Comparison of different approaches to manufacturing process optimization

Abstract

Abstract-There are many process variables, which more or less influence quality of a manufacturing process, and there are many parameters of a product, which constitute its quality. The control of variables of a manufacturing process for achievement of requested quality of a final product is a multi-parametric task. It is paid great effort to find such the way, which might lead to the most rapid solution. The use of full factorial experiments, fractional factorial experiments and Taguchi approach were compared from the point of view of number of experiments requested. It was found that the Taguchi orthogonal arrays are the most efficient method for higher number of process factors and that the full factorial experiments request the highest number of experiments from all methods under test.

Thermal treatment process of PET bottles filaments using industrial design of experiments

“…Staiculescu et al (2005), Mach and Barto (2010) and Antony et al (2020) reinforce that DoE purposely changes the input variables of the system under study so that the quality controller or person responsible for the experiment can observe and identify the reasons for the changes in the output response. Therefore, Mach and Barto (2010) express that the experimental design is a critically important engineering tool for improving the quality of the manufacturing process.…”

“…In this work, we selected the DoE because it is an effective optimization methodology and has been widely used for product and process optimization projects ((Lundikvist et al, 2020;Castello et al, 2020;Abt et al, 2018;P€ otters et al, 2018;Bergquist, 2015;Zhen et al, 2009, Montgomery, 1999. Staiculescu et al (2005), Mach and Barto (2010) and Antony et al (2020) reinforce that DoE purposely changes the input variables of the system under study so that the quality controller or person responsible for the experiment can observe and identify the reasons for the changes in the output response. Therefore, Mach and Barto (2010) express that the experimental design is a critically important engineering tool for improving the quality of the manufacturing process.…”

“…According to Mach and Barto (2010), Muthukumar and Mohan (2004), Saldanha (2008), Staiculescu et al (2005) and Pontes et al (2011), DoE is a statistical method and a critically important engineering tool for quality improvements in the manufacturing process and to identify optima of product yield and productivity (Mandenius and Brundin, 2008). Muthukumar and Mohan (2004) add that the DoE can be used for the construction and exploration of models and optimization of systems with multiple variables.…”

“…DoE, which has been widely applied to robust design and optimization of products and/or industrial process, is an effective method for optimizing product design (Lundikvist et al, 2020;Castello et al, 2020;Abt et al, 2018;P€ otters et al, 2018;Bergquist, 2015;Montgomery, 2013;Zhen et al, 2009). DoE is a statistical tool, in which experiments are carried out based on changes in the input of a given process, analyzing the effects on the output and the results of this analysis lead to product or process improvement (Antony et al, 2020;Mach and Barto, 2010;Al-Ghandi, 2013;Angrisani et al, 2011;Montgomery, 2013).…”

Making Cognitive Ergonomics in the Human–Computer Interaction of Manufacturing Execution Systems Assessable: Experimental and Validation Approaches to Closing Research Gaps

“…Design of Experiments (DoE) is an approach for changing smaller characteristics of an existing process and trying to analyze the effects of these changes on different key results [7][8][9]. A production process in the form of a simple illustration is shown in Figure 1 [10]. The input and output run through the process itself with influencing input factors.…”