Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors
As the manufacturing industry is heading toward the fourth industrial revolution, smart manufacturing is born at the right moment. By integrating new information technologies, such as the Internet of Things, the cyber-physical system (CPS), big data, and artificial intelligence, smart manufacturing...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8681516/ |
| _version_ | 1829472001426718720 |
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| author | Kai Ding Xudong Zhang Felix T. S. Chan Ching-Yuen Chan Chuang Wang |
| author_facet | Kai Ding Xudong Zhang Felix T. S. Chan Ching-Yuen Chan Chuang Wang |
| author_sort | Kai Ding |
| collection | DOAJ |
| description | As the manufacturing industry is heading toward the fourth industrial revolution, smart manufacturing is born at the right moment. By integrating new information technologies, such as the Internet of Things, the cyber-physical system (CPS), big data, and artificial intelligence, smart manufacturing has endowed the factories and shop floors with much intelligence, which is characterized by the organic cooperation among workers, machines, unmanufactured products, and other physical assets. In this situation, endowing these smart physical assets with self-X intelligence and autonomy to make manufacturing resources allocation decisions autonomously has been a vital problem that needs prompt solutions. To solve this problem, this paper deals with training a reasonable knowledge model from the historical shop floor data using a hidden Markov model (HMM) theory. In this model, the unmanufactured product's machining feature/process flow is considered as an observation sequence and the corresponding smart manufacturing resources (SMRs) sequence is considered as a hidden state sequence. The solving method to train the HMM-based knowledge model for autonomous manufacturing resources allocation (A-MRA) is further described in a step-by-step manner. Thereafter, a demonstrative case is studied to verify the proposed model and method. First, 123 pairs of historical data (i.e., process flow and SMR sequence) are used to learn the HMM-based knowledge model and another 5 pairs of historical data are used to test the feasibility and accuracy of the proposed model. The results show that only three elements (total $5\times 9$ elements) in the predicted SMR sequences are different from those in the historical SMR sequences, and the average vector angle between the five predicted and historical SMR sequences is 11.68°, which is relatively low considering that only nine elements exists in each SMR sequence. |
| first_indexed | 2024-12-14T02:05:38Z |
| format | Article |
| id | doaj.art-0bd6f44be71f4b62be7997050667df35 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:05:38Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0bd6f44be71f4b62be7997050667df352022-12-21T23:20:54ZengIEEEIEEE Access2169-35362019-01-017473664737810.1109/ACCESS.2019.29093068681516Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop FloorsKai Ding0https://orcid.org/0000-0002-8121-0258Xudong Zhang1Felix T. S. Chan2Ching-Yuen Chan3Chuang Wang4Department of Manufacturing Automation, School of Construction Machinery, Chang’an University, Xi’an, ChinaDepartment of Manufacturing Automation, School of Construction Machinery, Chang’an University, Xi’an, ChinaDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong KongDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong KongInstitute of Internet of Things and IT-Based Industrialization, Xi’an University of Posts and Telecommunications, Xi’an, ChinaAs the manufacturing industry is heading toward the fourth industrial revolution, smart manufacturing is born at the right moment. By integrating new information technologies, such as the Internet of Things, the cyber-physical system (CPS), big data, and artificial intelligence, smart manufacturing has endowed the factories and shop floors with much intelligence, which is characterized by the organic cooperation among workers, machines, unmanufactured products, and other physical assets. In this situation, endowing these smart physical assets with self-X intelligence and autonomy to make manufacturing resources allocation decisions autonomously has been a vital problem that needs prompt solutions. To solve this problem, this paper deals with training a reasonable knowledge model from the historical shop floor data using a hidden Markov model (HMM) theory. In this model, the unmanufactured product's machining feature/process flow is considered as an observation sequence and the corresponding smart manufacturing resources (SMRs) sequence is considered as a hidden state sequence. The solving method to train the HMM-based knowledge model for autonomous manufacturing resources allocation (A-MRA) is further described in a step-by-step manner. Thereafter, a demonstrative case is studied to verify the proposed model and method. First, 123 pairs of historical data (i.e., process flow and SMR sequence) are used to learn the HMM-based knowledge model and another 5 pairs of historical data are used to test the feasibility and accuracy of the proposed model. The results show that only three elements (total $5\times 9$ elements) in the predicted SMR sequences are different from those in the historical SMR sequences, and the average vector angle between the five predicted and historical SMR sequences is 11.68°, which is relatively low considering that only nine elements exists in each SMR sequence.https://ieeexplore.ieee.org/document/8681516/Smart manufacturingsmart shop floorautonomous manufacturing resource allocation (A-MRA)hidden Markov model (HMM) |
| spellingShingle | Kai Ding Xudong Zhang Felix T. S. Chan Ching-Yuen Chan Chuang Wang Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors IEEE Access Smart manufacturing smart shop floor autonomous manufacturing resource allocation (A-MRA) hidden Markov model (HMM) |
| title | Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors |
| title_full | Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors |
| title_fullStr | Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors |
| title_full_unstemmed | Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors |
| title_short | Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors |
| title_sort | training a hidden markov model based knowledge model for autonomous manufacturing resources allocation in smart shop floors |
| topic | Smart manufacturing smart shop floor autonomous manufacturing resource allocation (A-MRA) hidden Markov model (HMM) |
| url | https://ieeexplore.ieee.org/document/8681516/ |
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