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How Machine Learning Has Improved Production Factories’ Robotics

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How Machine Learning Has Improved Production Factories’ Robotics

Machine learning, robotics, and manufacturing automation have the potential to disrupt and transform our global economy in the upcoming years. The increased use of robots that are powered by machine learning and artificial intelligence in manufacturing and warehousing means there is a massive rise in efficiency and productivity. Machine learning is quickly improving the capability and competency of robots in production and automated manufacturing. Flexible and large training datasets have led to a marked improvement in several areas. Let’s take a close look at some of them.

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  1. Safety: The use of machine learning in robotics is gradually improving the safety standards of automated workspaces. 2D and 3D image datasets are being used for enhancing the environmental perceptions of all industrial robots. You can get reliable and fast object detection for making sure that powerful machines can avoid human beings and obstacles.
  2. Quality: Better image labeling in the field of robotics, is improving the capability of machines to identify faults and other defects in products that are coming fresh out of assembly lines. Computer vision-enabled cameras in robotics are capable of spotting defects that are not visible to the human eyes. Apart from that, AI-powered inspections may be carried out frequently without dropping the fault detection rates.
  3. Longevity: Machine learning-enabled systems are also being deployed that can be used to carry out the maintenance of other structures and machines. There is a regular use of visual datasets featuring pictures that are properly labeled with examples of wear for training models. These training models are used to spot possible defects in machinery or mechanical problems before there is a catastrophic failure. This type of preventive ML surveillance can improve the lifespan of several vital pieces of equipment.
  4. Product development: One of the more common uses of machine learning is product development. Both things viz. design of new products and the improvement of existing ones require the use of extensive data analysis to achieve the best results. ML solutions help collect and analyze a large amount of product data for understanding consumer demand and uncover hidden flaws to identify newer business opportunities. 

This not only helps in identifying existing product designs but can also develop superior quality products that can develop newer revenue streams for your business. Software developing has played a great deal of a role when it comes to product development, many companies have reached the top through analyzing and fitting software usage to their needs, there are many companies that can do a software business plan and help you achieve the things that your firm is striving for product wise.

  1. Cybersecurity: The solutions using machine learning depend on data, network, and tech platforms for both cloud and on-premise functions effectively. Security of these kinds of data and systems is crucial and machine learning plays a vital role in better regulation of important digital platforms and other info. Machine learning is capable of streamlining the way users will access sensitive data and the type of application they can use. It can also streamline the way you can connect with it. It is extremely useful for businesses to protect their digital assets by detecting anomalies fast and immediately triggering corrective action.

Use of machine learning for robotics in production factories

The various advancements made possible by artificial intelligence and machine learning for robotics have been used in several industries. There are many production factories out there that use AI-driven machinery for production. Some robotics arms that are trained with visual datasets can act as pickers for distribution warehouses. This raises the speed at which items can get moved away from a place. ML-powered robots are being used in automobile factories while using bounding boxes, for identifying vehicles, while they are moving in an assembly line. It allows the cars to avoid possible collisions in a crowded production environment.

Conclusion

The use of machine learning in production and other related processes can provide a significant rise in the efficiency of your manufacturing. This also leads to the development of newer business opportunities. Nowadays manufacturers wish to know how machine learning is useful for resolving specific business issues such as tracing production defects back to some specific steps taken undertaken in the production process. You can also achieve lesser waste with better identification of the presence of faulty components in the earlier stages of the production process. However, newer generations of machine learning must have access to a better quality of training data at a scale desired.

 

IoT

4 Applications For IoT in the Manufacturing Industry

Not too long ago, it seemed impossible for everyday objects and machines to carry out specific tasks unaided by a human controller. The mere thought of that possibility was a pipe dream to some people and represented a terrifying future to others. Today, IoT (Internet of Things) is as much of a reality as humans and other organic life forms.

Devices are much more intelligent than they used to be a few decades ago. Embedded with chips and sensors, they can make autonomous decisions. They may not handle some tasks that require higher levels of intelligence and emotional quotient. However, in many industries, IoT is proving to be the future of work.

The manufacturing industry is one that has benefited immensely from IoT. As a result, it is the biggest IoT market. In 2016, the manufacturing industry spent 100 billion USD more than its closest rival, the transportation industry, on IoT. How has all of this spending on IoT translated to its implementation? This post will discuss industrial IoT solutions in manufacturing.

Digital Twins

As efficient as some systems and machines are, one slight issue besets them – their form. Physical devices are usually limited to their current physical locations. In IoT manufacturing, this may hamper efficiency, so the digital twins’ concept had to be implemented.

A digital twin is a digital copy of a device or process. It helps production plants and businesses monitor how a concept will work in everyday use without spending a lot of money on trials and on-field testing. These businesses can then improve efficiency using results from this approach.

With this IoT idea, managers can predict how a device will operate over time, how wear and tear will affect it, how long it will stay usable, etc. They can use the knowledge gained to tweak their systems and devices to perform better. They can also know which machines to recall and when to do so.

Asset Management

Asset Management, also known as asset tracking, is a system that allows a business to gather information about its tangible assets, log them and use them to monitor the assets’ statuses. This type of program is more advanced, coming from the more complex structure category, like ones, created for managing businesses like essay writing service, or logistics organizations.

This IoT-driven approach monitors both fixed assets and mobile ones. However, because mobile assets are at more significant risk of theft and loss, they tend to be tracked more often. Assets can be tracked using GPS (for real-time tracking), barcodes that update the system, and Radio-Frequency Identification.

There are several benefits of asset management to manufacturing plants. The most obvious one is that this system has helped factories save a lot of money. For every device that the tracking hardware and software manages, it creates a log. The system then alerts managers when something goes wrong, giving them a head start at reversing the situation or stopping it at the very least.

It does not only serve protective purposes, but it also helps with the prevention of loss. Knowing that assets are being tracked in real-time will deter thieves from trying to take them. This preventive advantage is incredibly efficient if the thieves are in cohorts with factory workers. This is because the asset management system can also monitor employees’ whereabouts.

Smart Pumping

For a long time, water management plants have faced wastage issues and outrageous electricity bills. These factors make up a significant portion of the cost of operation. Smart pumping is the pump industry’s proactive response to these long-standing problems.

The industry has recently ditched its reactive approach for predictive maintenance to turn the tides back in its favor. With smart pumping, sensors have replaced human efforts in tedious and time-consuming tasks. Manual maintenance of pumps is fast becoming a thing of the past.

Nowadays, technology monitors the pumps’ health and checks for signs of abnormalities. It does this by monitoring temperatures, vibration levels, runtime, etc. When the sensors detect any irregularities, they trigger troubleshooting processes to get the equipment working optimally.

These sensors also regulate water pressure in the pumps. Each company has pre-determined metrics that it operates by (including compulsory industry standards), and the sensors adhere to these metrics. They shut off when they need to, thereby preventing wastage and helping the company save money.

Safety and Security

IoT isn’t just here for the machines; it is also implemented to ensure employee safety. The best IoT companies continuously seek to make human lives and work more comfortable and safer – not to replace them. In manufacturing, employees regularly work with heavy-duty machines and thus need the best safety measures in place.

With data analysis, plant managers can use IoT to ensure that working conditions are ideal. They monitor near misses and work-related injuries and use the data to improve their safety processes. If there are indicators of a procedure or device not being as safe as it once was, the system upgrades or replaces it.

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Amanda Dudley is a distinguished writer and a lecturer with a Ph.D. in History at Stanford University. She is fascinated with students and seeks to help them succeed. On the side, she works at EssayUSA with a team of professional writers. There, she implements the latest educational techniques to help students with their academic assignments, dissertations and papers.