Toward a cyber-physical manufacturing metrology model for industry 4.0
Само за регистроване кориснике
2021
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Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using deve...loped GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.
Кључне речи:
metrology / intelligent planning / Industry 4 / GA / Cyber-physicalИзвор:
Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing, 2021, 35, 1, 20-36Издавач:
- Cambridge Univ Press, New York
Финансирање / пројекти:
- Ministry of Education, Science and Technological Development of the Republic of Serbia
DOI: 10.1017/S0890060420000347
ISSN: 0890-0604
WoS: 000621807500003
Scopus: 2-s2.0-85095116725
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Stojadinović, Slavenko AU - Majstorović, Vidosav D. AU - Durakbasa, Numan M. PY - 2021 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3616 AB - Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose. PB - Cambridge Univ Press, New York T2 - Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing T1 - Toward a cyber-physical manufacturing metrology model for industry 4.0 EP - 36 IS - 1 SP - 20 VL - 35 DO - 10.1017/S0890060420000347 ER -
@article{ author = "Stojadinović, Slavenko and Majstorović, Vidosav D. and Durakbasa, Numan M.", year = "2021", abstract = "Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.", publisher = "Cambridge Univ Press, New York", journal = "Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing", title = "Toward a cyber-physical manufacturing metrology model for industry 4.0", pages = "36-20", number = "1", volume = "35", doi = "10.1017/S0890060420000347" }
Stojadinović, S., Majstorović, V. D.,& Durakbasa, N. M.. (2021). Toward a cyber-physical manufacturing metrology model for industry 4.0. in Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing Cambridge Univ Press, New York., 35(1), 20-36. https://doi.org/10.1017/S0890060420000347
Stojadinović S, Majstorović VD, Durakbasa NM. Toward a cyber-physical manufacturing metrology model for industry 4.0. in Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing. 2021;35(1):20-36. doi:10.1017/S0890060420000347 .
Stojadinović, Slavenko, Majstorović, Vidosav D., Durakbasa, Numan M., "Toward a cyber-physical manufacturing metrology model for industry 4.0" in Ai Edam-Artificial Intelligence For Engineering Design Analysis and Manufacturing, 35, no. 1 (2021):20-36, https://doi.org/10.1017/S0890060420000347 . .