These days, intelligent systems powered by AI form the core of Manufacturing, while information and communication technologies provide the foundation of smart manufacturing. New theories, models, algorithms, and applications aimed at mimicking, expanding, and improving human intellect are constantly being produced as AI technologies advance.
Deep learning and large data analysis advancements have sped up AI’s transition into the second stage. Data-driven deep reinforcement learning intelligence, network-based swarm intelligence, technology-oriented hybrid intelligence of human-machine and brain-machine interaction, cross-media reasoning intelligence, and other forms of artificial intelligence are examples of today’s AI technology. As a result, AI has a lot to offer to smart manufacturing, particularly customised manufacturing in smart factories.
AI solutions are generally applicable to a number of smart manufacturing domains. In a smart factory, AI algorithms may manage the production of customised goods. The goal of the AI-assisted Customised Manufacturing (CM) is to build intelligent manufacturing systems that are backed by autonomous decision-making, real-time data analysis, machine status sensors, and cognitive computing. Every link in the CM value chains—design, production, management, and service—is impacted by AI. The application of AI in the smart factory for CM involving architecture, manufacturing equipment, information interchange, flexible production line, and smart manufacturing services is the main topic of this article, which is based on these CM and AI insights.
AI-driven customized manufacturing
Industrial practitioners and researchers have started implementing AI technologies as they have shown promise in fields like market analysis, logistics, after-sales service, customer management, manufacturing management, manufacturing maintenance, and customized product design.
Thus, the implementation of AI technology may make customized manufacturing a reality. Such customized manufacturing powered by AI is referred to as AI-driven CM. In conclusion, AI-driven ERP for CM offers the following benefits.
Enhanced product quality and production efficiency
Automated machines may be able to make choices in CM manufacturing with fewer human inputs. Cognitive abilities, learning, and reasoning are made possible by technologies like machine learning and computer vision (e.g., analysis of order quantities, lead time, flaws, errors, and downtime). Computer vision and foreign object identification are useful tools for identifying process irregularities and product flaws. Process irregularities can be reported to human operators.
Making predictive maintenance easier
The best possible condition of the equipment is guaranteed by scheduled maintenance. A production line’s sensors gather data for analysis using machine learning techniques, such as convolutional neural networks. For instance, it is possible to identify a machine’s wear and tear in real time and send out a message.
The creation of intelligent supply chains
Machine Leaning (ML) algorithms can be used to forecast the supply chains’ unpredictability and variability for CM. Additionally, the information gathered can be utilized to forecast abrupt shifts in consumer preferences. In summary, smart manufacturing benefits from the integration of AI with industrial IoT. AI-powered technologies increase the productivity of production. Products with greater value can be released into the market in the interim. We cannot, however, ignore the fact that when AI technologies are formally applied to real-world manufacturing scenarios, they still have limits. On the one hand, algorithms for AI and ML frequently have high demands on computer infrastructure.
Features of specialized production
Notwithstanding the advancements, the manufacturing sector still faces several obstacles, such as the inability of traditional mass production to keep up with the quick production of customized goods and the growing prominence of resource constraints, environmental contamination, global warming, and an aging world population. In order to overcome these obstacles, a new manufacturing paradigm is required. The notion of customer-to-manufacture embodies the features of customized production, in which a manufacturing system communicates directly with a client to satisfy his or her specific requirements.
Realizing the quick customisation of customized goods is the aim. Better flexibility, transparency, resource use, and manufacturing process efficiency are all provided by the latest generation of intelligent manufacturing technology.
The production structure of CM is more complicated, quality control is more challenging, and energy use requires attention when compared to mass manufacturing. The production limits in classical automation were strict in order to guarantee efficiency, cost, and quality. CM differs from traditional production in the following ways.
Intelligent interconnectivity: Intelligent manufacturing encompasses a cyber-physical environment, such as storage, processing, detection, and assembly equipment, all of which function within a diverse industrial network.
Dynamic reconfiguration: The goal of the smart factory concept is to quickly manufacture a range of goods in small quantities. System resources must be constantly rearranged since product types can change at any time. To negotiate a new system configuration, a multi-agent system is presented.
Large amounts of data: An intelligent manufacturing system is made up of linked devices that produce data about process parameters and device status. Data analysis is now possible for decision-making, active preventative maintenance, and failure prediction thanks to cloud computing and big data science thorough integration. Cloud platforms, edge servers, upper monitoring terminals, and underlying intelligent manufacturing entities are all interconnected. In Cyber-Physical Systems (CPS), where information barriers are removed, data processing, control, and operations can all be carried out concurrently, achieving a deep integration of the information and physical environments.
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