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  • Open Access


    Effect of Measurement Error on the Multivariate CUSUM Control Chart for Compositional Data

    Muhammad Imran1, Jinsheng Sun1,*, Fatima Sehar Zaidi2, Zameer Abbas3,4, 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 >

  • Open Access


    Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

    Jeonghoon Choi1, Dongjun Suh1,*, 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 >

  • Open Access


    Mechanical and Rheological Properties of Bamboo Pulp Fiber Reinforced High Density Polyethylene Composites: Influence of Nano CaCO3 Treatment and Manufacturing Process with Different Pressure Ratings

    Cuicui Wang1, Xin Wei1, Lee M. Smith2, Ge Wang1, Shuangbao Zhang3, Haitao Cheng1,*

    Journal of Renewable Materials, Vol.10, No.7, pp. 1829-1844, 2022, DOI:10.32604/jrm.2022.018782

    Abstract In order to investigate the effect of the relative motion of nano CaCO3 reinforced bamboo pulp fiber (BPF)/HDPE composite components on the mechanical performance, a comparative study was performed. BPF was treated by nano CaCO3 blending (BM) and impregnation modification (IM) technology. The composites were produced using hot press (HPMP), extrusion (EMP) and injection molding process (IMP). The physical morphology of BPF was similar at different manufacturing processes. Compared to the samples manufactured by HPMP, a decrease in the (specific) flexural strength of BPF/HDPE composites and an increase in those of composites treated by nano CaCO3 manufactured by EMP and… More >

  • Open Access


    An Energy Efficiency Improvement Method for Manufacturing Process Based on ECRSR

    Haiming Sun1,3, Quande Dong2,*, Cuixia Zhang2, Jianqing Chen3,4

    Energy Engineering, Vol.117, No.3, pp. 153-164, 2020, DOI:10.32604/EE.2020.010706

    Abstract The improvement of energy efficiency is considered as one of the keys to the sustainable development of manufacturing enterprises. This paper proposes an energy efficiency improvement method for the manufacturing process. Based on the analysis of the characteristics of energy consumption in the manufacturing process, a necessary energy consumption model, an assistant energy consumption model and an ineffective energy consumption model are constructed for identifying the energy consumption attributes of the manufacturing process. Then, the relationship model of energy consumption is built, and the energy efficiency improvement method for the manufacturing process is proposed based on ECRSR (Elimination, Combination, Rearrangement,… More >

  • Open Access


    Manufacturing Process Selection of “Green” Oil Palm Natural Fiber Reinforced Polyurethane Composites Using Hybrid TEA Criteria Requirement and AHP Method for Automotive Crash Box

    N. S. B. Yusof1,2, S. M. Sapuan1,3,*, M. T. H. Sultan1,4, M. Jawaid1

    Journal of Renewable Materials, Vol.8, No.6, pp. 647-660, 2020, DOI:10.32604/jrm.2020.08309

    Abstract In this study, the best manufacturing process will be selected to build an automotive crash box using green oil palm natural fibre-reinforced polyurethane composite materials. This paper introduces an approach consist of technical aspects (T), the economic point of view (E) and availability (A), and it’s also called as TEA requirement. This approach was developed with the goal of assisting the design engineer in the selection of the best manufacturing process during the design phase at the criteria selection stage. In this study, the TEA requirement will integrate with the analytical hierarchy process (AHP) to assist decision makers or manufacturing… More >

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