robots (AMRs), they are popping up all over the factory floor. They work along side their human counterparts while performing simple, repetitive, and in some case hazardous tasks. This frees up their human counterparts to focus on more difficult tasks, as well as solve any problems. Industrial robots are only a small piece of the equation. To be effective in this new age of manufacturing, there is also a need for an immense amount of data collection. To accomplish this, sensors are deployed throughout the factory floor to maintain effective control at every point in the manufacturing process. Vision sensors onboard the robots, as well as along the conveyor path, help provide quality assurance. LiDAR is also being utilized in robotic applications to protect workers and assets, allowing an AMR to navigate the factory floor and to avoid collisions. By integrating sensor fusion, the entire process and environment can be monitored in real time. The number of sensors deployed on a factory floor is jaw-dropping.
unique IO Device Description (IODD) that allows it to be identified. Manufacturers can now utilize data collected from
a vast sensor network to analyze every step within a manufacturing process across all lines simultaneously. Sensors collect data on temperature, vibration, humidity, voltage/current, and much more. As a result, manufacturers can quickly identify when a piece of equipment is operating out of specification, which can be a warning of a potential failure. By identifying this early, maintenance can be performed in a timely manner to reduce downtime, as well as costly repairs. Through a predictive maintenance program, a manufacturer can increase equipment longevity, reduce errors, reduce damage to equipment and materials, as well as protect workers from potential injury. Not to mention the cost savings in energy and resources. A few years ago, a revolutionary technology called “digital twin” was introduced to the manufacturing world. By creating a digital copy of the factory floor down to the most minute detail, a manufacturer can analyze trends in production, material shortages, efficiencies, and forecast peaks and valleys in their process. It also gives them the ability to make programming changes in a simulated manufacturing environment to predict how it would affect a real- world scenario.
Adding AI technology into digital twin creates an even more powerful tool for manufacturers. Rather than passively monitoring a production process, AI can analyze data and make real-time adjustments to create a more efficient process. By incorporating adaptive learning, creating greater accuracy and effectiveness can be achieved. Utilizing this new AI component, digital twin can not only monitor when a machine is beginning to fail, but can literally predict the type of failure, and preemptively order its repair at a time that won’t delay production. Smart manufacturing today means more efficiency through better production processes. It helps to reduce workplace injuries, reduce waste, increase quality, increase productivity, and increase competitive advantage. Manufacturers today, face a host of obstacles. o combat these obstacles and stay competitive, manufacturers must turn to smart manufacturing technologies such as industrial robotics, wireless sensor networks, AI and digital twin.
Incorporating all of them and collecting their data requires a
network platform such as IO-Link. IO- Link is a point-to-point, bidirectional communication network standard that enables communication with sensors and actuators through a wired or wireless connection. An IO-Link Master can communicate with a large number of sensors and actuators (IO-Link devices). Each sensor or actuator is assigned a
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