An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system

Yin, Xianhui; Niu, Zhanwen; He, Zhen; Li, Zhaojun Steven; Lee, Dong‐Hee

doi:10.1016/j.cie.2020.106284

Cited by 20 publications

(5 citation statements)

References 52 publications

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“…Concurrently, this experimentation consulted to relevant literature for the spacing of the parametric values. Furthermore, the formation of the parametric values for the HIAO algorithm is regulated by referring to the Taguchi methodology [73] layout where examination mode was used for experimental purpose [82]. The Taguchi methodology [73] is introduced where orthogonal matrixes (arrays) are utilized to remarkably lower the counts of experiments [74].…”

Section: Parametric Determinationmentioning

confidence: 99%

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Chen

2022

Int J Comput Intell Syst

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This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

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Section: Parametric Determinationmentioning

confidence: 99%

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Chen

2022

Int J Comput Intell Syst

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“…Therefore, this study utilizes this index as an evaluation tool for the process quality of eco-friendly yarn. Ying et al [10] and Pearn et al [11] have pointed out that as the process quality for each quality characteristic attains the required quality level, it is guaranteed that the process quality of the product can satisfy customer requirements. For this reason, this study integrates the LTB Six Sigma quality indices to evaluate three important quality characteristics for the eco-friendly yarn and then proposes a product quality index as an evaluation tool for the product quality.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Evaluation Model for Products with Multifunctional Quality Characteristics: Case Study on Eco-Friendly Yarn

Chen,

Huang,

Chiou

et al. 2024

Mathematics

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Numerous advanced industrial countries emphasize green environmental protection alongside athletic healthcare. Many world-renowned sports brands are actively developing highly functional, environmentally friendly, and aesthetically pleasing products. For example, in the production of sports shoes, the eco-friendly yarn process is one of the important processes. This process involves multiple crucial larger-the-better quality characteristics closely tied to the functionality of sports shoes. Facing green environmental regulations and external competitors, it is evidently an imperative issue for enterprises to consider how to improve the quality of newly developed products, increase product value, and lower rates of both rework and scrap to accomplish the goals of saving energy and minimizing waste. Aiming to solve this problem, this study proposed a fuzzy evaluation model for products with multifunctional quality characteristics to assist the sporting goods manufacturing industry in evaluating whether all functional quality characteristics of its products meet the required quality level. This study first utilized the larger-the-better Six Sigma quality index concerning environmental protection for evaluation and then proposed product evaluation indicators for the eco-friendly yarn. Since the parameters of these indicators have not yet been determined, sample data need to be used for estimation. Enterprises require rapid response, so that the sample size is relatively small. Sampling error will increase the risk of misjudgment. Therefore, taking suggestions from previous studies, this study constructed the fuzzy evaluation model based on confidence intervals of quality indicators for the eco-friendly yarn. This method incorporated previous experience with data, thereby enhancing assessment accuracy.

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“…Whether production indicators such as the grinding capacity, sucrose extraction, and electric consumption meet the standards will affect sugar production’s smooth operation and economic benefits. The output of the manufacturing system mainly depends on the corresponding conditions of input [ 5 ]. The appropriate setting of the operating parameters in sugarcane milling is a necessary premise to ensure that the production index fluctuates around the optimal value.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

Chen

Meng

et al. 2022

Foods

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The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers’ experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF–EF–IF) by introducing synergetic theory, and each flow’s order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters—including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction—are produced as outputs. A collaborative optimization model of the MF–EF–IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.

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An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system

Cited by 20 publications

References 52 publications

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Fuzzy Evaluation Model for Products with Multifunctional Quality Characteristics: Case Study on Eco-Friendly Yarn

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

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