Using Taguchi and neural network approaches in the optimum design of product development process

Lin, Man; Qiu, Guopeng; Zhou, Xue Hua; Chen, Chien-Nan

doi:10.1080/0951192x.2019.1639218

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…In the Taguchi method, each experiment is developed for one process or product with the desired functionality expressed through the features and quality characteristics customers require [14]. The idea of the Taguchi method can be expressed as follows: the goal of the global quality system is to design processes and products resistant to the effects of random factors, which is achieved by minimizing the loss caused by deviations in the functional characteristics of products from their nominal, prescribed value [15].…”

Section: Theoretical Foundation Of Taguchi Methodsmentioning

confidence: 99%

Application of Taguchi Method in Optimization of the Extraction Procedure of Sheet Metal

Puška,

Stojanović

2024

adeletters

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The process of deep sheet metal drawing is accepted in all industrial branches. This process is, therefore, very important to maintain a certain level of quality. For this reason, measurements and tests must be carried out to determine how much sheet metal deformation occurred after the deep drawing process. For this purpose, an experiment of deep drawing of sheet metal was carried out using the example of kitchen utensils. In addition, the Taguchi method was used in this experiment to test the quality of the obtained kitchenware. In the experiment, three factors were taken with three alternatives that affect the deep drawing of sheet metal, and 27 experiments were used for the Taguchi method. The results of this experiment showed that the best results were achieved by the smallest drawing depth of 65 mm and the worst results were obtained by the drawing thickness of 70 mm. Regarding the thickness of the material, the best results were achieved by the material of 21 mm, and the material of 15 mm achieved the worst results. In addition, an analysis of variance was carried out, which determined the relationship between force and deformation of the material.

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Section: Theoretical Foundation Of Taguchi Methodsmentioning

confidence: 99%

Application of Taguchi Method in Optimization of the Extraction Procedure of Sheet Metal

Puška,

Stojanović

2024

adeletters

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“…For example, Tole et al [ 7 ] used the Taguchi method to find a better combination of engineering parameters to optimize the degree of amorphization (DOA). Lin et al [ 8 ] applied the Taguchi method to simplify the analysis of a product development process. Akyalcin et al [ 9 ] adopted an L 9 orthogonal array to investigate the optimal desilication conditions on mesopore volume.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Optical Film Lamination to Enhance the Luminance of TFT-LCD Displays Using the Taguchi-NNGA Method

Leu

Lin

2021

Materials

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Luminance is an essential quality of a TFT-LCD display. Manufacturers have attempted to improve the soft-to-hard lamination stage to enhance the luminance of their TFT-LCD displays. In addition, many customers have complained about the insufficient luminance of the TFT-LCD displays of the case company. While product engineers have kept tuning the control factors in the soft-to-hard lamination stage through the trial and error method, the improvement of the luminance was not good enough. This study aims to assist the product engineers to fine-tune the settings of the control factors using a new method composed of the Taguchi method, a neural network, and a genetic algorithm. The confirmation experiments showed that the proposed method had increased the average luminance of the TFT-LCD displays from 17.03 to 25.15, which was higher than the required luminance value of 25. As a result, the number of complaints on the TFT-LCD displays had been significantly reduced.

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Design of Experiments and machine learning for product innovation: A systematic literature review

Arboretti

Ceccato

Pegoraro

et al. 2021

Quality & Reliability Eng

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The recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques. This article aims to provide a systematic overview of the literature on the joint application of DOE and ML in product innovation (PI) settings. To this end, a systematic literature review (SLR) of two major scientific databases is conducted, retrieving 388 papers, of which 86 are selected for careful analysis. The results of this review delineate the state of the art and identify the main trends in terms of experimental designs and ML algorithms selected for joint application on PI. The gaps, open problems, and research opportunities are identified, and directions for future research are provided.

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Using Taguchi and neural network approaches in the optimum design of product development process

Cited by 9 publications

References 23 publications

Application of Taguchi Method in Optimization of the Extraction Procedure of Sheet Metal

Application of Taguchi Method in Optimization of the Extraction Procedure of Sheet Metal

Optimizing Optical Film Lamination to Enhance the Luminance of TFT-LCD Displays Using the Taguchi-NNGA Method

Design of Experiments and machine learning for product innovation: A systematic literature review

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