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Data generation, collection, analysis, and preprocessing
Published in Madhusree Kundu, Palash Kumar Kundu, Seshu Kumar Damarla, Chemometric Monitoring: Product Quality Assessment, Process Fault Detection, and Applications, 2017
Madhusree Kundu, Palash Kumar Kundu, Seshu Kumar Damarla
Design of experiments (DOE) is a systematic and efficient method to determine the relationship between factors affecting a process output. It is used to correlate the process output and the main factors, as well as the interaction among the main factors affecting the process output. This correlation is needed to tailor the process inputs in order to optimize the output in terms of quality and quantity. There are different types of DOEs: OVAT, full factorial design, fractional factorial design (Taguchi designs), response surface design, mixture design, EVOP (evolutionary operations), and Plackett-Burman design (two-level fractional factorial design).
Optimization of process parameters of ultrasonic metal welding for multi layers foil of AL8011 material
Published in Welding International, 2023
Shah Samir, Komal Dave, Vishvesh Badheka, Dhaval Patel
A data collection system showing the experimental configuration for ultrasonic metal welding. In order to keep the workpiece from sliding when welding, the horn’s contact area is serrated in a fashion similar to that of an anvil’s top surface. DOE (design of experiments) is a helpful tool for investigating the interplay between multiple factors (inputs) and a single response (outcome). Using Design-Expert version 13, version 13 of Design expert contains a slew of a new features. we did a CCD (Central composite design) with three independent variables to examine their influence on the response. It is common practice to employ CCD when fitting a second-order response surface. CCD is made up of three separate types of point runs: cube point runs, centre point runs and axial point runs.
Experimental and statistical optimization of the hydrogen reduction process of nickel oxide
Published in Materials and Manufacturing Processes, 2018
Maryam Abdollahi, Mahmood Sameezadeh, Majid Vaseghi
The hydrogen reduction rate of NiO is under the influence of many factors such as temperature, holding time, hydrogen gas flow, and pressure. Design of experiments (DOE) is an extremely effective method to optimize process factors, where multiple parameters are involved. There are different statistical methods such as fractional factorial, full factorial, and Taguchi methods to investigate the optimal condition of the effective factors and the influence of each one in DOEs.[10,11] Nowadays, these methods are frequently used to optimize the effective parameters in the various metallurgical processes.[12131415]
Numerical investigation and modelling of controllable parameters on the photovoltaic thermal collector efficiency in semi-humid climatic conditions
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
Ilias Terrab, Nor Rebah, Samir Abdelouahed, Michel Aillerie, Jean-Pierre Charles
The design of experiments (DOE) is a computational approach that is usually used to assess the relationship between the variables that influence a process output. In our case, we used it to identify which factors and how these factors influence the efficiency process. In other words, it is used to explain what causes what by exploring the effects of covariate factors (Montgomery 2005). DOE is also utilized to learn about a system, process, or product and estimate its optimal operating conditions.