Seal strength models for medical device trays

• Main Author: Mays, Patricia Faye
• Other Authors: Malave, Cesar O.
• Format: Others
• Language: en_US
• Published: 2010
• Subjects: medical device; trays; burst; peel; testing; package; seal strength; sealing; burst test; peel test; models
• Online Access: http://hdl.handle.net/1969.1/ETD-TAMU-2756; http://hdl.handle.net/1969.1/ETD-TAMU-2756

Seven empirical equations were developed for the prediction of seal strength for medical device trays. A new methodology was developed and used for identifying burst and peel locations and comparing burst pressure and peel force. Multiple linear regression was used to fit 76 models, selecting the best models based on the Akaike Information Criterion (AIC) and adjusted R2 (R2 adj) value of each model. The selected models have R2 adj and prediction R2 (R2 pred) values of .83 to .94. Factors investigated for the peel force response were sealing pressure (3 levels), dwell time (3 levels), sealing temperature (3 levels), and adhesive. Additional factors investigated for the burst pressure response were restraining plate gap, and tray volume, height, length-to-width ratio and area. Polyethylene terephthalate-glycol (PETG) trays with Tyvek 1073B lids and two popular water-based adhesives were used. Trays were selected to yield three levels of area and three levels of length-to-width ratio, defining nine package configurations. Packages for burst testing were sealed under a fractional factorial design with 27 treatments. Packages for peel testing were sealed under a 17-point face-centered central composite design. Packages were tested using peel testing following the ASTM F88-07 standard and restrained burst testing with three gap distances following the ASTM F2054-00 standard. All possible subsets of the factors were evaluated, with the best models selected based on AIC value. Equations were developed to predict peak and average peel force based on sealing process parameters (R2 pred =.94 and .92), burst pressure based on tray and sealing parameters and gap (R2 pred =.94), and four peel force responses based on burst pressure and gap (R2 pred =.83 to .86). Models were validated through cross-validation, using the prediction error sum of squares (PRESS) statistic. The R2 pred was calculated to estimate the predictive ability of each model.