Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors

Kai Ding (Corresponding Author), Xudong Zhang, Felix T.S. Chan, Ching Yuen Chan, Chuang Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)47366-47378
Number of pages13
JournalIEEE Access
Publication statusPublished - 4 Apr 2019


  • Autonomous manufacturing resource allocation (A-MRA)
  • Hidden Markov model (HMM)
  • Smart manufacturing
  • Smart shop floor

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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