Further to the discussion Role of Artificial Intelligence and Internet of Things in Manufacturing, The manufacturing industry has always been open to adopting new technologies. Drones and industrial robots have been a part of the manufacturing industry since 1960s. The next automation revolution is just around the corner and the US manufacturing sector is awaiting this change eagerly.
With the adoption of AI if companies can keep inventories lean and reduce the cost, there is a high likelihood that the American manufacturing industry will experience an encouraging growth.
Having said that, the manufacturing sector has to gear up for networked factories where supply chain, design team, production line, and quality control are highly integrated into an intelligent engine that provides actionable insights.
Talk of automation in manufacturing tends to focus on industrial robots, for obvious reasons. The robotics market is growing at an unprecedented pace and vague worries about job loss due to automation however misguided—often take shape in visions of robots replacing individual workers on production lines.
However, there’s a much less tangible form of automation that’s poised to make an even bigger impact on manufacturing in the near future: Artificial Intelligence (AI).
The concept is notoriously difficult to pin down. “I would say it’s as difficult to define as intelligence itself,” noted Philippe Beaudoin, SVP research at Element AI. But for manufacturers, what matters most is what AI can do.
But before more explanation we must know about Artificial Intelligence and Internet of Things :-
Artificial Intelligence is a general term that implies the use of a replicate intelligent behavior. Artificial Intelligence focuses on the development and analysis of algorithms that learn intelligent behavior with minimal human intervention.
These techniques continue to be applied to a broad range of problems that arise in e-commerce, robotics, gaming, medical diagnosis, mathematics and military planning and logistics, to name a few. Several research groups fall under the general umbrella of Artificial Intelligence in the department, but are disciplines in their own right, including: computer vision, robotics, computational biology, natural language processing, and e-commerce.
The connected economy is transitioning from rigid rule-based algorithms to flexible, intelligent ones. These are machine learning solutions that learn and evolve on their own over time, with the appropriate training data.
AI has achieved recent performance breakthroughs across numerous applications, from image classification to pattern recognition and ontological reasoning. For example, over the past decade, automated image recognition has improved in accuracy from 85% to 95% (a human averages 93%), allowing innovations such as autonomous warehouse order picking.
Internet Of Things
It’s about connecting devices over the internet, letting them talk to us, applications, and each other. The popular, example is the smart fridge what if your fridge could tell you it was out of milk, texting you if its internal cameras saw there was none left, other example that you can see your garage in real time from internet and if something happen bad or suppose you forgot to close the door then you can close that from internet or can trigger alarm.
IoT is more than smart homes and connected appliances, however. It scales up to include smart cities think about connected traffic signals or smart bins that signal when they need to be emptied – and industry, with connected sensors for everything from tracking parts to monitoring crops.
The Role of Artificial Intelligence (AI) and Internet of Things (IoT) in Manufacturing
Manufacturing was one of the first industries to harness the power of Artificial Intelligence by using robots to assemble products and package them for shipment. Advances in technology have made assembly of increasingly complex items possible.
These advances are also revolutionising mass production by streamlining production and boosting output. While a human workforce must operate in shifts to ensure continuous production, AI-driven robots can ‘man’ a production line 24-hours a day.
In addition to driving operational efficiencies, AI can reduce manufacturing operating expenditure. Although implementation of the technology would require major capital outlay, the return would be significantly higher.
Businesses in all areas of industrial manufacturing, including automotive, electronics, and durable goods, are investing in IoT devices, and starting to see a return. According to a Tata Consultancy Survey, manufacturers deploying IoT solutions in 2014 saw an average 28.5 percent increase in revenues between 2013 and 2014.
IoT can gather data from multiple machines to deliver waves of real-time data relating to performance and workload. This enables goods to be tracked and equipment maintenance needs to be predicted. Advanced data analysis makes it possible to identify the factors that can contribute to equipment malfunction or failure, including extraneous factors like weather and temperature. With advanced data insight, machinery maintenance can be scheduled proactively, reducing the risk of costly downtimes.
But there is more to adopting IoT than simply producing insights from plant and machinery. It can also create a two-way flow of information, allowing the manufacturer to send information back to the connected devices, changing settings, orders, and operations, all securely and remotely. It will be possible to adjust manufacturing operations automatically based on real-time conditions.
There’s clearly a lot of enthusiasm here, but one might worry about the impact something as extensive as Artificial Intelligence could have on manufacturing jobs.
“Having a human in the loop is really important,” he said. “You can’t hide behind the fact that it’s an algorithm, because the machine is just capturing patterns in the data. So, you need to know your data.”
This raises questions regarding the ethical implications of making decisions based on machine learning. We’re not talking about hackneyed, Terminator-type scenarios, but more realistic concerns about, for example, the potential discriminatory aspects of automated decision-making. There’s even an industry organisation that was created to deal with these issues.