The hottest machine learning brings great changes

  • Detail

Machine learning has brought great changes to the manufacturing industry

Abstract: automation and machine replacement have solved the problem of labor shortage, but it is still impossible to meet today's small batch and diversified production requirements. Achieving more efficient production requires the integration of many technologies, such as industrial IOT, big data analysis and artificial intelligence

technological progress continues to promote the improvement of human productivity, from traditional manual manufacturing to automated, networked and intelligent production. Today, the new generation of information technology has brought many changes. Artificial intelligence has been gradually applied to many fields such as industrial manufacturing, and has driven huge economic value

traditional manufacturing industry relies on cheap labor to obtain higher returns through mass production. However, today's market is becoming more and more diversified, and the needs of consumers are constantly changing, which requires factories to have the ability to quickly produce different types of products

automation and machine replacement have solved the problem of labor shortage, but they still can't meet today's small batch and diversified production requirements. Achieving more efficient production requires the integration of many technologies, such as industrial IOT, big data analysis and artificial intelligence

equipment maintenance is no longer a guessing game

in the past production system, equipment maintenance personnel usually waited for the machine to break down before they knew the maintenance, and could not predict the downtime of the equipment in advance. For the routine maintenance of machines, most factories adopt the method of regular maintenance to reduce the failure rate of equipment, but the accuracy of this method is low. Even engineers with rich experience in the Jiede experimental machine series judge the possible problems of the equipment by guessing

with the popularization and application of industrial IOT, there is a new definition of machine maintenance. Predictive maintenance brings great convenience to modern factories. Equip the equipment with many sensors, and predict the possible faults of the machine in advance by monitoring the running state of the machine in real time. Machine learning algorithm plays an important role here, which can help managers find machine problems as soon as possible

enterprises can draw lessons from past experience, and the pace of market centers can follow, or sum up experience from similar events. This is the great ability of machine learning. Machine learning can recognize the repeated patterns in data through the understanding and learning of historical big data, and apply them to production judgment, so as to predict trends more accurately and detect production problems in real time. Adopting machine learning to improve the production system is conducive to improving the performance and efficiency of enterprises

intelligent monitoring can effectively prevent downtime

after years of development, sensor technology has become smaller and cheaper, which means that for many companies, it can monitor the machinery and equipment of the whole factory in real time at a lower cost. However, if you want to obtain correct and valuable insights from the data, you need to further screen and analyze the data

it will be a laborious job to analyze these huge data manually. Machine learning is very important here. The intelligent program can monitor the internal action of the machine 24 hours a day. For every part of the equipment, it can even be as small as a button, establish a long-term disease history report in fact table, and analyze the current data in addition to affecting the jaw misalignment, and compare it with the historical case

when the data value of the equipment deviates from the normal state, the system will warn possible faults or failures in advance. In this way, the enterprise can repair the equipment in time before the equipment failure, so as to prevent the shutdown from causing huge production losses. In addition, the analysis of equipment data can enable managers to better understand the current situation of the production system and know how to make more rational use of equipment resources, so as to reduce labor costs and improve product quality

the traditional quality inspection mode will become the past

production quality is the key to enterprise brand and market competitiveness. Machine learning can help enterprises gain more advantages. The traditional production method is to wait for the completion of product production before quality inspection, which means that unqualified products will need to be reworked or scrapped, and the factory wastes not only time but also risk losses. However, this approach may soon become a thing of the past

machine learning solutions will bring disruption to the manufacturing inspection system, that is, under ideal circumstances, traditional testing will be completely replaced in the future. Because machine learning algorithm can help the system detect and control the production quality in the production process. That is, in every production link, we can ensure the successful production of qualified parts

with the continuous improvement of detection technology and measurement accuracy, we can inspect complex parts such as casting pores in the production process, and the software can predict the quality of products from the production process. More interestingly, the self-learning algorithm not only reports predefined errors, but also finds some unknown problems

optimize energy management with pattern recognition

in most factories, a large amount of energy is consumed every day. From power, coal to water resources, a set of scientific energy management schemes can help factories save a lot of money. Artificial intelligence can help enterprises analyze the actual situation of energy use, find out the unreasonable places of energy and optimize it, so as to further reduce production costs

from the perspective of energy suppliers, the mixture of fossil fuels and renewable energy is changing the electricity pattern, which forces power producers and operators to adopt new strategies. Machine learning technology enables power companies to use historical consumption patterns to predict the future in real time, which enables enterprises to adjust cost prices and demand more accurately, and ultimately lead to more efficient operations

autonomous vehicles improve logistics efficiency

the manufacturing of a product usually requires many processes, from taking materials from the warehouse to processing, assembly, debugging, and the intermediate process has a lot of logistics work to complete. More and more enterprises are considering using automated transportation to reduce labor input and create more economic benefits

how to realize more efficient logistics transportation? Machine learning autonomous vehicles are paving the way for automated logistics. Artificial intelligence has become the key technology of automated logistics and internal logistics system. As long as through in-depth study, vehicles can correctly understand and understand the surrounding environment and successfully complete the logistics tasks in production

in the future, driverless transportation system will undertake many tasks. It can combine big data to predict demand and carry out planning work, which will enable the replenishment process to be completed automatically. Machine learning has many application scenarios in the manufacturing industry. Intelligent algorithms can improve the function and performance of equipment and further give play to the efficiency of factory production systems. In the near future, it will bring an unprecedented great change

Copyright © 2011 JIN SHI