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AI in Manufacturing: Uses and Benefits

  • Yashoda Gandhi
  • Jan 23, 2022
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Artificial intelligence (AI) is the ability of a digital computer or a computer-controlled robot to perform tasks commonly associated with intelligent beings. The term generally refers to the effort to create systems with human-like cognitive processes, such as the ability to reason, uncover meaning, generalise, or learn from past experience.


Since the invention of the digital computer in the 1940s, it has been demonstrated that computers can be trained to execute exceedingly complex tasks with great competence, such as proving mathematical theorems or playing chess. 


Nonetheless, despite continual advances in computer processing speed and memory capacity, no programs can match human adaptability in larger domains or occupations requiring a high degree of everyday knowledge.


However, certain computers have outperformed human specialists and professionals in specific tasks, therefore artificial intelligence in narrow sense may be found in applications ranging from medical diagnosis to computer search engines to voice or handwriting recognition.


4 Types of AI


  • Reactive machines


Reactive Machines carry out simple tasks. This is the most basic level of artificial intelligence. These types respond to some input by producing some output. There is no learning taking place. 


This is the first stage of any artificial intelligence system. A simple, reactive machine is one that takes a human face as input and generates a box around it to recognize it as a face. The model does not learn and does not store any inputs.


Reactive machines are static machine learning models. Their architecture is the most basic, and they can be found on GitHub repositories all over the internet. These models are simple to download, trade, pass around, and put into a developer's toolbox.


  • Limited memory


Memory problems By monitoring behaviours or data, AI learns from the past and gains experiential knowledge. This sort of AI makes predictions and performs sophisticated categorization tasks by combining historical, observational data with pre-programmed knowledge. It is the most often utilized type of AI nowadays.


Autonomous vehicles, for example, employ limited memory AI to watch other cars' speed and direction, allowing them to "read the road" and modify as needed. This process of comprehending and analyzing incoming data makes them more secure on the road.


However, memory is a constraint. As the name implies, artificial intelligence (AI) is still in its infancy. The data that autonomous vehicles use is transient, and it is not kept in the car's long-term memory.


  • Theory of mind


Humans have thoughts and feelings, as well as memories and other brain patterns, which drive and impact their behaviour. The work of theory of mind researchers is founded on this psychology, with the goal of developing computers that can mimic human mental models. That is machines that grasp that people and animals have ideas and feelings that influence their behaviour.


This idea of mind enables people to interact socially and establish communities. Theory of mind devices would be required to utilize and learn from knowledge collected from people, which would then inform how the machine interacts in or reacts to a new circumstance.


  • Self aware


Self-awareness is the last form of AI. This will be the point at which machines are aware of not only the emotions and mental states of others but also of their own. When self-aware AI is realized, we will have AI with human-level awareness and intellect, as well as the same wants, desires, and emotions.


At the time, this AI has not been built effectively since we lack the necessary technology and algorithms. AI researchers will continue to improve limited memory AI and focus on the theory of mind AI.


(Related reading: Major branches of AI)


Benefits of AI in manufacturing


  1. Quality Control


The key advantage of artificial intelligence in manufacturing may be quality assurance. Machine learning models may be used by businesses to discover deviations from normal design specifications and uncover faults or inconsistencies that the ordinary human may not notice.


Incorporating machine learning techniques into the quality assurance process increases product quality while saving money and time.


  1. Quick decision making


Companies may share simulations, confer on industrial activities, and transmit crucial or significant information in real-time when IIoT is combined with cloud computing and virtual or augmented reality, regardless of geographical location. 


The data collected by sensors and beacons aids in determining customer behaviour, helping businesses to predict future demands and make quick manufacturing choices, as well as speeding up the interchange between manufacturers and suppliers.


  1. Preventative Maintenance


Another advantage of machine learning is preventative maintenance. When the AI platform can forecast which components need to be changed before an outage happens, you can identify issues before they occur and guarantee that production does not have to be halted due to equipment failure.


Uses of AI in manufacturing


  1. Helps predict failure in design


Failures of machinery are widespread in the manufacturing business, resulting in greater downtime, higher costs, and a longer time to market. The failure to discover problems in advance might have a detrimental influence on the final product's quality and performance. This is where AI in the industry comes into play in the industrial industry.


AI helps spot product or equipment faults far in advance through predictive learning, preventing significant breakdowns in the future. This reduces downtime, saves money on idle time, and ensures increased production.


  1. Quality check


It is one of the most effective AI use cases in the industrial industry. Internal equipment flaws are difficult to discover. Experts are sometimes unable to spot defects in items by examining their operation. 


However, artificial intelligence (AI) and machine learning (ML) technologies are capable of doing so efficiently. Minor defects in equipment may also be easily recognized using AI systems, tools as well as AI applications.


As a result, AI in the manufacturing sector ensures quality control. Smart AI systems monitor machinery productivity, track performance, detect defects, increase productivity, and decrease maintenance costs. As a result, most industrial organizations include AI automation in their production processes.


  1. Predictive maintenance


A single flaw in equipment can cause severe disruption to the whole manufacturing process, increasing downtime and total expenses. As a result, thorough and timely machinery maintenance is critical. Unfortunately, this is frequently neglected unless there is a catastrophic breakdown.


To solve these issues, industrial units are already deploying ML-powered predictive tools and AI solutions that can forecast when equipment requires routine maintenance. 


In certain cases, IoT and cloud sensors are incorporated in equipment, which aids in the prediction of a timely repair. This also guarantees that any big equipment concerns that may develop in the future are overcome.


Implementing AI-powered manufacturing solutions may aid in the automation of processes, allowing firms to create smart operations that cut costs and downtime.


  1. Predict equipment failure


It is yet another important AI application in the manufacturing sector. The most prevalent use of AI and machine learning in manufacturing is to increase equipment efficiency. 


Manufacturers confront difficulties as a result of unexpected mechanical malfunctions. A product may appear excellent from the outside, yet it performs poorly when we use it. Productivity is impacted.


It is the second most important reason for the increased demand for AI in the manufacturing sector. Using the capabilities of AI, ML, and predictive analytics technologies, AI development businesses are developing best-in-class robotic solutions and predictive maintenance systems that provide early warnings of equipment degradation and avoid unintentional shutdowns of machinery.


  1. Inventory management


Inefficient inventory management can result in significant cost overruns for a manufacturing company. Using AI technology, manufacturers may manage their order records and add/delete new inventory. Machine Learning is crucial in managing stocks depending on demand and availability.


Manufacturing processes are being transformed by artificial intelligence. AI can assist you in transforming business operations, improving product quality, and reducing costs.


AI technologies have advanced dramatically in recent years. It has an influence on every industry, including manufacturing. Here are four major ways that AI technology is impacting manufacturers.





Manufacturers are progressively using AI robots in the manufacturing process to provide a safer workplace and increase efficiency. Manufacturers may use AI to discover product flaws as well as quality and design concerns. They can also produce hundreds of product designs in a matter of seconds using a combination of AI, ML, and industrial revolution technologies. 


Such design options aid producers in developing end-products with a distinct structure. As previously said, AI solutions assist manufacturers in managing inventory and balancing supply and demand. 


AI inventory management solutions for manufacturing, as well as AI demand forecasting apps and tools, assist manufacturing organizations in managing inventory levels and retaining lucrative customers.


(Also read: AI for Environmental Sustainability)

Latest Comments

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    Oct 06, 2022

    AI in manufacturing has become the major trend in recent time. Thanks for the informative article.

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