Application of design of experiments to welding process of food packaging

Abstract

HRON JAN, MACÁK TOMÁŠ: Application of design of experiments to welding process of food packaging. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2013, LXI, No. 4, pp. 909-915 Design of experiments is one of the many problem-solving quality tools that can be used for various investigations such as fi nding the signifi cant factors in a process, the eff ect of each factor on the outcome, the variance in the process, troubleshooting the machine problems, screening the parameters, and modeling the processes. The objectives of the experiment in this study are twofold. The fi rst objective is to identify the parameters of food packaging welding, which infl uence the response strength of a weld. The second objective is to identify the process parameters that aff ect the variability in the weld strength. The results of the experiment have stimulated the engineering team within the company to extend the applications of DOE in other core processes for performance improvement and variability reduction activities.food packaging, welding process, 2 k full factorial design, optimization, interaction in processes Experimental methods are widely used in research as well as in industrial settings, however, sometimes for very diff erent purposes. The primary goal in scientifi c research is usually to show the statistical signifi cance of an eff ect that a particular factor exerts on the dependent variable of interest. In many cases, it is suffi cient to consider the factors aff ecting the production process at two levels. For example, the temperature for a chemical process may either be set a little higher or a little lower, the amount of solvent in a dyestuff manufacturing process can either be slightly increased or decreased, etc. The experimenter would like to determine whether any of these changes aff ect the results of the production process. The most intuitive approach to study those factors would be to vary the factors of interest in a full factorial design, that is, to try all possible combinations of settings. This would work fi ne, except that the number of necessary runs in the experiment (observations) will increase geometrically. For example, if you want to study 7 factors, the necessary number of runs in the experiment would be 2**7 = 128. To study 10 factors you would need 2**10 = 1,024 runs in the experiment. Because each run may require time-consuming and costly setting and resetting of machinery, it is o en not feasible to require that many diff erent production runs for the experiment. In these conditions, we have two ways how to reduce experimental trals (and also time a cost of them). The fi rst ways is based on fractional factorials are used that "sacrifi ce" interaction eff ects so that main eff ects may still be computed correctly. The second way is based on previous screening of factors for selecting the signifi cant ones. For this purpose we can use the Analysis of ariance (ANOVA) or graphic tools (for example Normal plot of the standardized eff ect or Pareto chart).In...

Study of the mechanical behavior of gamma-irradiated single-use bag seals

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Multicriteria evaluation and optimization of the ultrasonic sealing performance based on design of experiments and response surface methodology

“…As an answer to this shortcoming, a few authors introduced the statistical concepts of "design of experiments" (DOE) and "response surface modelling" (RSM) in the field of heat conductive sealing. [16][17][18] These concepts allow for a detailed analysis of the effect of the sealing parameters, provide the capability of predicting a response of interest, and this based on an efficient use of resources (workload, time, and film material). 16 The purpose of DOE is to set up an experiment in such a way that insight in the effect of a set of input parameters on a certain response can be gained based on a limited number of carefully selected experimental runs.…”

“…Moreover, it does not result in a complete picture of the effect of all parameters on the seal quality and therefore does not allow for optimization. As an answer to this shortcoming, a few authors introduced the statistical concepts of “design of experiments” (DOE) and “response surface modelling” (RSM) in the field of heat conductive sealing . These concepts allow for a detailed analysis of the effect of the sealing parameters, provide the capability of predicting a response of interest, and this based on an efficient use of resources (workload, time, and film material) .…”

“…They stated that an important characteristic of a good heat seal film is a broad seal window . Finally, Hron and Macák used a 2 k factorial design in which two levels (low and high) of each of the sealing parameters were considered to perform a screening of the effects of the parameter settings on the seal strength . In all three of these studies, heat conductive sealing was considered, with the seal/peel strength as a single response parameter, and no optimization was performed.…”