By leveraging the capabilities of the Internet, manufacturers can increase data integration and storage. Employing the cloud allows businesses to access highly configurable computing resources.[14]​ This allows servers, networks, and other storage applications to be created and released at a rapid pace. The companies' integration platforms allow the manufacturer to collect data broadcast by their machines, which can track metrics such as workflow and machine history. Open communication between manufacturing devices and networks can also be achieved through Internet connectivity. This ranges from tablets to machine automation sensors and allows machines to adjust their processes based on information from external devices.[4]​.

As of 2019, 3D printing is primarily used in rapid prototyping, design iteration, and small-scale production. Improvements in speed, quality, and materials could make it useful in mass production and mass customization").[15]​[16]​.

Smart manufacturing can also be attributed to monitoring workplace inefficiencies and assisting in worker safety. Optimizing efficiency is a big goal for adopters of “smart” systems, done through data investigation and smart learning automation. For example, operators can be given personal access cards with built-in Wi-Fi and Bluetooth, which can connect to machines and a cloud platform to determine which operator is working on each machine in real time.[17] An intelligent, interconnected "smart" system can be established to set a performance goal, determine if the goal is achievable, and identify inefficiencies through missed or delayed performance goals.[18] In general, automation can alleviate the pain points. inefficiencies due to human errors. And overall, evolving AI eliminates the inefficiencies of its predecessors.

Worker safety can be increased through safe, innovative design and increased integrated automation networks. This is done under the idea that technicians are less exposed to hazardous environments as automation matures. If successful, less human supervision and user training for automation will divert workplace safety concerns.[19]​.

Industry 4.0 is a project of the German government's high-tech strategy that promotes the computerization of traditional industries such as manufacturing. The goal is the smart factory (Smart Factory) characterized by adaptability, resource efficiency and ergonomics, as well as the integration of customers and business partners in commercial and value processes. Its technological base consists of cyber-physical systems and the Internet of things.[20]​.

This type of "smart manufacturing" makes great use of:.

European Roadmap "Factories of the Future" and the German "Industrie 4.0" illustrate several of the lines of action to be undertaken and the related benefits. Some examples are:.

Contenido

En esta sección se examinan las principales visiones del futuro de la fabricación inteligente y para cada una de ellas se presentan diferentes ejemplos de las principales organizaciones industriales y de investigación.

Cyber-physical systems in smart manufacturing

As one of the main elements of Industry 4.0, cyber-physical systems play an important role in the future of smart manufacturing systems.

Connection layer.

Systems, feedback, machines and humans are essential parts of manufacturing systems and their contributions depend largely on their connection to the rest of the manufacturing elements. For example, an operator may require advanced production system intelligence to make the most efficient decisions for scheduling maintenance or production orders. Advanced communication technologies, such as 5G technology, would significantly improve connectivity between manufacturing systems.[21]​.

Cyber ​​layer.

This layer is a data storage center where big data analysis tools are used for better and more efficient decision making. A digital twin can be realized at this layer by integrating cyberspace into physical components through the tactile Internet. In addition, similarity-based methods can be used to make comparisons between machines and help improve fault diagnosis and increase their efficiency.

Layer of cognition.

In this layer, infographic tools are used to present the results of analytical studies to users. Simple radar charts and degradation trends can be used for a simple representation of the health status of the component. Then operators can easily make a decision based on the data presented.[22]​[23]​.

Different Artificial Intelligence techniques and approaches inspired by Neuroscience can be used to emulate the socio-cognitive experiences of operators and technologists and provide machines with artificial cognitive capabilities.

Configuration layer.

In this layer, decisions made in the Cognition layer are applied to the physical system to make the systems self-adaptive, self-configured, and self-resilient.

South Korea's Ministry of Economy, Trade and Industry announced on March 10, 2016 that it had helped build smart factories at 1,240 small and medium-sized enterprises, which it said resulted in an average of 27.6% decrease in defective products, 7.1% faster production of prototypes, and 29.2% cost reduction.[24]

From Siemens' perspective, there are three central elements of this evolution:[20]​.

By leveraging the capabilities of the Internet, manufacturers can increase data integration and storage. Employing the cloud allows businesses to access highly configurable computing resources.[14]​ This allows servers, networks, and other storage applications to be created and released at a rapid pace. The companies' integration platforms allow the manufacturer to collect data broadcast by their machines, which can track metrics such as workflow and machine history. Open communication between manufacturing devices and networks can also be achieved through Internet connectivity. This ranges from tablets to machine automation sensors and allows machines to adjust their processes based on information from external devices.[4]​.

As of 2019, 3D printing is primarily used in rapid prototyping, design iteration, and small-scale production. Improvements in speed, quality, and materials could make it useful in mass production and mass customization").[15]​[16]​.

Smart manufacturing can also be attributed to monitoring workplace inefficiencies and assisting in worker safety. Optimizing efficiency is a big goal for adopters of “smart” systems, done through data investigation and smart learning automation. For example, operators can be given personal access cards with built-in Wi-Fi and Bluetooth, which can connect to machines and a cloud platform to determine which operator is working on each machine in real time.[17] An intelligent, interconnected "smart" system can be established to set a performance goal, determine if the goal is achievable, and identify inefficiencies through missed or delayed performance goals.[18] In general, automation can alleviate the pain points. inefficiencies due to human errors. And overall, evolving AI eliminates the inefficiencies of its predecessors.

Worker safety can be increased through safe, innovative design and increased integrated automation networks. This is done under the idea that technicians are less exposed to hazardous environments as automation matures. If successful, less human supervision and user training for automation will divert workplace safety concerns.[19]​.

Industry 4.0 is a project of the German government's high-tech strategy that promotes the computerization of traditional industries such as manufacturing. The goal is the smart factory (Smart Factory) characterized by adaptability, resource efficiency and ergonomics, as well as the integration of customers and business partners in commercial and value processes. Its technological base consists of cyber-physical systems and the Internet of things.[20]​.

This type of "smart manufacturing" makes great use of:.

European Roadmap "Factories of the Future" and the German "Industrie 4.0" illustrate several of the lines of action to be undertaken and the related benefits. Some examples are:.

Contenido

En esta sección se examinan las principales visiones del futuro de la fabricación inteligente y para cada una de ellas se presentan diferentes ejemplos de las principales organizaciones industriales y de investigación.

Cyber-physical systems in smart manufacturing

As one of the main elements of Industry 4.0, cyber-physical systems play an important role in the future of smart manufacturing systems.

Connection layer.

Systems, feedback, machines and humans are essential parts of manufacturing systems and their contributions depend largely on their connection to the rest of the manufacturing elements. For example, an operator may require advanced production system intelligence to make the most efficient decisions for scheduling maintenance or production orders. Advanced communication technologies, such as 5G technology, would significantly improve connectivity between manufacturing systems.[21]​.

Cyber ​​layer.

This layer is a data storage center where big data analysis tools are used for better and more efficient decision making. A digital twin can be realized at this layer by integrating cyberspace into physical components through the tactile Internet. In addition, similarity-based methods can be used to make comparisons between machines and help improve fault diagnosis and increase their efficiency.

Layer of cognition.

In this layer, infographic tools are used to present the results of analytical studies to users. Simple radar charts and degradation trends can be used for a simple representation of the health status of the component. Then operators can easily make a decision based on the data presented.[22]​[23]​.

Different Artificial Intelligence techniques and approaches inspired by Neuroscience can be used to emulate the socio-cognitive experiences of operators and technologists and provide machines with artificial cognitive capabilities.

Configuration layer.

In this layer, decisions made in the Cognition layer are applied to the physical system to make the systems self-adaptive, self-configured, and self-resilient.

South Korea's Ministry of Economy, Trade and Industry announced on March 10, 2016 that it had helped build smart factories at 1,240 small and medium-sized enterprises, which it said resulted in an average of 27.6% decrease in defective products, 7.1% faster production of prototypes, and 29.2% cost reduction.[24]

From Siemens' perspective, there are three central elements of this evolution:[20]​.