An intelligent factory-wide optimal operation system for continuous production process

Ding, Jinliang; Chai, Tianyou; Wang, Hongfeng; Wang, Junwei; Zheng, Xiangwei

doi:10.1080/17517575.2015.1065346

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Intelligent manufacturing can discover the potential laws of the production process through data mining technology, and the intelligent decision model can be built by using this rule to realize green and intelligent manufacturing [1]. With the increasing use of personalization in large-, medium-, and small-batch manufacturing processes, the quality of products is also getting higher and higher expected requirements [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

Shuanjun

Liu

2020

Mathematical Problems in Engineering

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Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.

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Section: Introductionmentioning

confidence: 99%

A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

Shuanjun

Liu

2020

Mathematical Problems in Engineering

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“…Traditionally, existing researchers simply uses the multi-objective algorithm NSGA-II [18] to solve the established OIOB problem [15], [16]. However, the global optimal solutions of OIOB problem are difficult to obtain because unit processes in the beneficiation process are strongly coupling, thus leading to Eq.3 has many local optimums.…”

Section: B Multi-objective Operational Indices Optimization Modelingmentioning

confidence: 99%

“…EAs are also suitable for solving MOPs mainly because they are the population-based methods and can achieve a set of solutions in a single run. For this reason, EAs have been popular applied in real world optimization problems [15], [16]. Solving MOPs by using EAs is often known as multi-objective optimization evolutionary algorithms (MOEAs) [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Yang

Ding

Jin

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Abstract-Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a dataset collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking framework for operational indices optimization has conducted on 10 different production Conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem.Note to Practitioners−Operational indices optimization is a typical method to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multi-objective multi-tasking framework is developed to address the operational indices optimization, which includes a multi-taking multi-objective operational indices optimization problem formulation and a multi-taking multiobjective evolutionary optimization to solve the above formulated optimization problem. The proposed approach can achieve a solution set for the decision making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associate with a higher grade and yield of the product.

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Constrained dynamic multi-objective evolutionary optimization for operational indices of beneficiation process

Yang

Ding

2017

J Intell Manuf

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An intelligent factory-wide optimal operation system for continuous production process

Cited by 10 publications

References 35 publications

A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

A Framework of Cloud Model Similarity-Based Quality Control Method in Data-Driven Production Process

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Constrained dynamic multi-objective evolutionary optimization for operational indices of beneficiation process

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