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Many innovative technologies are redefining the operations of smart manufacturing. Companies must integrate these technologies as there is increased competition and revolution in manufacturing industries taking place.
FREMONT, CA: With innovative and advanced technologies driving the industry forward, manufacturing is following the path of information and automation. Technologies like artificial intelligence (AI) and machine learning, sensors, and the internet of things (IoT) are radically transforming manufacturing businesses. Therefore, it is essential to understand the trends spurring the industry, which will help companies adapt to those new patterns and grow in the future.
Smart manufacturing encompasses a range of different technologies, which include robotics, AI, and the IoT. Several technologies exist within these categories and often overlap in various ways, like AI depending on data provided by IoT sensors.
Robotic Process Automation
Robotic process automation automates software operations to simplify the manual work of employees. For example, back-office tasks, AR/AP tracking, and vendor and inventory management.
Although there are many applications that robotic process automation provides, the ability to automate different tasks is the most important technology for smart manufacturing. Chatbot personalisation is the common theme that encompasses this topic. Conversational AI also has the potential to automate customer service, troubleshooting, and reporting services for employees.
Artificial Intelligence and Machine Learning (ML)
Complex AI and ML algorithms are developed to improve existing technologies, keep the machinery running longer, and identify ways to make factories more efficient while becoming cost-effective.
Predictive maintenance is the most popular advantage of ML in manufacturing. Waiting to perform maintenance until a machine breaks down costs hugely for businesses. However, ML algorithms integrated with special IoT sensors can be employed to forecast when equipment should be serviced ahead of time before its breakdown. In addition, machine vision for visual inspection is also one of the many use cases of ML.
Digital twins are based on the nature of the way AI is used. It is difficult to test the efficiency of layouts when companies seek the most optimised methods of setting up production. The concept of a digital twin solves this challenge by digitising the testing process. The production line pieces can be rearranged and modified in the simulation to find the most optimal layout by digitising the factory floor into a simulation-based reality.
However, digital twins differ from simulations, and engineers can view data based on real-world conditions. This data comes from the sensors in the real world, which ensures that the digital twin is based on reality for a more accurate image.
With the rise of various concerns, manufacturers are now shifting to securely storing data on cloud storage networks. This helps engineers to receive the advantage of accessing data on demand securely from any location. Moreover, storing data in the cloud is more cost-effective than storing it in different sites.
Storing data securely in the cloud alleviates IT services and storage hardware expenses at their production sites. Cloud storage is highly scalable and elastic, providing more storage space if necessary. This might not be achieved by using physical on-site storage.