Agricultural and Forestry Robotics and wrote an invited chapter on Agricultural Robotics on an SAE book on autonomous vehicles. Beyond endowing machines and vehicles with higher levels of intelligence, two long-term challenges, such as humanlike manipulation of crops and harvesting robots, must be addressed before R&A makes a full incursion into agriculture. Membership in the TC is open to all interested in contributing to the exciting field of agricultural robotics and automation.
Maisenbacher S.,TU Munich |
Yassine A.,American University of Beirut |
El-Zein M.,Moline Technology Innovation Center |
Goyal R.,John Deere India Private Ltd |
Maurer M.,TU Munich
Gain Competitive Advantage by Managing Complexity - Proceedings of the 14th International Dependency and Structure Modelling Conference, DSM 2012 | Year: 2012
Changes in welding sequence have high effects on welding distortion and production time. As the effects on cycle time are simply due to the distance travelled by the Heat Source, the influences of welding sequence on distortion are still not clearly defined. The goal of this paper is to build a model of the entire welding process to get deeper insights on the physics of welding and especially on the influence of welding sequence on distortion. DSM and MDM models are used to map and analyse these correlations.