How Will The Automation Industry Change In The Next 10 Years?
Automation helps manufacturers cut costs, improve quality, and expand capacity. Until recently though, cost, complexity and lack of flexibility have put it out of reach for smaller businesses. That is starting to change as the automation industry adopts new technologies and new ways of thinking about manufacturing.
Independent forecasts, like that from Fortune Business Insights, predict solid growth in the automation industry over the years ahead. Here are some of the trends and changes to prepare for.
Lower Costs and Easier to Use
The big innovation in robotics in recent years has been the rise of collaborative robots, or cobots. Their built-in safety features can eliminate the need for safety cages and simple teaching methods make them easier and faster to set up. Together, these advances make robots affordable for small and medium manufacturers, even those who deal with high product variety.
Machine vision is another technology where costs are falling while capabilities and ease-of-use are growing. Vision systems have moved beyond reading barcodes and detecting presence, to the point where affordable and easy-to-use systems perform robot guidance and part inspection.
Of course, not all automation needs a robot. Many repetitive tasks can be performed by one or two axes of motion if the control sophistication and precision is available. Modern servo motors and drives are proving more than capable of such functions, especially if aided by PLCs with straightforward visual programming interfaces.
Emphasis on Connectivity
With Industrial Ethernet and WiFi, there is no reason for any factory machine to operate without a connection to the factory network. This lets the machine report status and production statistics while also allowing remote access so engineers on-site and off can carry out programming or other modifications.
Machine level connectivity is only the start though. Increasingly, every piece of equipment, from pumps and motors to drives and pneumatic actuators, can be networked for a previously unimaginable level of data acquisition and recording.
This connectivity, often referred to as the Industrial Internet of Things or IioT, will only be accelerated by the advent of 5G wireless. While bandwidth and latency limitations currently mean much of the available data must be processed at the machine – on the “edge” – 5G will let every machine tap into the computing power available on the factory server, and further afield if necessary.
Sensors are getting cheaper and more capable. From inductive and photoelectric sensors to camera systems that detect presence, identify parts and make measurements, it’s now possible to capture vastly more data than was possible before, and at relatively low cost.
Combine this sensor power with connectivity capabilities, and the result is a tsunami of numbers describing what is happening in the factory. Spindle temperatures, motor current draws, cycle times and much more are all available, to a precision never before seen. The challenge is, who is going to make sense of all this data?
This has been described as the challenge of big data, and there are two answers. First, there is a growing number of analytics tools that will help capture, sort and analyze the data coming in from sensors and equipment across the factory. Second, artificial intelligence (AI) is emerging as a practical tool for recognizing patterns in massive data sets.
Practical applications of AI are appearing now in two fields, with a third close behind. The two already being adopted are AI in machine vision, and AI for predictive maintenance. The third area is AI for process optimization. It is not here, yet but it will come.
AI, or more specifically, deep learning, (a subset of AI,) is being used with machine vision to improve inspection capabilities. Conventional “rules based” vision systems struggle with complex patterns and textures, but deep learning vision systems have been shown to excel at detecting defects under such conditions.
Predictive maintenance is an Industry 4.0 or IIoT tool. This uses data gathered from sensor-equipped machines, (temperature, vibration, fluid level and so on,) to detect the onset of problems before they cause quality or speed issues. With predictive maintenance, manufacturers are spared from scheduling downtime for servicing work that may be unnecessary. Instead, just wait for the AI to warn that work will be needed shortly.
Increasing Levels of Manufacturing Automation Ahead
For decades, automation has been complicated and expensive. The technology needed highly skilled engineers to implement and maintain, putting it out of reach of small to medium manufacturers and any operation dealing with extensive product variety.
That is changing, and the automation industry is changing with it. With more tools and equipment options to offer, and advanced technology getting cheaper, more flexible, and easier to use, expect vendors to provide a wider range of services. They will also be offering the advice and support to match.
In parallel, demand for expensive integration services will likely fall as easier-to-use systems are deployed and supported by users themselves. However, as more manufacturers automate their processes, the demand for automation equipment will grow overall, with increased volumes helping lower costs further. It is going to be an exciting time in the automation industry!
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