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Article
Publication date: 1 February 2016

Naiming Xie, Chuanzhen Hu and Songming Yin

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The…

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Abstract

Purpose

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Design/methodology/approach

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

Findings

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

Practical implications

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

Originality/value

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

Details

Grey Systems: Theory and Application, vol. 6 no. 1
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 5 September 2024

Chinmaya Prasad Padhy, Suryakumar Simhambhatla and Debraj Bhattacharjee

This study aims to improve the mechanical properties of an object produced by fused deposition modelling with high-grade polymer.

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Abstract

Purpose

This study aims to improve the mechanical properties of an object produced by fused deposition modelling with high-grade polymer.

Design/methodology/approach

The study uses an ensembled surrogate-assisted evolutionary algorithm (SAEA) to optimize the process parameters for example, layer height, print speed, print direction and nozzle temperature for enhancing the mechanical properties of temperature-sensitive high-grade polymer poly-ether-ether-ketone (PEEK) in fused deposition modelling (FDM) 3D printing while considering print time as one of the important parameter. These models are integrated with an evolutionary algorithm to efficiently explore parameter space. The optimized parameters from the SAEA approach are compared with those obtained using the Gray Relational Analysis (GRA) Taguchi method serving as a benchmark. Later, the study also highlights the significant role of print direction in optimizing the mechanical properties of FDM 3D printed PEEK.

Findings

With the use of ensemble learning-based SAEA, one can successfully maximize the ultimate stress and percentage elongation with minimum print time. SAEA-based solution has 28.86% higher ultimate stress, 66.95% lower percentage of elongation and 7.14% lower print time in comparison to the benchmark result (GRA Taguchi method). Also, the results from the experimental investigation indicate that the print direction has a greater role in deciding the optimum value of mechanical properties for FDM 3D printed high-grade thermoplastic PEEK polymer.

Research limitations/implications

This study is valid for the parameter ranges, which are defined to conduct the experimentation.

Practical implications

This study has been conducted on the basis of taking only a few important process parameters as per the literatures and available scope of the study; however, there are many other parameters, e.g. wall thickness, road width, print orientation, fill pattern, roller speed, retraction, etc. which can be included to make a more comprehensive investigation and accuracy of the results for practical implementation.

Originality/value

This study deploys a novel meta-model-based optimization approach for enhancing the mechanical properties of high-grade thermoplastic polymers, which is rarely available in the published literature in the research domain.

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