MODELING OF THE PEEL AND TACK VALUES OF PSAS USING PROBE TEST MEASUREMENTS

Pressure-sensitive-adhesives (PSAs) are increasingly popular for fastening applications due to their safe and easy handling. With the increase in the number and variety of applications come more demanding requirements in terms of properties. In order to speed up the optimization of the properties needed for a specific application, it will become more and more important to develop a good test for the quick screening of a large number of materials, in order to select the most promising ones [1]. Initially, the instrumented probe tack test has been developed to gain more physical insight in the mechanisms of debonding of PSAs [2] [3]. Most advances came from the very detailed information obtained from the probe test, which provides an entire stress-strain curve and not a unique value, coupled with an in situ observation of the deformation mechanisms of the adhesive layer with a video camera [4] [5]. Despite its advantages, such as speed of execution and reproducibility [6], the probe test is not yet widely accepted in industry where adhesive properties are still typically tested with standardized industry tests closer to applications, such as loop tack, peel or shear tests [7]. It would therefore be economically advantageous to be able to predict the result of a peel test, of a loop tack test or of a shear test from a simple probe test experiment which typically lasts less than a minute. However, the important conceptual advances that the more fundamental investigations brought did not yet result in an easy correlation between the outcome of the probe test (a curve of stress as a function of strain) and a value such as peel force, loop tack or shear. Here, we explore a different but parallel approach to predict the tack or the peel value from a probe test curve. This approach is based on the assumption that the information about the tack and the peel is contained in the probe test curve, but needs to be extracted with statistical tools. Indeed, the sensitivity of the test to changes in chemical structure and in formulation has been shown in several publications [2]. The methodology we use has been developed to extract the most significant part of the data to build a predictive model of the desired property [8] [9]. Experimental