Muhammad Imran¹, Jinsheng Sun^1,*, Fatima Sehar Zaidi², Zameer Abbas^3,4, Hafiz Zafar Nazir⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1207-1257, 2023, DOI:10.32604/cmes.2023.025492

Abstract Control charts (CCs) are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E’s) are involved in the quality characteristic of interest, which can effect the CC’s performance. The authors explored the impact of a linear model with additive covariate M.E on the multivariate cumulative sum (CUSUM) CC for a specific kind of data known as compositional data (CoDa). The average run length is used to assess the performance of the proposed chart. The results indicate that M.E’s significantly… More >

Yeonjee Choi¹, Hyun Suk Hwang², Chang Soo Kim^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4901-4916, 2023, DOI:10.32604/cmc.2023.035219

Abstract A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing… More >

Jeonghoon Choi¹, Dongjun Suh^1,*, Marc-Oliver Otto²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2945-2966, 2023, DOI:10.32604/cmc.2023.033417

Abstract Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique… More >

Abdallah Abdallah¹, Muhamed Dauwed², Ayman A. Aly³, Bassem F. Felemban³, Imran Khan⁴, Dag Øivind Madsen^5,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4571-4586, 2023, DOI:10.32604/cmc.2023.032995

Abstract The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars. Renewable energy sources have the ability to bring the fossil fuel age to an end. Electrochemical storage devices, particularly lithium-ion batteries, are critical for this transition’s success. This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge. Given the environmental degradation caused by hazardous wastes and the scarcity of some resources, recycling used lithium-ion batteries has significant economic and practical importance. Many efforts have been undertaken in recent years to recover cathode materials… More >

Khaled Bawaneh^1,*, Bradley Deken², Amin Esmaeili³

Energy Engineering, Vol.120, No.1, pp. 23-34, 2023, DOI:10.32604/ee.2023.019813

Abstract In this study, information on energy usage in the United States (U.S.) aerospace manufacturing sector has been analyzed and then represented as energy intensities (kWh/m²) to establish benchmark data and to compare facilities of varying sizes. First, public sources were identified and the data from these previously published sources were aggregated to determine the energy usage of aerospace manufacturing facilities within the U.S. From this dataset, a sample of 28 buildings were selected and the energy intensity for each building was estimated from the data. Next, as a part of this study the energy data for three additional aerospace manufacturing… More >

Jie Ren^1,2, Chuqiao Xu³, Junliang Wang^2,4, Jie Zhang^2,*, Xinhua Mao⁴, Wei Shen⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 599-618, 2023, DOI:10.32604/cmes.2022.022415

Abstract The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing execution on the fog server.… More >

Yanhua Zhao^1,3,*, Wenhao Tian¹, Jianhua Liu¹, Dongqing Qian², Wei Meng¹, Jiaming Wang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.2, pp. 451-470, 2023, DOI:10.32604/fdmp.2022.021035

Abstract The process parameters of laser additive manufacturing have an important influence on the forming quality of the produced items or parts. In the present work, a finite element model for simulating transient heat transfer in such processes has been implemented using the ANSYS software, and the temperature and stress distributions related to 316L stainless steel thin-walled ring parts have been simulated and analyzed. The effect of the laser power, scanning speed, and scanning mode on temperature distribution, molten pool structure, deformation, and stress field has been studied. The simulation results show that the peak temperature, weld pool size, deformation, and… More >

Yadian Geng¹, Junqing Li^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 241-266, 2023, DOI:10.32604/cmes.2022.020307

Abstract To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on… More >

Yueqiang Yu¹, Tingang Ma¹, Suling Wang^1,*, Minzheng Jiang¹, Yanling Guo^2,3, Ting Jiang^1,*, Shuaiqi Huang¹, Ziming Zheng¹, Bo Yan¹, Jiyuan Lv¹

Journal of Renewable Materials, Vol.11, No.1, pp. 333-347, 2023, DOI:10.32604/jrm.2022.022296

Abstract A calculation model of stress field in laser additive manufacturing of walnut shell composite powder (walnut shell/Co-PES powder) was established. The DFLUX subroutine was used to implement the moveable application of a double ellipsoid heat source by considering the mechanical properties varying with temperature. The stress field was simulated by the sequential coupling method, and the experimental results were in good accordance with the simulation results. In addition, the distribution and variation of stress and strain field were obtained in the process of laser additive manufacturing of walnut shell composite powder. The displacement of laser additive manufacturing walnut shell composite… More > Graphic Abstract

Eman K. Elsayed¹, Asmaa K. Elsayed^2,*, Kamal A. Eldahshan³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5103-5120, 2022, DOI:10.32604/cmc.2022.030803

Abstract Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized systems. One of the key aspects of these systems is a production planning, specifically, Scheduling operations on the machines. To cope with this problem, this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm (DRLAC). We model the Job-Shop Scheduling Problem (JSSP) as a Markov Decision Process (MDP), represent the state of a JSSP as simple Graph Isomorphism Networks (GIN) to extract nodes features during scheduling, and derive the policy of optimal scheduling which guides the included… More >