This work presents a novel algorithm, the MTS-TSO based Neural Network (NN) algorithm, which combines the Mahalanobis Taguchi System (MTS) with the Two-Step Optimal (TSO) method for parameter selections which are adjusted under a dynamic environment for product parameter design. The utility of the algorithm is assessed in two dimensions: the MTS shows how individual product parameter dimensions are selected; and, the TSO-NN links parameters selection decisions across two different times and it can be used to focus on dynamic system design (DSD) and to identify product architecture dimensions that are critical for a dynamic design system strategy. The MTS which can easily solve product parameter design problems and shows it’s computationally efficient in the previous works. Additionally, the TSO algorithm is a simple and efficient means of constructing a dynamic design system, which is verified by the neural network algorithm from this work, and the neural network algorithm from this work, and the neural network is already successfully applied in dynamic system of the past studies. Based on the main aims and verifies of this work, we conclude that the MTS-TSO based neural network algorithm can be applied successfully to dynamic environments for solving product design problems.

This work presents a novel algorithm, which combines the Mahalanobis Taguchi system (MTS) with the adaptive resonance theory neural network (ARTN) method for parameter selections in a dynamic product design system (DPDS). The utility of the algorithm is assessed in two dimensions: the MTS shows how individual product parameter dimensions are selected, and the ARTN links parameter selection decisions across two different timelines and can be used to focus on DPDS and to identify product architecture dimensions that are critical for a DPDS. The MTS can easily solve product parameter selection problems and has proved to be computationally efficient in previous studies. Additionally, the ARTN algorithm provides a simple and efficient means of constructing a DPDS, which is verified by the ARTN algorithm and is presented in this study. From the results of this study, we conclude that the MTS-ARTN algorithm can be applied successfully in a DPDS environment.

This work presents A novel algorithm, the Component Search-Two Steps Optimal algorithm (CS-TSO), which combines the Component Search and Two-Steps Optimal (TSO) algorithm for parameter selection in product design, and parameter adjustment under in dynamic environments. From the results of the confirm experiment, an electricity company is adopted to applies in the methodology, we find that the methodology of the CS-TSO algorithm can easily solves pattern-recognition problems, and is computationally efficient for constructing A model of A system. The CS-TSO algorithm is good at pattern-recognition and model construction of A dynamic system.

The key success factors in cable manufactory industry are new product development, design and selection of key parameters to meet the customer requirements. Simultaneously, products become smaller size and larger capacity to match customer requirements. However, new product development to increase product functionality sometimes increases the cost of the product. Past literature focusing on aspects of the new part gradually replace existing elements or rescheduled elements. But, structure approach was rarely focused. The Kano, Taguchi system are integrated as an algorithm, which is called Kano based TS product design system is proposed to solve above issues in this paper. The proposed method might be helpful by creating a system, reducing the element and enhancing the function in a product design system is observed in this study.

This paper presents the comparison of the Mahalanobis-Taguchi System (MTS) and Component Search (CS) for product designs in parameter selections. Many previous works finding that the Mahalanobis-Taguchi System (MTS) algorithm is successful and effective for parameter selections. Additionally, the CS is already successfully applied in the product/system component selection problems. Based on the main aims and verifies of this work, we find that the methodologies of the MTS and the CS algorithm can easily solve product parameter selection problems. Moreover, the MTS has better performance than the CS algorithm for parameter selections in product designs.