2011
DOI: 10.1016/j.optlastec.2010.05.008
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Weld-bead profile and costs optimisation of the CO2 dissimilar laser welding process of low carbon steel and austenitic steel AISI316

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Cited by 88 publications
(34 citation statements)
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“…A step-wise regression method was used to fit the second order polynomial Eq. 1 to the experimental data and to find the significant model terms [23,24]. The same statistical software was used to generate the statistical and response plots as well as the optimization.…”
Section: Experimental Designmentioning
confidence: 99%
“…A step-wise regression method was used to fit the second order polynomial Eq. 1 to the experimental data and to find the significant model terms [23,24]. The same statistical software was used to generate the statistical and response plots as well as the optimization.…”
Section: Experimental Designmentioning
confidence: 99%
“…They stated that weld bead dimensions were affected by the level of heat input. Ruggiero et al [23] and Olabi et al [24] showed the effects of the process parameters on the weld geometry and operating cost for austenitic steel and low carbon steel. The authors developed models and stated that, in terms of weld bead dimensions, the most influential parameter was welding speed.…”
Section: Introductionmentioning
confidence: 99%
“…Accordingly, a DOE approach has been selected to be implemented in this work, in particular by adopting the Taguchi's methodology with two level factorial design, which have the lower number of runs to study a multifactor and multi-responses process such as the laser cutting. Unfortunately, the two-level FD involve some restrictions given that the quadratic effect cannot be determined using (it is a screen design); to the same extent some of the interactions between the factors affecting the process cannot be determined using Taguchi methodology due to the aliased structures, which means not all the interaction effects can be estimated [28]. Conversely, RSM offers interesting features in analyzing the relationship and the influences of input machining parameters on the responses [24,25,29], making possible to find out all the factor's effects and their interactions.…”
Section: The Experimental Designmentioning
confidence: 99%