While using Google, we get an option of “Search by voice,” it comes under speech recognition, and it’s a popular application of machine learning. Speech recognition is a process of converting voice instructions into text, and it is also known as “Speech to text”, or “Computer speech recognition.” At present, machine learning algorithms are widely used in various applications of speech recognition. Google Assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow voice instructions.
Facebook provides us with a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning’s face detection and recognition algorithm.
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc.
Machine learning is a buzzword for today’s technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google Assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning:
In applications where belt drives on a pulley, they are used in automotive engines such as cars, trucks, buses, stationary power generators, marine engines, aviation engines, etc. Belt and pulley are also used in air compressor engines, agricultural equipment, conveyors, etc. in fact, a belt, and pulley are purposely designed for any application that requires a rotational motion from another source. There are tons of machines and equipment out there from larger ones to smaller ones such as winches, treadmills and washing machines, etc. A single pulley that changes the direction of force is used on cranes, well to lift out a bucket, raise a flag, adjust a window blind, etc.
The fatigue life of austenitic steel can be characterized by Manson–The coffin curve or Wöhler curve. Both these characteristics depend on temperature and environment. The dependence on the environment increases with increasing temperature. Figure 20 shows the Manson–Coffin curve of two types of 316L steel in a wide domain of plastic strain amplitudes at room temperature (Polák et al., 1994). 316L-TH steel had a larger grain size than 316L-VZ. In 316L-VZ steel, a small fraction of sigma-phase has been identified. In the low-cycle domain, both curves coincide while in the high-cycle domain the slope of the 316L-TH steel is higher and the fatigue life shifts to lower the number of cycles in comparison with 316L-VZ steel. These changes in Manson–Coffin curve are connected with easier initiation of the fatigue cracks in large grains of 316L-TH steel. Longer fatigue cracks are initiated than in 316L-VZ steel and they propagate more rapidly.
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