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Smart Machining Processes
corrective action is started. The following points are widely recognized in the relevant state of the art (Assad et al., 2021):
• Condition monitoring is of vital importance for the maintenance process.
• The recent trend is to employ the IoT for data collection so that further processing and decision-making are possible.
• Condition monitoring is also useful for production control.
• Much work has been done in the field of machining, but less for assembly
lines.
• Considerations of life cycle assessment in smart manufacturing are not
given enough attention.
• New opportunities exist under Industry 4.0.
Phuyal et al. (2020) summarize the main characteristics and challenges of smart
manufacturing systems:
• Security issues in smart manufacturing
• System integration (machine-to-machine communication and interconnectivity of a system require a better communication system)
• Interoperability (ability of different systems to understand and access each
other’s functions independently) at four levels of Industry 4.0, namely operational, systematical, technical, and semantic
• Safety in human–robot collaboration – foremost consideration to be given
to occupational health and safety of personnel working on the site, to avoid
any hazardous environment, and to maintain necessary occupational health
and safety
• Multilingualism – ability to interpret any instructions given in a human language into a machine language to instruct a machine on the desired operation (AI implementation)
• Return on investment in new technology
To investigate implementation paths of the smart manufacturing information system (SMIS), Zhang and Ming (2021) analyze 42 representative enterprises from
key fields, including offshore engineering equipment manufacturing, aerospace
equipment manufacturing, household electrical appliances manufacturing, 3C
manufacturing, automobile manufacturing, shipbuilding, medical equipment manufacturing, agricultural equipment bureau manufacturing, manipulator equipment
manufacturing, and rail transit equipment manufacturing industries. The smart
characteristics dimension and system layer dimension are assessed in four implementation directions: (1) resource-based automated factories, (2) network-based
interconnection factory, (3) platform-based data sharing factory, and (4) information system integration factory. The authors conclude the implementation path for
SMIS is usually divided into six big steps, each of them divided into five sub-steps.
Further divisions are derived from the five-level manufacturing system dimension
and five-level product life cycle dimension, improved to customize manufacturing
Smart Machining Processes
corrective action is started. The following points are widely recognized in the relevant state of the art (Assad et al., 2021):
• Condition monitoring is of vital importance for the maintenance process.
• The recent trend is to employ the IoT for data collection so that further processing and decision-making are possible.
• Condition monitoring is also useful for production control.
• Much work has been done in the field of machining, but less for assembly
lines.
• Considerations of life cycle assessment in smart manufacturing are not
given enough attention.
• New opportunities exist under Industry 4.0.
Phuyal et al. (2020) summarize the main characteristics and challenges of smart
manufacturing systems:
• Security issues in smart manufacturing
• System integration (machine-to-machine communication and interconnectivity of a system require a better communication system)
• Interoperability (ability of different systems to understand and access each
other’s functions independently) at four levels of Industry 4.0, namely operational, systematical, technical, and semantic
• Safety in human–robot collaboration – foremost consideration to be given
to occupational health and safety of personnel working on the site, to avoid
any hazardous environment, and to maintain necessary occupational health
and safety
• Multilingualism – ability to interpret any instructions given in a human language into a machine language to instruct a machine on the desired operation (AI implementation)
• Return on investment in new technology
To investigate implementation paths of the smart manufacturing information system (SMIS), Zhang and Ming (2021) analyze 42 representative enterprises from
key fields, including offshore engineering equipment manufacturing, aerospace
equipment manufacturing, household electrical appliances manufacturing, 3C
manufacturing, automobile manufacturing, shipbuilding, medical equipment manufacturing, agricultural equipment bureau manufacturing, manipulator equipment
manufacturing, and rail transit equipment manufacturing industries. The smart
characteristics dimension and system layer dimension are assessed in four implementation directions: (1) resource-based automated factories, (2) network-based
interconnection factory, (3) platform-based data sharing factory, and (4) information system integration factory. The authors conclude the implementation path for
SMIS is usually divided into six big steps, each of them divided into five sub-steps.
Further divisions are derived from the five-level manufacturing system dimension
and five-level product life cycle dimension, improved to customize manufacturing