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Remanufacturing and Advanced Machining
based on customer needs. The implementation steps can be described as follows
(Zhang and Ming, 2021):
Step 1 is the establishment of a resource-based automated factory, including
five sub-steps: equipment for core technology and key short board, digital simulation and research and development (R&D), automatic production
line, digital workshop or factory, and intelligent management within an
enterprise.
Step 2 is the establishment of a network-based interconnection factory, which
includes five sub-steps of vizualization: equipment and energy, process and
quality, monitoring and visualization of production and logistics, monitoring and visualization of workshops, and large screen for visual display of
enterprises.
Step 3 is the establishment of platform-based data sharing factory, which
includes five sub-steps: data acquisition and management platform for
equipment, management platform of simulation data, platform of production line data, management platform of production data, monitoring and
visualization of workshops, and data management platform for product life
cycle.
Step 4 is the establishment of an information system integration factory, which
includes five sub-steps: information system integration for equipment hierarchy, process hierarchy, production line hierarchy, workshop or factory
hierarchy, and enterprise hierarchy.
Step 5 is the establishment of a new model factory for the product life cycle,
which includes five sub-steps: innovating new models of development and
design, planning and scheduling, flexible production, intelligent logistics,
and sale and service.
Step 6 is the establishment of the new model factory for personalized customization, which includes five sub-steps: innovating new models of dynamic
demand, personalized customization, open collaborative design, flexible
manufacturing, and experiential service.
Although AI enables manufacturing systems to operate with a high degree of autonomy and intelligence, there are some tasks impossible to accomplish without human
intervention. Thus, according to Baroroh et al. (2020), the purpose of introducing
AI or automation technologies is not to completely replace human involvement, but
rather to facilitate manual operations. In tasks involving huge data or fuzzy conditions, AI may not perform better, because humans can utilize their cognitive capabilities or implicit knowledge to respond quickly. Thus, to let humans and machines
work with each other, in a complementary fashion, seems to be a more feasible
approach. The ideas of “humans in the loop” or human–cyber–physical system
(HCPS) reflect this objective. In this context, integrating augmented reality (AR)
with intelligent functions can be considered a good strategy. AR can serve as an
interface that strengthens interactions between a human operator and manufacturing environment allowing the former to assess the ambient intelligence through the
AR interface and ensuring his proper response to manufacturing tasks in real time.
Remanufacturing and Advanced Machining
based on customer needs. The implementation steps can be described as follows
(Zhang and Ming, 2021):
Step 1 is the establishment of a resource-based automated factory, including
five sub-steps: equipment for core technology and key short board, digital simulation and research and development (R&D), automatic production
line, digital workshop or factory, and intelligent management within an
enterprise.
Step 2 is the establishment of a network-based interconnection factory, which
includes five sub-steps of vizualization: equipment and energy, process and
quality, monitoring and visualization of production and logistics, monitoring and visualization of workshops, and large screen for visual display of
enterprises.
Step 3 is the establishment of platform-based data sharing factory, which
includes five sub-steps: data acquisition and management platform for
equipment, management platform of simulation data, platform of production line data, management platform of production data, monitoring and
visualization of workshops, and data management platform for product life
cycle.
Step 4 is the establishment of an information system integration factory, which
includes five sub-steps: information system integration for equipment hierarchy, process hierarchy, production line hierarchy, workshop or factory
hierarchy, and enterprise hierarchy.
Step 5 is the establishment of a new model factory for the product life cycle,
which includes five sub-steps: innovating new models of development and
design, planning and scheduling, flexible production, intelligent logistics,
and sale and service.
Step 6 is the establishment of the new model factory for personalized customization, which includes five sub-steps: innovating new models of dynamic
demand, personalized customization, open collaborative design, flexible
manufacturing, and experiential service.
Although AI enables manufacturing systems to operate with a high degree of autonomy and intelligence, there are some tasks impossible to accomplish without human
intervention. Thus, according to Baroroh et al. (2020), the purpose of introducing
AI or automation technologies is not to completely replace human involvement, but
rather to facilitate manual operations. In tasks involving huge data or fuzzy conditions, AI may not perform better, because humans can utilize their cognitive capabilities or implicit knowledge to respond quickly. Thus, to let humans and machines
work with each other, in a complementary fashion, seems to be a more feasible
approach. The ideas of “humans in the loop” or human–cyber–physical system
(HCPS) reflect this objective. In this context, integrating augmented reality (AR)
with intelligent functions can be considered a good strategy. AR can serve as an
interface that strengthens interactions between a human operator and manufacturing environment allowing the former to assess the ambient intelligence through the
AR interface and ensuring his proper response to manufacturing tasks in real time.