If we go back to a few decades back we can clearly differentiate how medical, manufacturing, fashion, production, and other important sectors vary with respect to today’s advancements in these fields. The transformations observed with the developing technology is primarily observed in the manufacturing industry as compared to the rest of the sectors. Watching robots perform logistic activities and the automation of most of the manufacturing activities would have seemed quite fictional to not just humans but also the manufacturers as well. Undoubtedly, AI has played an important role in all these. With Artificial Intelligence, the production time is reduced, the need to have large inventories are eliminated by real-time product development, and there is more robotic use for manufacturing activities instead of humanitarian aid. But why this transition? The obvious answer is the improvement in speed and accuracy of doing all the manufacturing activities. Additionally, there has been a remarkable decrease in the wastes generated because of low demand or customer unlikeness. There are indeed various astonishing applications of Artificial Intelligence in the manufacturing industries and we are here to discuss all of these. So, let us move on to understanding Artificial Intelligence and looking at its varied applications to discover the developments seen in the manufacturing sector.

Let us first discuss Artificial Intelligence to get to know about it better.

Artificial Intelligence and its classification

Artificial Intelligence is defined as the intelligence illustrated by the machines. This is something opposite to the natural intelligence found in human beings and animals. Through Artificial Intelligence, only selective actions have conducted that increase the chance of success in a certain situation. It can be referred to like software that equips itself for specific tasks while improving its performance in these activities over a certain period.

By Artificial Intelligence, we do not just mean robots and bots that are remarkably intelligent. It indeed refers to several programmable bodies that efficiently perform a set of tasks thus making them highly autonomous. Artificial Intelligence has the ability to imitate human behavior and thus evolves to emerge smarter over time. There are also a variety of technologies involved in this that make these robots and AI bodies capable of imitating human behavior. Categorizing Artificial Intelligence with respect to the type of intelligence, there are three different categories of AI such as –

  • Artificial Narrow Intelligence (ANI)

The intelligence exhibited wherein an AI can imitate human intelligence and characteristics within a narrow range of aspects is known as ANI or Weak AI or Narrow AI. However, this narrow intelligence should not be confused with less intelligence.

One of the befitting examples of ANI can be seen in RankBrain implemented by Google that ranks pages. Although ranking pages is a complicated work it has a narrow range of operational efficiency. Currently, all the operational AI are ANI.

  • Artificial General Intelligence (AGI)

A situation where an AI is capable of imitating human intelligence and characteristics which is completely equivalent to that of a human is referred to as Artificial General Intelligence, AGI which is also called as Strong AI or Deep AI.

Recent news about Fujitsu-built K is one of the best examples of the application of AGI that effectively and quickly reads complex neural networks. The scientists from Japan and Germany have been successful in operating the biggest simulation of neural networks in the human brain. The Fujitsu-built K accomplished the simulation of neural network activities for one second within 40 minutes in real-time. This was confirmed by RIKEN, a research institute of Japan which is involved in operating this machine.

  • Artificial Super Intelligence (ASI)

ASI is a situation in which an AI does not imitate human intelligence rather outperforms it. However, this type of Artificial Intelligence is something we can hypothesize about. With ASI, the AI will exceed human performance in various activities starting from reading and writing to specifying medicines and other crucial services. It is thus expected of an ASI inspired AI to excel in almost every field including bots and literature as well.

While we continue our exploration of manufacturing AI and its overall process efficiency, the information and advancements offered by Andrew Ng, the founder of the Deep Learning Google Brain project are quite enlightening.

Andrew Ng and Deep Learning for manufacturing

As quoted by Andrew Ng – “Deep Learning is a superpower. With it, you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that isn’t a superpower, I don’t know what is.” He believes that AI has effective results in the manufacturing industry as compared to other sectors. He additionally says, “AI will perform manufacturing, quality control, shorten design time, and reduce material waste, improved production reuse, perform predictive maintenance, and more.”

He also adds that industrial AI increases process efficiency with the help of robots or their prototypes that make use of CAD data to create parts without the need for programming. Andrew Ng’s own company Landing.ai offers effective visual inspection by making use of manufacturing AI. Their operational efficiency increases as they are capable of identifying a series of imperfections after visualizing just five product images obtained. This enhances the process efficiency as compared to traditional methods because the need to read and implement activities based upon huge data sets is exceptionally reduced. Such AI inspired advancements to come in handy in various shopfloors and industrial factories as they are capable of implementing various logistic activities and other important processes by the help of only CAD model without the requirement of any programming. The robot, on its own, can determine the task to be executed by each of its arms. The operational efficiency of these robots also increases such that they are themselves capable of amending the faults. The arms are equipped enough to accumulate the components that slip out within the field of vision of the camera. This not only makes their movement in factories and shopfloors convenient but also makes them competent in whatever they do.

Knowing all these wonders, it becomes important to comprehend how industrial AI is able to transform the whole manufacturing system. The inherent Machine Learning technology and pattern-recognition system can help in the advancement of factories by leaps and bounds. Manufacturing AI will not only reduce hazardous downtime but also generate uniquely and effectively designed products. So, let us have a look at how AI is changing the manufacturing industry.

Ways in which AI transforms manufacturing sector

  • Generative design

Generative design plays an important role in increasing the process efficiency of the manufacturers. Various designers provide the desired design which is accompanied by the required material aspects, manufacturing processes, and cost restrictions that is fed into the generative design software. With the help of this software, we can determine favourable chances of a solution and come up with different alternatives to the generative design.

Simultaneously, the manufacturing AI makes use of Machine Learning to examine and master the pros and cons of each repetition of the operation. As Vice President of platform engineering at Autodesk, Brian Matthews says the generative design has the capability to help you lease 50,000 computers for one whole hour. With this benefit, industrial AI can get 50,000 days of engineering work done in a single day.

  • Predictive maintenance

In predictive maintenance, the machines have to report their developments in each minute. This discards the hypothesizing process included in preventive maintenance. With this feature of manufacturing AI, a large number of businesses can save their useful time and resources while ensuring the best manufacturing performance. This is possible due to the availability of sensors and developed analytics in the manufacturing apparatus. They help the industrial AI to practice predictive maintenance by instantly taking actions in case of alerts and addressing machine problems.

As per a Deloitte report, titanium’s hardness can be achieved with the help of tools that require diamond tips to accurately cut it. Thus, it is quite obvious that detecting the exact dullness of tips which is highly dependent on the required time to sharpen them is quite hard to comprehend. However, with the help of predictive maintenance, you can make use of vibrations obtained through sensors and the torque regulators to examine the status of the machinery with respect to the movement and the sound of the dull tips.

Lean Manufacturing is one of the best examples of how manufacturing AI has excelled over time. While practising lean manufacturing different types of principles and equipment are used to decrease the number of wastes generated in the manufacturing activities while aiding the overall productivity to reach its maximum output. Lean Manufacturing makes use of a number of tools such as Heijunka, Kanban, Jidoka, and 5S that help in drastically reducing the wastes produced for the optimal productivity of industrial AI.

  • Computerized Vision

By making use of a machine that has a camera with sensitivity many times higher than the naked eye, the chance to not detecting any flaw is remarkably less. Basically, the apparently logical steps are taken into consideration and these relevant images are sent to an individual to make modifications and pass that on to a machine too. Andrew Ng in his organization Landing.ai has produced machine – vision tools that are efficient in finding microscopic flaws in the products generated. The manufacturing AI utilizes various machine learning algorithms that are trained to read a category of relevant images. Once studied the computer not only visualizes but also handles the information acquired and increases its learnings from the things observed. In case of any anomaly detected, the industrial AI makes use of the technique of automated issue identification.

  • Digital Twins

Digital twins can be referred to as a basic model of a method or service or a product. When operating with the help of any equipment from an isolated area, digital twins come in handy because it makes use of cloud computing and available cloud storage. The sensors available in a physical product are capable of collecting data about real-time activities and the working environment. The cloud computing system acquires the data available from the sensors and accesses them. With digital twins, there is the collaboration of a cloud-based system that makes use of IoT and Machine Learning and Artificial Intelligence skills.

So, we see that regardless of the stage of development of robots and bots giving a human touch to these robots by incorporating human intelligence and specific behaviour is what really matters. Below, we have discussed Industry 4.0 and Industry 5.0 for a better understanding of how the manufacturing sector is thriving currently and what all scopes are available for this huge industry with the help of Industry 5.0. It is for sure that Industry 4.0 is something widely discussed and practised. However, Industry 5.0 is not too far to be a reality.

Industry 4.0

In Industry 4.0 there is a huge implementation of automation wherein the machines are interconnected among each other and with the Cloud as well. This resulted in effective communication among the machines and processing of the data gathered to become increasingly smarter. Currently, under Industry 4.0 machines are made smarter without any human communication that proves beneficial in case of successful mass production.

However, with advancing technology customers are aiming to personalize their products starting from their apparel to their cars or vehicles. Through mass production and inherent customization seen in Industry 4.0 manufacturers collaborate with the customization and durability of personalized manufacturing processes to generate products with lower unit cost. This has led to a trend of mass personalization rather than concentrating on a singular order. With mass customization, the businesses are capable of comprehending consumer attributes and offering specific value to their customers by personalizing individual data obtained.

Talking more about Industry 4.0, there have been huge improvements over time. Since the machines are evolving and there is increased demand for customized products, the deadlines set for market time are quite short and there is an increase in product complexity. This makes it harder for manufacturing organizations to maintain top-notch quality and standards. Additionally, the customers’ anticipation of flawless products stimulates manufacturers to improve their quality standards while considering the damages incurred from defects and recalls.

So, to minimize such problems faced, various AI algorithms are utilized to alert the manufacturers of any flaws found in production processes that may lead to quality problems. With this preventive outlook of addressing these problems quite early improved quality level is obtained. In addition to this, Industry 4.0 facilitates manufacturers to gather data relevant to the implementation and functioning of their products in specific sectors. This, in turn, helps the product development authorities to makes use of strategic and prudent design-related decisions.

Industry 5.0

Industry 5.0 is an advanced stage of Industry 4.0 wherein various interconnected systems, developed manufacturing methods, and robots have concurred with cognitive and ingenious thinking abilities of the human mind. As a result of this various crucial manufacturing activities can be performed with enhanced productivity and mass personalization by the collaborative effort of humans and intelligent machines.

While intelligent machines and robots remain engaged in conducting consecutive activities like data mining and operative maintenance humans will be mostly involved in handling smart systems, initiating real-time activities, and practising customization. Various manufacturing units are magnifying the involvement of humans on the shop floors to implement customization and improve efficiency.

You can explore more of Industry 4.0 and Industry 5.0 in this blog posted on our website.


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Thus, we see that Artificial Intelligence is undeniably an essential part of Manufacturing Industry 4.0. It is not just bound to effective implementation in the production floor. In fact, these AI algorithms can be successfully incorporated to maximize the supply chains and to flawlessly implement the market changes in the organization. With Industry 5.0 the operational efficiency if the industrial Ai used will increase such that manufacturing automation will also be accompanied with elevated customized experiences, thanks to critical thinking skills of humans. AI algorithms improvise the overall process efficiency by helping the manufacturing AI to detect location patterns, weather aspects, customer behaviour, economic and political factors, and more.

So, we conclude by saying that Artificial Intelligence is going to revolutionize the manufacturing sector. Starting from designing and applications in production floor to management and supply chains, AI is the ultimate source of unbounded innovations and augmentation in the manufacturing industry.