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Deep learning in the manufacturing industry
Introduction to Deep Learning for Manufacturing:
Before getting into the details of deep learning for manufacturing, it’s good to take a step back and see a brief history. Concepts, original thinking, and physical inventions have been shaping the world economy and manufacturing industry since the beginning of the modern era, that is, in the early 18th century.
Ideas of economies of scale from the likes of Adam Smith and John Stuart Mill, the first industrial revolution and steam engines, the electrification of factories and the second industrial revolution and the introduction of the assembly line method by Henry Ford, are just a few prime examples of how the pursuit of high efficiency and improved productivity has always been at the heart of manufacturing.
Yet almost all of these inventions were focused on extracting maximum efficiency from men and machines by carefully manipulating the laws of mechanics and thermodynamics. However deep learning company in USA, over the past few decades, the biggest new gains in manufacturing have come from adding the concept of information or data to the existing mix.
How can AI help to manufacture?
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 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.
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. Additionally, product defects can lead to recalls, greatly damaging the reputation of the company and its brand. AI services in USA 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 company in USA that uses high-resolution cameras to monitor defects much better than a human. It could be combined with a cloud-based data processing framework that generates 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.
What are the deep learning uses in manufacturing?
Predictive maintenance is gaining popularity over preventive maintenance. This could be partly due to more efficient predictive maintenance thanks to deep learning solutions.
Predictive analytics based on deep learning is used to prevent machine failure by identifying potential upcoming problems with greater precision. Machine learning development company in USA analyzes sound, images and other data in real-time from sensors installed in equipment to reduce system downtime.
Supply chain management:
Deep learning is a novel, data-hungry, and high-precision analytical approach. Thus, you can add value in the complex supply chain management space where simple algorithms cannot achieve high levels of precision.
Real-time demand forecast:
Optimize your supply chain operations and production schedules.
Achieving efficient inventory management helps reduce raw material purchasing costs
These capabilities allow companies to respond quickly to changing market demand, such as a tanker stuck in the Suez Canal.
For logistics operations, deep learning models can increase fuel efficiency and delivery time by analyzing real-time data about the vehicle and the driver.
If you’re ready to use deep learning in your business, we’ve put together a data-driven list of companies offering deep learning platforms.
Feel free to read our AI applications in manufacturing research.
Read More: How Much Does Artificial Intelligence Cost?
Intelligent manufacturing enabled by information systems has increased the productivity and quality of industrial organizations, large and small, for many decades. In this smart manufacturing environment, the use of data analytics, statistical modeling, and predictive algorithms has increased by leaps and bounds, as the quality and propensity of the machine and human-generated data improved over time. The industry, which began with Henry Ford’s assembly line at the beginning of the last century, was favored during the 20th century by innovations in automation, control systems, electronics, sensors, digital computing and the Internet. The big data revolution of the 21st century is poised to finally take it to a whole new level by unlocking opportunities for exponential growth.
To take full advantage of this explosion of data, deep learning and associated AI chatbot development company in USA techniques must be integrated into the toolset of modern manufacturing systems, as they are exponentially more powerful than classical statistical learning and prediction systems.
Deep learning can be seamlessly integrated with the ambitious goals of Industry 4.0: extreme automation and digital factory. Industry 4.0 is designed around the constant connection to information: sensors, drives, valves, all working together with a single common goal: minimize downtime and increase efficiency. Algorithmic frameworks like a deep neural network, which is flexible enough to work with a variety of data types as they are continuously transmitted, are the right choice to handle that particular type of task.
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