Artificial intelligence is growing, but exact numbers can be difficult to come by, as the definition of technologies such as machine learning, artificial intelligence, machine vision, and others is often blurred. For example, the use of a robotic arm and camera to inspect parts could be advertised as artificial intelligence or machine learning device. While the device might work fine, it may only compare images taken with others that were manually added to a library. Some would argue that this is not a machine learning device, as you are making a pre-programmed decision, not a "learned" one from machine experience.
AI and ML use in Manufacturing Industry:
Quality Improvement:
In the modern world of short lead times and the increased level of complexity of products, it becomes even more difficult to meet the highest standards and regulations in terms of quality. Customers expect flawless products. In addition, product defects can cause recalls, greatly damaging the reputation of the company and its brand. AI can alert companies to problems on the production line that can result in quality problems. These flaws can be major or subtle, but they all influence the overall level of production and could be eliminated in the early stages.
Machine vision, for example, is an Artificial Intelligence Development Service that uses high-resolution cameras to monitor defects much better than a human. Artificial Intelligence could be connected with a cloud-based data processing framework that creates an automatic response. In addition, manufacturers can obtain data on the performance of their products when they reach the market to make better strategic decisions in the future.
Market Adaptation:
Artificial Intelligence and Machine Learning are already key elements of the Factory, but they can also increase supply chains, creating them interactive to changes in the market. Therefore, managers can improve their strategic vision by relying on AI suggestions. AI generates estimates based on linking a number of factors such as political situations, weather, consumer behavior and the state of the economy. Personnel, inventory and supply of materials could be calculated according to the predictions.
The world's largest companies are already using machine learning and Artificial Intelligence in Manufacturing and investing millions in their development. These are some of the most prominent examples of companies that use it.
Smart Maintenance:
Image Source: activesilicon
Being a very important part of all asset-dependent production operations, equipment maintenance is one of the biggest expenses in the manufacturing industry - unplanned downtime costs plants and factories nearly $ 50K million, 42% of that is due to asset failures.
That is why predictive maintenance became a vital solution that will help save a huge amount of money. Complex AI algorithms such as neural networks and Machine Learning Development Services are generating reliable predictions about the state of assets and machinery. Equipment Remaining Life (RUL) becomes significantly longer. If something needs to be repaired or replaced, the technicians will know beforehand and even know what methods to use to fix the problem.
Better product development:
Generative design is the method that allows you to put a detailed report created by humans in an AI algorithm. The information in the summary can contain different parameters such as available production resources, budget and time. The algorithm examines all possible variations and generates some optimal solutions. This set of solutions can be evaluated using pre-trained Deep Learning Development Service Solutions, which can add more knowledge and choose certain options. You can go through this process as many times as you like to decide on the perfect one. Artificial intelligence is completely objective with no untested assumptions unlike humans.
Search Engine:
There are search engines available during the search to provide the best results to customers. There are many machine learning algorithms created to find the query of a particular user, such as Google. Whatever page users are opening for a particular topic, it will often stay at the top of the page for a long time.
Customer Support:
Most reputable companies or many websites offer the option of chatting with a customer service representative. So after making any query from the client, it is not mandatory that the answer is given only by the human, sometimes the answers are given by the chatbot that extracts the information from the website and provides the answer to the clients. Now they are better and understand queries quickly and quickly and also provide the good result by giving appropriate results and it is done only by Uses of Machine Learning in Manufacturing.
Social media platform:
Social media is used to provide better news and advertising based on user interest, primarily through the use of machine learning. There are many examples like friend suggestions, Facebook page suggestions, song suggestions and YouTube videos. It mainly works on the simple concept on the basis of the user experience, with which he connects and visits the profiles or websites very frequently, the suggestions are provided to the user accordingly.
Must Read: The Cost to Development of Artificial Intelligence
Popular machine learning and Artificial Intelligence apps:
Image recognition: Image recognition is one of the most important and notable artificial intelligence and machine learning techniques - an approach to cataloging and detecting a feature or an object in the digital image. This technique is being adopted for further analysis such as pattern recognition, face detection, or face recognition.
Image Source: techradar
Language translation: Machine learning plays an important role in translating from one language to another. We are in awe of how websites can be translated from one language to another effortlessly and also give contextual meaning. It has allowed the world to interact with people from all corners of the world; without him, life would not be as easy as it is now. He has given travelers and business partners a kind of confidence to safely venture into foreign lands with the conviction that language will no longer be a barrier.
Product Recommendations: One of the most popular and well-known applications of machine learning is product recommendation. Product recommendation is one of the main features of almost every e-commerce website today, which is an advanced application of machine learning techniques.
Using machine learning and artificial intelligence, websites track your behavior based on your previous purchase, your search pattern, your cart history, and make product recommendations.
Analysis of feelings:
Sentiment analysis is a real-time Machine Learning Applications In the Manufacturing Industry that determines the emotion or opinion of the speaker or writer. For example, if someone has written a review or email (or any form of document), a sentiment analyzer will instantly discover the actual thought and tone of the text. This sentiment analysis app can be used to analyze a review-based website, decision-making apps, etc.
Computer vision:
Closely tied to industrial robotics, computer vision Use cases of Artificial Intelligence in the Manufacturing industry space often involve visual inspections. Artificial intelligence has two benefits over humans when it comes to visual version: fast and accurate. A computer vision system that uses cameras that are more sensitive than the naked eye and augmented with AI can identify microscopic defects that human inspectors could miss at a rate they cannot hope to match.
Predictive analytics:
The basic idea is to leverage the data generated before, during and after the production process to obtain information on product quality or predictions about future product failures. This is definitely a job for AI, as the sheer volume of manufacturing data being generated makes it impossible for insignificant human minds to understand all the various and diverse relationships between signals.
Industrial robotics:
Robots and AI go hand in hand like apple pie and ice cream, peanut butter and chocolate, or Wookies and Ewoks - good on their own, but awesome in combination. Although they have been used for more than half a century, industrial robots have been changing their image in recent decades, moving from coldly competing against human workers, to supplant them with ruthless efficiency; too friendly helpers who can make life easier for line workers instead of robbing them of their livelihoods. At the center of this change are collaborative robots, or cobots, which are specifically designed to work with humans.
Predictive maintenance with machine learning:
Image Source: reliableplant
Rather than fixing failures when they occur or scheduling equipment inspections, it is better to predict problems before they occur. Using time-series data, machine learning algorithms tune the predictive maintenance system to analyze failure patterns and predict potential problems.
When sensors track parameters such as humidity, temperature, or density, this data is collected and processed by a machine learning algorithm. There are several machine learning models that can predict equipment failure.
Depending on the goal of the prediction: time remaining before failing, getting probabilities of failure or anomalies, there are several approaches to machine learning development:
- Regression models for prediction of remaining useful life (RUL). Using both historical and static data, this method allows you to predict how many days are left before a failure.
- Classification models for predicting a failure within a predefined period of time. To define how soon the machine will fail, we can develop a model that predicts failures within a defined number of days.
- Anomaly detection models for marking devices. This approach allows you to predict failures by identifying differences between normal system behavior and failure events.
The key benefits of machine learning-based predictive maintenance are accuracy and speed. By revealing anomalies in production devices, analyzing their nature and frequency, it is possible to optimize performance before failure occurs.
AI to build digital twins:
It is a virtual copy of a physical manufacturing method. In the manufacturing area, there are digital twins of specific machinery assets, complete machinery systems, or components of particular systems. The most common uses for digital twins are real-time diagnosis and evaluation of the production process, prediction and visualization of product performance, among others.
To teach digital twin models to understand how to optimize the physical system, data science engineers use supervised and unsupervised machine learning algorithms. By processing raw and historical data collected from continuous real-time monitoring, machine learning algorithms look for patterns of behavior and find anomalies. These algorithms help optimize production scheduling, quality improvements, and maintenance.
In addition, the use of NLP techniques provides the ability to process external data from research, industry reports, social media and media. Enhance the functionality of digital twins not only to design a future product but also to simulate its performance.
Conclusion:
Technology is moving faster, losing the Difference between Artificial Intelligence and Machine Learning waves could mean being stranded, and it's harder to compete if the company falls behind. Machine Learning is not just an important key to competitive use, it is now a need for survival in many industries. On the other hand, companies are starting to realize that they don't need to climb a mountain of ML and AI, they need to keep taking the right, small, and necessary steps to reach new heights. However, many companies have legacy teams that do not provide the data or send it to other locations and also other factors such as trust, maturity, scalability, ROI and connectivity are holding back the adoption of AI in some cases.
USM Business Systems is the best company for Artificial Intelligence Development, Human Resource Management Systems, Mobile Application Development, Chatbot Development, data quality solutions, workforce service to create interactive experiences for major platforms. USM also provides Artificial Intelligence in Retail and Artificial Intelligence in Manufacturing.
WRITTEN BY
Koteshwar Reddy
I'm a tech assistant. and content researcher at USM. I share my knowledge about information in modern technologies.
