Knowledge-Based Calculation and Prediction of Energy Consumption in Product Manufacturing Process

Journal of South China University of Technology (Natural Science Edition) ›› 2009, Vol. 37 ›› Issue (2): 14-19.

• Mechanical Engineering • Previous Articles Next Articles

Knowledge-Based Calculation and Prediction of Energy Consumption in Product Manufacturing Process

Gong Yun-qi Lu Min Wang Gang Fu Yi-li

School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

Received:2008-05-15 Revised:2008-09-01 Online:2009-02-25 Published:2009-02-25
Contact: 宫运启（1971-），男，博士生，主要从事先进制造技术、知识管理等研究． E-mail:gongyunqil@sohu．com
About author:宫运启（1971-），男，博士生，主要从事先进制造技术、知识管理等研究．
Supported by:
黑龙江省自然科学基金资助项目（E200634）

Abstract

Abstract:

To estimate the energy consumption in the manufacturing process of a new product, a knowledge-based prediction method is proposed in this paper. In this method, first, the ontology technology is used to explicate the calculation and management of energy consumption, and a semantic model of energy consumption knowledge and its semantic representation are proposed. Then, a case retrieval process and a method of ease result reuse are presen- ted in view of the characteristics of energy consumption. Moreover, an energy consumption prediction model corre- sponding to manufacturing parameters is established by means of neural network. The effectiveness of the model is finally verified by the machining process of a blade. Thus, a hierarchical framework of energy consumption predic- tion from the overall view to the technical process and further to the machining precedure is formed. The develop- ment and application of the corresponding prototype system are also illustrated in the paper.

Key words: energy consumption prediction, discrete manufacturing industry, ontology, ease-based reasoning, neural network

Gong Yun-qi Lu Min Wang Gang Fu Yi-li . Knowledge-Based Calculation and Prediction of Energy Consumption in Product Manufacturing Process[J]. Journal of South China University of Technology (Natural Science Edition), 2009, 37(2): 14-19.

[1]	FANG Gang, YUAN Longhua, WANG Xiaoming, et al. Soft-Sensor Modeling Method Based on Ensemble Kalman Filter-Elman Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 126-136.
[2]	MA Xiaoliang, AN Lingling, DENG Congjian, et al. Translation Optimization Technology of Automatic Speech Recognition Based on Industry-Specific Vocabulary [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 118-125.
[3]	ZHU Zhengyu, LUO Chao, HE Qianhua, et al. Multi-View Lip Motion and Voice Consistency Judgment Based on Lip Reconstruction and Three-Dimensional Coupled CNN [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 70-77.
[4]	YE Feng, CHEN Biao, LAI Yizong. Contrastive Knowledge Distillation Method Based on Feature Space Embedding [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 13-23.
[5]	LIU Yijun, CAO Yu, YE Wujian, et al. Design and Implementation of Hardware Structure for Online Learning of Spiking Neural Networks Based on FPGA Parallel Acceleration [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 104-113.
[6]	LENG Sheng, FU Youwei, MA Wantai, et al. Study on the Morphology Control Technology of Spray Forming Ingot Billets Based on GA-BP Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(2): 27-34.
[7]	LIU Zeqing, YANG Xufeng, ZHANG Yi. Estimation of P-S-N Curve of Metal Materials Based on Bayesian Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(11): 82-92.
[8]	XU Lunhui, YU Jiaxin, PEI Mingyang, et al. Repositioning Strategy for Ride-Hailing Vehicles Based on Geometric Road Network Structure and Reinforcement Learning [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 99-109.
[9]	LAN Fengchong, ZHANG Yue, CHEN Jiqing, et al. Correlation Analysis and Prediction of Pedestrian Casualty Risk in Car-Pedestrian Collision Accident [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(5): 1-10.
[10]	YANG Chunling YANG Yajing. A Deep Neural Network Based on Layer-by-Layer Fusion of Multi-Scale Features for No-Reference Image Quality Assessment [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(4): 81-89,141.
[11]	FU Xinsha, ZENG Yanjie, MA Li, et al. Weather Recognition of Highway Surveillance Scenes Based on Light-Weight Deep Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(3): 1-8.
[12]	QIU Zhibin, LU Zuwen, WANG Haixiang, et al. Recognition of Bird Sounds Related to Power Grid Faults Based on Mel Spectrogram and Convolutional Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(2): 129-136.
[13]	DU Qiliang, XIANG Zhaoyi, TIAN Lianfang, et al. Two-Stream Adaptive Attention Graph Convolutional Networks for Action Recognition [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 20-29.
[14]	XING Xiaofen, LI Minsheng, XU Xiangmin . Image Sentiment Transformation Based on Adaptive Brightness Adjustment [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 1-12.
[15]	CAI Xiaodong, ZENG Zhiyang . AFGSRec: A Social Recommendation Model Based on Adaptive Fusion of Global Collaborative Features [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 71-79.

Knowledge-Based Calculation and Prediction of Energy Consumption in Product Manufacturing Process

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles 0

Metrics

Comments