71 research outputs found
Profiling the desirable CAD trainee: Technical background, personality attributes, and learning preferences
No full textThis research aims to explore some of the underlying reasoning for why some individuals acquire mechanical computer-aided design (CAD) skills with relative ease while some others seem to falter. A methodical study was performed by monitoring 74 mechanical engineering seniors (over a 3 year period) in a semester-long formal training on a commercial three-dimensional (3D) CAD package (PRO-ENGINEER, version WILDFIRE). The study methodically explored the trainees' (1) technical background, (2) personality attributes, and (3) learning preferences. Investigating the technical background included quantifying the trainees' following technical foundations: basic math, advanced math, CAD-related math, computer science and engineering, methodologies related to CAD, graphics, and mechanical design. Determining the trainees' personality attributes included exploring their willingness-to-learn CAD, perception, gauging their actual behavior (practice), and CAD syntax learned throughout the training. Trainees' learning preferences were determined according to the index of learning styles (ILS). Furthermore, and in order to assess the trainees' progress in CAD knowledge acquisition, competency tests were conducted at four intervals throughout the semester-long study. The assessment involved hands-on modeling of CAD test parts of comparable complexity. At the conclusion of the study, statistical methods were used to correlate the trainees' attributes with their monitored performance. Only a fraction (17 out of a class of 74 trainees or 1 in 4) of the trainees were found to fit the star CAD trainee mold, which is defined here as someone who is fast on the tube and perceptive enough to see through the procedure of building progressively more sophisticated CAD models. A profile of the star CAD trainee character emerges as an individual who is technically competent, perceptive, and motivated. The study also reveals these most desirable trainees to possess an active, sensor, visual, and sequential learning style. Copyright © 2009 by ASME.Ahmed S, 2007, J MECH DESIGN, V129, P709, DOI 10.1115-1.2723807; Bailey J. L., 2001, P 2001 ACM SIGCPR C, P93, DOI 10.1145-371209.371221; Beitz W., 1990, International Journal of Human-Computer Interaction, V2; Cross TL, 2007, GIFTED CHILD QUART, V51, P285, DOI 10.1177-0016986207302723; DAREL EM, 1995, IIE TRANS, V27, P272, DOI 10.1080-07408179508936741; Felder R. M., INDEX LEARNING STYLE; Felder RM, 2005, INT J ENG EDUC, V21, P103; Field DA, 2004, COMPUT AIDED DESIGN, V36, P1431, DOI 10.1016-j.cad.2003.10.007; Hamade RF, 2009, COMPUT IND ENG, V56, P1510, DOI 10.1016-j.cie.2008.09.025; HAMADE RF, 2006, INT J HUMAN COMPUTER, V19, P305, DOI 64309713,12,1; Hamade RF, 2007, COMPUT EDUC, V49, P640, DOI 10.1016-j.compedu.2005.11.009; HAMADE RF, STUDY IMPACT W UNPUB; Hamade RF, 2010, J ENG DESIGN, V21, P561, DOI 10.1080-09544820802409289; Hamade RF, 2008, COMPUT AIDED DESIGN, V40, P262, DOI 10.1016-j.cad.2007.11.001; Harzallah M, 2006, IEEE T SYST MAN CY A, V36, P187, DOI [10.1109-TSMCA.2005.859093, 10.1109-TSMCA.2006.859093]; Joshi K. D., 2005, P 2005 ACM SIGMIS CP, P32, DOI 10.1145-1055973.1055980; LEE CRJ, 1993, THESIS U ILLINOIS UR; LEE DMS, 1995, MIS QUART, V19, P313, DOI 10.2307-249598; LIEBERMAN MB, 1987, STRATEGIC MANAGE J, V8, P441; MCDERMOTT CM, 1995, IEEE T ENG MANAGE, V42, P410, DOI 10.1109-17.482090; Miller C. L., 1991, ENG DESIGN GRAPHICS, V55, P5; Mupinga D., 2006, COLL TEACHING, V54, P185, DOI DOI 10.3200-CTCH.54.1.185-189; Ostojic SM, 2006, J STRENGTH COND RES, V20, P740; Pektas ST, 2010, INT J TECHNOL DES ED, V20, P63, DOI 10.1007-s10798-008-9060-x; REITER MD, 2007, J COLL STUD DEV, V41, P34; Roberts A, 2006, DESIGN STUD, V27, P167, DOI 10.1016-j.destud.2005.07.001; Robertson BF, 2007, J MECH DESIGN, V129, P753, DOI 10.1115-1.2722329; Mehrabad MS, 2007, COMPUT IND ENG, V53, P306, DOI 10.1016-j.cie.2007.06.023; Sorby S., 2000, J ENG EDUC, V89, P301; Tweed C, 2001, AUTOMAT CONSTR, V10, P617, DOI 10.1016-S0926-5805(00)00064-9; Wright T. P., 1936, J AERONAUT SCI, V3, P122; Ye XZ, 2004, COMPUT AIDED DESIGN, V36, P1451, DOI 10.1016-j.cad.2003.11.006; YULE R, 2002, MACH DES, V74, P9643
A study of the influence of learning style of beginner computer-aided design users on their performance
No full textIn this work, we go about answering the question: does the learning style of beginner computer-aided design (CAD) users have any influence on their CAD competence development? We empirically examine a group of 44 novel trainees as they undergo a semester-long formal training program on mechanical 3D software (Pro-Engineer Wildfire). All the while we continuously monitor the trainees performance progress in learning CAD skills. Furthermore, the trainees are classified according to two popular methods: the learning style questionnaire [Honey, P. and Mumford, A., 1992. The manual of learning styles. 3rd ed. Maidenhead: Peter Honey] and the index of learning styles [Felder, R.M. and Soloman, B.A., 2004. Index of Learning Styles (ILS)[online].Available from: http: www.ncsu.edu-felder-public-ILSpage.html (Accessed 15 February 2006)]. Finally, we report on the statistical correlation between the trainees learning styles and their CAD performance throughout the training and we comment on the various learning styles in relation to their CAD competence building capabilities. © 2010 Taylor and Francis.Baartmans B. J, 1996, ENG DESIGN GRAPHICS, V60, P13; BRIGGS ARJ, 2000, ED TRAINING, V42, P16, DOI 10.1108-00400910010371482; Felder RM, 2005, INT J ENG EDUC, V21, P103; FELDER RM, 2004, INDEX LEARNING STYLE; Felder RM, 2005, J ENG EDUC, V94, P57; Field DA, 2004, COMPUT AIDED DESIGN, V36, P1431, DOI 10.1016-j.cad.2003.10.007; GUSTER D, 1986, J VOCATIONAL ED RES, V11, P25; Hamade RF, 2007, COMPUT EDUC, V49, P640, DOI 10.1016-j.compedu.2005.11.009; Hamade RF, 2005, INT J HUM-COMPUT INT, V19, P305, DOI 10.1207-s15327590ijhc1903_2; HAMADE RF, BEHAV INFOR IN PRESS; Hamade RF, 2008, COMPUT AIDED DESIGN, V40, P262, DOI 10.1016-j.cad.2007.11.001; Hamade R. F., 2007, Journal of European Industrial Training, V31, DOI 10.1108-03090590710734345; James-Gordon Y, 2001, J WORKPLACE LEARNING, V13, P239, DOI 10.1108-EUM0000000005723; Keefe J. W., 1979, STUDENT LEARNING STY, P1; Kolb D., 1984, EXPERIENTIAL LEARNIN; Kolb D. A., 1971, ORG PSYCHOL EXPT APP; LITZINGER TA, 2005, P 2005 AM SOC ENG ED, P9615; MCGEE MG, 1979, PSYCHOL BULL, V86, P889, DOI 10.1037--0033-2909.86.5.889; Miller C. L., 1991, ENG DESIGN GRAPHICS, V55, P5; MILLS J, 2005, LEARNING LEARNING ST; Mumford A., 1992, MANUAL LEARNING STYL; MURPHY M, 2007, CADALYST 0101, P36; MURPHY M, 2006, CADALYST, V23, P38; NOE RA, 1986, ACAD MANAGE REV, V11, P736, DOI 10.2307-258393; ROBOTHAM D, 2003, J EUROPEAN IND TRAIN, V27, P473, DOI 10.1108-03090590310506487; ROWELL JA, 1975, BRIT J EDUC PSYCHOL, V45, P232; SCALES AY, 2000, THESIS N CAROLINA ST; SCALES AY, 2000, ASEE SE SECT C; Sorby S., 2000, J ENG EDUC, V89, P301; Witkins H., 1971, MANUAL EMBEDDED FIGU; Wright T. P., 1936, J AERONAUT SCI, V3, P122; Ye XZ, 2004, COMPUT AIDED DESIGN, V36, P1451, DOI 10.1016-j.cad.2003.11.00653
Numerical simulations of the cathodic delamination of adhesive bonded rubber-steel joints
No full textCathodic delamination of mechanically loaded rubber-steel adhesive bonds occurs due to bondline degradation (weakening) followed by crack growth under mechanical (here, mostly cleavage) load. In this paper, a mechano-chemical failure criterion is proposed, which couples fracture mechanics principles with the weakening mode of debonding due to environmental effects. The latter is mainly described by electrolyte type, cathodic potential, and temperature and may be analytically described according to the recently introduced [1] analytical model based on liquid-solid reactions and is capable of simulating the weakening mode of bond degradation. This paper extends the model advanced in [1] to where we now account for externally applied mechanical loading (mostly peel mode). Such loads cause already weakened bonds to delaminate thus resulting in physical separation of the rubber from the steel substrate. For the rubber-metal, variable-G, strip blister specimen (SBS) used in this work, progressive delamination proceeds as the applied strain energy release rate, G, decreases from an initial maximum value, G T0 (of about 2.24 kJ-m 2 for the most utilized specimen configuration). As the applied G decreases, delamination correspondingly proceeds at progressively slower rates. The fact that delamination rates decrease with increasing delaminated bond lengths has already been established experimentally and simulated using empirical [2] and semi-empirical models [3] but will be simulated numerically in this paper. The model is validated using such experimental data of bond delamination under a variety of cathodic conditions. The validated methodology provides numerical simulations of joint delamination of the SBS under the combined action of mechanical peel loads and cathodic environment. © 2012 Elsevier Ltd. All rights reserved.Allahar KN, 2007, CORROS SCI, V49, P3638, DOI 10.1016-j.corsci.2007.03.024; [Anonymous], 2001, COMSOL MULT FIN EL A; BOERIO FJ, 1989, J ADHESION, V30, P119, DOI 10.1080-00218468908048201; Hamade R. F., 2009, P ASME INT MECH ENG; Hamade RF, 2005, INT J ADHES ADHES, V25, P147, DOI 10.1016-j.ijadhadh.2004.06.002; Hamade RF, 2003, J ADHES SCI TECHNOL, V17, P1235, DOI 10.1163-156856103322114561; Hamade RF, J ADHES SCI IN PRESS; Hamade RF, 2007, INT J ADHES ADHES, V27, P108, DOI 10.1016-j.ijadhadh.2006.01.002; Hamade RF, 2008, J ADHES SCI TECHNOL, V22, P775, DOI 10.1163-156856108X295374; Hamade RF, 2009, J ADHES SCI TECHNOL, V23, P579, DOI 10.1163-156856108X379128; HAMADEH RF, 1988, J ADHES SCI TECHNOL, V2, P77, DOI 10.1163-156856188X00093; ISHIDA M, 1968, AICHE J, V14, P311, DOI 10.1002-aic.690140218; KOZINSKI SE, 1990, J ADHES SCI TECHNOL, V4, P131, DOI 10.1163-156856190X00153; LIECHTI KM, 1989, INT J FRACTURE, V39, P217, DOI 10.1007-BF000474510
EPA proposed RfDs in perspective
No full textAli K. Hamade, PhD, DABT.This archived document is maintained by the State Library of Oregon as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Includes bibliographical references (pages 20-35).Mode of access: Internet from the Oregon Government Publications Collection.Text in English
Modeling cathodic weakening of rubber-steel adhesive bonds as liquid-solid reactions
No full textIn order to predict the loss of adhesion in adhesive bonded joints under cathodic conditions, empirical and semi-empirical approaches were previously implemented by Hamade and coworkers. In this paper, a method is presented to estimate bond weakening progress via numerical simulations after being modeled as a liquid-solid chemical reactor. The diffusion and chemical reaction mechanisms involved in bond weakening are mathematically represented via a simplified, 2 partial differential equations (p.d.e.) boundary value problem (BVP) which is a reduced version of the more complex electrochemical formulation needed to fully describe the chemistry at the bondline under cathodic conditions. The model presented is analytical-empirical hybrid formulation solvable by numerical methods and is made possible by the empirical knowledge developed previously by Hamade and coworkers regarding the dependence of the diffusion and chemical reaction parameters on cathodic conditions. The findings support that weakening is governed by a chemical reaction-controlled mechanism at relatively short distances and by a diffusion-controlled mechanism as the degraded front propagates. The numerical model is validated using experimental weakening data collected previously.The numerical solutions provide estimates of the life of the bonded joint (weakened length vs. time) asfunction of cathodic parameters. Koninklijke Brill NV, Leiden, 2012 © 2012 Copyright Taylor and Francis Group, LLC.BOERIO FJ, 1989, J ADHESION, V30, P119, DOI 10.1080-00218468908048201; Furbeth W, 2000, PROG ORG COAT, V39, P23, DOI 10.1016-S0300-9440(00)00095-3; Hamade RF, 2007, INT J ADHES ADHES, V27, P108, DOI 10.1016-j.ijadhadh.2006.01.002; Hamade RF, 2008, J ADHES SCI TECHNOL, V22, P775, DOI 10.1163-156856108X295374; HAMADEH RF, 1989, J ADHES SCI TECHNOL, V3, P421, DOI 10.1163-156856189X00317; ISHIDA M, 1968, AICHE J, V14, P311, DOI 10.1002-aic.690140218; KOZINSKI SE, 1990, J ADHES SCI TECHNOL, V4, P131, DOI 10.1163-156856190X00153; Leidheiser H. Jr., 1987, Journal of Adhesion Science and Technology, V1, DOI 10.1163-156856187X00094; Levenspiel O, 1999, CHEM REACTION ENG; Moiseev Y. V., 1987, CHEM RESISTANCE POLY, P180; TURNBULL A, 1986, CORROS SCI, V26, P601, DOI 10.1016-0010-938X(86)90027-2; Vetter K. J., 1976, ELECTROCHEMICAL KINE; WEN CY, 1968, IND ENG CHEM, V60, P34, DOI 10.1021-ie50705a0070
A semi-empirical model for the cathodic delamination of elastomer-to-metal adhesive joints
No full textWe have developed a semi-empirical model capable of quantitatively estimating delamination rates (DRs) of adhesive bonds upon exposure to cathodic environments. The equations used in this model are derived from physical considerations that account for variables which influence the DRs: cathodic voltage (implicitly current density), temperature and peel stress (utilizing strain energy release rate, G, as a fracture mechanics parameter). In the model equations, the constants are determined by fitting these equations to experimentally collected delamination data. Nomograms are developed that can be easily consulted in order to determine DRs under a multitude of cathodic polarization, temperature, and strain energy release rate, G. This semiempirical model can serve as a first-order predictor of cathodic delamination rates of adhesive bonds under a variety of cathodic conditions (including some which are highly accelerated). © 2008 VSP.BUSSA SL, 1972, MATER RES STANDARD, V12, P31; CURLEY AJ, 2000, INT J FRACTURE, V41, P103; Hamade RF, 2005, INT J ADHES ADHES, V25, P147, DOI 10.1016-j.ijadhadh.2004.06.002; Hamade R. F., 2004, SILANES OTHER COUPLI, V3, P69; Hamade RF, 2003, J ADHES SCI TECHNOL, V17, P1235, DOI 10.1163-156856103322114561; Hamade RF, 2007, INT J ADHES ADHES, V27, P108, DOI 10.1016-j.ijadhadh.2006.01.002; HAMMOND JS, 1981, CORROS SCI, V21, P239, DOI 10.1016-0010-938X(81)90033-0; Leidheiser H. Jr., 1987, Journal of Adhesion Science and Technology, V1, DOI 10.1163-156856187X00094; Levi D. W., 1977, J APPL POLYM SCI, V32, P189; LIECHTI KM, 1989, INT J FRACTURE, V39, P217, DOI 10.1007-BF00047451; *MATHWORKS INC, 3 APPL HILL DRIV; Stevenson A., 1985, Intl Adhes. Adhes., V5, P81, DOI 10.1016-0143-7496(85)90020-X; STEVENSON A, 1987, J ADHESION, V21, P313, DOI 10.1080-00218468708074978; Vetter K. J., 1976, ELECTROCHEMICAL KINE88
A methodology for the optimization of PCD compact core drilling in basalt rock
No full textThis work presents an optimization technique using genetic algorithm for efficient core drilling in basalt rock. Optimization of the compact core-drilling problem is based on maximizing a desirability function which accounts for (a) maximizing the drilling feed while minimizing tool-wear progression, (b) minimizing the thrust force and torque (power), and (c) satisfying realistic constraints related to process parameters. The resulting set of optimized cutting parameters is sought in order to make the tool last longer while effectively drilling with high productivity. A room temperature model to relate the experimental data on changing drill forces and torques required by the progressive tool wear, and developed in a previous paper, is used in this study. © 2011 Springer-Verlag London Limited.Ali-Tavoli M, 2006, MACH SCI TECHNOL, V10, P491, DOI 10.1080-10910340600996126; Bourgoyne Jr A.T., 1974, T AIME SOC PET ENG J, V257, P371; Choudhury SK, 2000, INT J MACH TOOL MANU, V40, P899, DOI 10.1016-S0890-6955(99)00088-7; Clayton R, 2005, SPE IADC DRILL C P A; Fergason RL, 2006, J GEOPHYS RES-PLANET, V111, DOI 10.1029-2005JE002583; Fernandes M, 2006, INT J MACH TOOL MANU, V46, P76, DOI 10.1016-j.ijmachtools.2005.03.016; GLOWKA DA, 1989, J PETROL TECHNOL, V41, P850; GLOWKA DA, 1989, J PETROL TECHNOL, V41, P797; Hamade R. F., 2009, P ASME INT MECH ENG; Hamade RF, 2008, CIRP ICME 08 NAPL IT; Hamade RF, 2010, J MATER PROCESS TECH, V210, P1326, DOI 10.1016-j.jmatprotec.2010.03.023; Hamade RF, 2006, INT J MACH TOOL MANU, V46, P387, DOI 10.1016-j.ijmachtools.2005.05.016; Holland John Henry, 1992, ADAPTATION NATURAL A; Judzis A, 2009, SPE DRILL COMPLETION, V24, P25; Lia ZC, 2005, INT J MACH TOOL MANU, V45, P1402; MALAKOOTI B, 1989, OPER RES, V37, P805, DOI 10.1287-opre.37.5.805; Myers RH, 1995, PROBABILITY STAT SER; Plinninger RJ, 2004, EUROCK 2004 53 GEOM; Plinninger RJ, 2002, 9 P C INT ASS ENG GE, P2226; Sardinas RQ, 2006, ENG APPL ARTIF INTEL, V19, P127, DOI 10.1016-j.engappai.2005.06.007; Tulu IB, 2008, 42 US ROCK MECH 2 US; VENET V, 1993, REV I FR PETROL, V48, P15; Wang X, 2005, INT J PROD RES, V43, P3543, DOI 10.1080-13629390500124465; Weaver GE, 2000, US Patent, Patent No. 6109368; WOJTANOWICZ AK, 1993, J ENERG RESOUR-ASME, V115, P247, DOI 10.1115-1.2906429; Yinghui Liu, 2007, Journal of Manufacturing Science and Engineering, V129, DOI 10.1115-1.2515345; Zacny KA, 2004, J GEOPHYS RES-PLANET, V109, DOI 10.1029-2003JE002204; Zhang JY, 2006, J INTELL MANUF, V17, P203, DOI 10.1007-s10845-005-6637-z11
Cathodic delamination of elastomer-to-metal adhesive joints: Experimental data and empirical modeling
No full textIn order to estimate the cathodic delamination rates of metal adhesive-steel adhesive bonds, an empirical model based on crack growth is developed. To this end, a considerable amount of delamination data was experimentally collected under several levels of cathodic environmental harshness (some highly accelerated) and applied peel stress. For a given electrolyte, cathodic delamination was found to depend on cathodic voltage (or current density), temperature, and applied strain energy release rate, G. In order to describe the progress of delamination under various cathodic environments as a function of applied G, the collected, experimental delamination-distance-versus-time data were used to develop characteristic curves of log delamination rates versus log G. For all conditions tested, these log-log plots were dominated by linear regions (commonly referred to as Region II) that follow the Paris law. In this region, it was found that bond delamination rates vary dramatically depending on the environment. Furthermore, and unlike with the critical fracture toughness (Gc), the threshold value of G (Gth) for the degraded bond varies from one set of cathodic conditions to another. To model delamination rates as function of the cathodic environment and G, a nonlinear equation capable of modeling subcritical crack growth behavior was used. The experimental delamination data was fitted to this equation in order to determine the coefficients of this equation. For each environment tested, one equation was determined and which describes the bond delamination rate versus G behavior over the entire G-range. Consequently, these coefficients were consolidated into master, easy-to-use, empirical equations that relate delamination rates to G. The resulting empirical model is applicable over a wide range of cathodic conditions of voltage and temperature. © 2006 Elsevier Ltd. All rights reserved.Curley AJ, 1998, J ADHESION, V66, P39, DOI 10.1080-00218469808009959; CURLEY AJ, 2000, INT J FRACTURE, V41, P103; CUTTS E, 1981, DEV ADHESIVES, P367; GUO S, 2003, THESIS VIRGINIA TECH; Hadavinia H, 2003, INT J ADHES ADHES, V23, P449, DOI 10.1016-S0143-7496(03)00074-5; Hamade RF, 2005, INT J ADHES ADHES, V25, P147, DOI 10.1016-j.ijadhadh.2004.06.002; Hamade R. F., 2004, SILANES OTHER COUPLI, V3, P69; Hamade RF, 2003, J ADHES SCI TECHNOL, V17, P1235, DOI 10.1163-156856103322114561; HAMMOND JS, 1981, CORROS SCI, V21, P239, DOI 10.1016-0010-938X(81)90033-0; HERTZBERG RW, 1976, DEFORMATION FRACTURE, P439; KOEHLER EL, 1984, CORROSION, V40, P5; LEFEBVRE DR, 1988, EXP MECH, V28, P38, DOI 10.1007-BF02328994; LEIDHEISER H, 1981, IND ENG CHEM PROD RD, V20, P547, DOI 10.1021-i300003a024; Leidheiser H. Jr., 1987, Journal of Adhesion Science and Technology, V1, DOI 10.1163-156856187X00094; LIECHTI KM, 1989, INT J FRACTURE, V39, P217, DOI 10.1007-BF00047451; LIECHTI KM, 1985, ADHESIVE SYSTEMS STR; Miller M. A., 1985, THESIS U CINCINNATI; Paris P, 1963, T ASME D, V85, P528; PENG SJ, 1984, AFRPLTR84036; Stevenson A., 1985, Intl Adhes. Adhes., V5, P81, DOI 10.1016-0143-7496(85)90020-X; STEVENSON A, 1987, J ADHESION, V21, P313, DOI 10.1080-0021846870807497899
A study of the impact of the willingness-to-learn of CAD novice users on their competence development
No full textThe notion that 'attitude drives behavior' manifests itself in a variety of ways in educational and occupational settings. As applied to CAD competence development, industrial training of novice CAD users on their way to becoming competent CAD users consume a lot of corporate resources. This paper is the third paper in the line of research that attempts to answer the question having to do with what it takes to make a competent CAD user. Specifically, we examine the CAD-specific factors revolving around the trainees' willingness-to-learn CAD. These factors are analyzed in two stages. At the start of the training, trainees' initial attitude towards CAD is established by means of a short questionnaire. Afterwards, throughout the training, trainees' behavior (online and offline practice) is gauged and, in turn, a relation is established to illustrate how this practice leads to the development of CAD-specific skills. For this purpose, another short questionnaire was utilized. Strong correlations were established relating the trainees' CAD-specific behavior with the CAD-specific outcomes of learning CAD syntax. Furthermore, and in order to assess the quality of the trainees' learning of CAD, overall competence was monitored throughout the study via performance measures that describe the time it took the trainees to build test models (speed), which reflects upon the ability to learn the syntax of the CAD tool (declarative knowledge). The sophistication of the models is also used as another measure. Correlating the trainees' character attributes with these assessed measures, it was found that the stronger is the trainees' will to learn CAD, the stronger is the likelihood to learn faster. Perhaps more importantly, trainees with initial favorable attitude toward CAD were shown to develop increasingly positive behavior that manifested through additional practice and other forms of visible effort. © 2011 Elsevier Ltd. All rights reserved.Ajzen I., 1988, ATTITUDES PERSONALIT; Barrett JH, 2005, INT J IND ERGONOM, V35, P871, DOI 10.1016-j.ergon.2004.12.004; Colquitt JA, 2000, J APPL PSYCHOL, V85, P678, DOI 10.1037--0021-9010.85.5.678; Hamade RF, 2009, COMPUT IND ENG, V56, P1510, DOI 10.1016-j.cie.2008.09.025; Hamade RF, 2007, COMPUT EDUC, V49, P640, DOI 10.1016-j.compedu.2005.11.009; Hamade RF, 2005, INT J HUM-COMPUT INT, V19, P305, DOI 10.1207-s15327590ijhc1903_2; Hamade RF, 2010, J ENG DESIGN, V21, P561, DOI 10.1080-09544820802409289; Hamade RF, 2008, COMPUT AIDED DESIGN, V40, P262, DOI 10.1016-j.cad.2007.11.001; Jaber M.Y., 2011, LEARNING CURVES THEO; MCDERMOTT CM, 1995, IEEE T ENG MANAGE, V42, P410, DOI 10.1109-17.482090; MCGRANN RTR, 2006, J EDUC TECHNOL SYST, V34, P165, DOI 10.2190-2B89-MRNQ-WD57-EU48; MURPHY M, 2007, CADALYST 0101, P36; NOE RA, 1986, ACAD MANAGE REV, V11, P736, DOI 10.2307-258393; Robinson G, 2007, COMPUT AIDED DESIGN, V39, P245, DOI 10.1016-j.cad.2006.12.001; Salas E, 2001, ANNU REV PSYCHOL, V52, P471, DOI 10.1146-annurev.psych.52.1.471; Schumann J., 1996, P SIGCHI C HUM FACT, P35, DOI DOI 10.1145-238386.238398; VANDERHEIDEN GH, 1984, P 21 C DES AUT DAC 8, P220; Wright T. P., 1936, J AERONAUT SCI, V3, P12230
A methodology for predicting the durability of adhesively bonded joints in cathodic environments
No full textThis paper represents a concise summary of several published studies by the author and coworkers (1-7) on the topic of debonding of adhesively bonded (elastomer-to-steel) joints under cathodic conditions. Together, these studies amount to a verified methodology for quantitatively predicting the debonding of adhesive bonded joints in cathodic environments. Based on extensive experimental debond data collected by the author and coworkers, mathematical models are developed that are capable of predicting the life of adhesive bonds under cathodic conditions (some being highly accelerated) of varying voltage, temperature, and applied stress. Copyright © 2010 by ASME
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