Machine learning is a branch of artificial intelligence that works through algorithms that process the data constantly received by the machine. Therefore, we can state that the system learns without the need to be programmed. In this way, it recognizes patterns and is capable of drawing conclusions on its own. <\/p>\n
Machine learning and Industry 4.0<\/h2>\n
Extracting value from the data generated by the daily activities of a company is key to improving processes, and therefore, machine learning must be highly present.<\/p>\n
This implies that it is necessary to eradicate the mindset of many <\/span>industrial managers<\/a> <\/span>some ideas that hinder this growth. Notably, we can refer to the <\/span>report<\/a> <\/span>IDC report that highlights the main obstacles in this regard:<\/p>\n But we also have positive figures that give reason for hope. For example, we have the <\/span>presented by Fujitsu<\/a> <\/span>in their study on predictive maintenance and Industry 4.0. According to this, more than half of the participants indicate that their most critical assets are sensorized with devices from <\/span>IoT (Internet of Things)<\/a>.<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.8.2″ text_font=”|300|||||||” min_height=”55px” custom_margin=”||2px|||” custom_padding=”||17px|||” link_option_url_new_window=”on” hover_enabled=”0″ sticky_enabled=”0″] Machine learning is a valuable resource throughout almost the entire <\/span>supply chain<\/a>. Therefore, the application of these tools is one of the essentials of Industry 4.0. Among the main benefits that companies will enjoy by implementing them are: <\/p>\n All these advantages, and many more, are achieved thanks to an important and diverse list of <\/span>applications that this technology has in industrial plants<\/a>. Here are 5 of the most notable ones: <\/p>\n We have already talked about the predictive capability that machine learning provides. This influences the planning of machinery maintenance operations. Wouldn’t it be great to know in advance which equipment is most likely to fail and when it will happen? This is possible thanks to machine learning, shaping the concept of <\/span>predictive maintenance<\/a>.<\/p>\n With this, we can anticipate and establish repair protocols that prevent the machine from breaking down completely and being unable to function for a period of time. Remember, downtime equals economic losses. <\/p>\n Artificial vision, computer vision, or machine vision are the different ways to refer to this technology that combines machine learning algorithms with images received from a camera.<\/p>\n In this way, the algorithms will process the collected images and extract value from them. This greatly multiplies the speed and reliability of visual inspections, eliminating human factors such as fatigue and biases inherent to our condition. <\/p>\n It is important to highlight how quality control of raw materials, intermediate products, and finished goods has evolved thanks to the contribution of machine learning.<\/p>\n In the previous section, we referred to artificial vision. Well, one of its main uses is to determine the quality of resources on the production line. For example, with computer vision, it is easier and faster to identify defects, some of which are imperceptible to the human eye. <\/p>\n But machine learning and quality are not only related to artificial vision; there are also variables that are measured with sensors, such as temperature, pressure, vibration, etc. Data that is continuously collected and processed. For example, in the food industry, detecting packaging temperatures below the established threshold can be indicative of the fact that the product will not be suitable for consumption. <\/p>\n Machine learning can work with all kinds of information within the plant, data related to both material and human resources.<\/p>\n This means that with the interpreted information we can extract from machine learning systems, we have a more realistic and updated view of everything that is happening in the <\/span>smart factory<\/a>. This way, managers will be able to define truly effective strategies that align with what the situation really demands. <\/p>\n A clear example of this is the possibility of <\/span>optimally organizing employees’ tasks<\/a>, so that they align with their workload, skills, experience, authorizations, and even <\/span>location<\/a>.<\/p>\n Finally, we have a point that we can relate to some of the previous ones, such as the <\/span>quality control<\/a> <\/span>and artificial vision. Both sensors and cameras help identify aspects that will be decisive for classifying products based on the measured parameters. <\/p>\n Let’s see an example of this. We have a company that collects and markets oranges. Depending on the presence of certain defects recognized through computer vision, some oranges will go directly to the market; others, more defective, will be used to make juice; and there will be others that will have to be discarded. <\/p>\n As you can see, machine learning is not a technology you should overlook within the <\/span>operations control<\/a> <\/span>in your industry. In fact, successful companies have been using it in their routines for years. <\/p>\n Are you going to be less? Since <\/span>Sixphere<\/a> <\/span>We provide you with solutions that apply the principles of this technology so that your factory hides no secrets from you. Do you want to know more? Here we are.<\/p>[\/et_pb_text][et_pb_button button_url=”https:\/\/puntazo.sixphere.com\/polaris\/” url_new_window=”on” button_text=”Discover how to intelligently manage your production” button_alignment=”center” _builder_version=”4.8.2″ _module_preset=”default” custom_button=”on” button_text_color=”#000000″ hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” prev_background_color=”#ffffff” _builder_version=”3.24″ use_background_color_gradient=”on” background_color_gradient_start=”rgba(104,168,222,0.1)” background_color_gradient_end=”rgba(41,196,169,0)” min_height=”477px” top_divider_style=”slant” top_divider_height=”8px”][et_pb_column type=”2_3″ specialty_columns=”2″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_row_inner _builder_version=”3.25″ custom_padding=”22px|||||”][et_pb_column_inner saved_specialty_column_type=”2_3″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blog fullwidth=”off” posts_number=”2″ include_categories=”current” meta_date=”j M Y” show_author=”off” show_date=”off” show_categories=”off” show_pagination=”off” _builder_version=”3.24″ header_level=”h4″ header_font=”||||||||” header_text_align=”center” body_font=”|300|||||||” body_text_align=”center” meta_font=”||||||||” meta_text_align=”center” read_more_font=”|600||on|||||” read_more_text_color=”#dc911b” custom_padding=”|25px||25px” animation_style=”fade” border_radii=”on|4px|4px|4px|4px” border_width_all=”0px” box_shadow_style=”preset1″][\/et_pb_blog][et_pb_button button_url=”\/blog” button_text=”All blogs” button_alignment=”center” _builder_version=”3.23.3″ custom_button=”on” button_text_color=”#67ab6a ” button_font=”||||||||” custom_margin=”||0px|||”][\/et_pb_button][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_cta title=”The success stories of Sixphere” button_url=”\/casos-de-exito” button_text=”Success stories” _builder_version=”3.23.3″ header_font=”||||||||” header_text_align=”right” header_font_size=”30px” background_color=”#67ab6a” text_orientation=”right” custom_margin=”|16px||16px” custom_padding=”|16px||16px” animation_style=”slide” animation_direction=”right” border_radii=”on|4px|4px|4px|4px” box_shadow_style=”preset1″] <\/i><\/p>\n Do you want to know what we do and how we do it? Visit our success stories and ask us anything you need to know. <\/p>[\/et_pb_cta][\/et_pb_column][\/et_pb_section]\n","protected":false},"excerpt":{"rendered":" As we know you like to stay up to date with the latest technology applied to your factory, the term machine learning won’t be unfamiliar to you. You\u2019ve probably heard it many times and might even know its basic principles. But do you truly know in which aspects of your plant it\u2019s really useful and how this is possible? Don\u2019t put it off any longer and dive into this reading with us. <\/p>\n","protected":false},"author":3,"featured_media":23958,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","footnotes":""},"categories":[195,182,169],"tags":[191,172,196],"class_list":["post-24612","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ia-machine-learning-en","category-industry-4-0","category-production","tag-industrial-sector","tag-industry-4-0","tag-machine-learning-en"],"acf":{"cuerpo":" Machine learning is the process by which a functional unit improves its performance by acquiring new knowledge or skills, or by reorganizing existing knowledge or skills. This is the definition provided by the International Organization for Standardization (ISO) in the standard <\/span>ISO\/IEC 2382-31:1997<\/a>, on information technology.<\/p>\n Machine learning is a branch of artificial intelligence that works through algorithms that process the data constantly received by the machine. Therefore, we can state that the system learns without the need to be programmed. In this way, it recognizes patterns and is capable of drawing conclusions on its own. <\/p>\n Extracting value from the data generated by the daily activities of a company is key to improving processes, and therefore, machine learning must be highly present.<\/p>\n This implies that it is necessary to eradicate the mindset of many <\/span>industrial managers<\/a> <\/span>some ideas that hinder this growth. Notably, we can refer to the <\/span>report<\/a> <\/span>IDC report that highlights the main obstacles in this regard:<\/p>\n But we also have positive figures that give reason for hope. For example, we have the <\/span>presented by Fujitsu<\/a> <\/span>in their study on predictive maintenance and Industry 4.0. According to this, more than half of the participants indicate that their most critical assets are sensorized with devices from <\/span>IoT (Internet of Things)<\/a>.<\/p> Machine learning is a valuable resource throughout almost the entire <\/span>supply chain<\/a>. Therefore, the application of these tools is one of the essentials of Industry 4.0. Among the main benefits that companies will enjoy by implementing them are: <\/p>\n All these advantages, and many more, are achieved thanks to an important and diverse list of <\/span>applications that this technology has in industrial plants<\/a>. Here are 5 of the most notable ones: <\/p>\n We have already talked about the predictive capability that machine learning provides. This influences the planning of machinery maintenance operations. Wouldn't it be great to know in advance which equipment is most likely to fail and when it will happen? This is possible thanks to machine learning, shaping the concept of <\/span>predictive maintenance<\/a>.<\/p>\n With this, we can anticipate and establish repair protocols that prevent the machine from breaking down completely and being unable to function for a period of time. Remember, downtime equals economic losses. <\/p>\n Artificial vision, computer vision, or machine vision are the different ways to refer to this technology that combines machine learning algorithms with images received from a camera.<\/p>\n In this way, the algorithms will process the collected images and extract value from them. This greatly multiplies the speed and reliability of visual inspections, eliminating human factors such as fatigue and biases inherent to our condition. <\/p>\n It is important to highlight how quality control of raw materials, intermediate products, and finished goods has evolved thanks to the contribution of machine learning.<\/p>\n In the previous section, we referred to artificial vision. Well, one of its main uses is to determine the quality of resources on the production line. For example, with computer vision, it is easier and faster to identify defects, some of which are imperceptible to the human eye. <\/p>\n But machine learning and quality are not only related to artificial vision; there are also variables that are measured with sensors, such as temperature, pressure, vibration, etc. Data that is continuously collected and processed. For example, in the food industry, detecting packaging temperatures below the established threshold can be indicative of the fact that the product will not be suitable for consumption. <\/p>\n Machine learning can work with all kinds of information within the plant, data related to both material and human resources.<\/p>\n This means that with the interpreted information we can extract from machine learning systems, we have a more realistic and updated view of everything that is happening in the <\/span>smart factory<\/a>. This way, managers will be able to define truly effective strategies that align with what the situation really demands. <\/p>\n A clear example of this is the possibility of <\/span>optimally organizing employees' tasks<\/a>, so that they align with their workload, skills, experience, authorizations, and even <\/span>location<\/a>.<\/p>\n Finally, we have a point that we can relate to some of the previous ones, such as the <\/span>quality control<\/a> <\/span>and artificial vision. Both sensors and cameras help identify aspects that will be decisive for classifying products based on the measured parameters. <\/p>\n Let's see an example of this. We have a company that collects and markets oranges. Depending on the presence of certain defects recognized through computer vision, some oranges will go directly to the market; others, more defective, will be used to make juice; and there will be others that will have to be discarded. <\/p>\n As you can see, machine learning is not a technology you should overlook within the <\/span>operations control<\/a> <\/span>in your industry. In fact, successful companies have been using it in their routines for years. <\/p>\n\n
Advantages of applying machine learning in industry<\/h2>\n
\n
5 applications of machine learning in industry<\/h2>\n
\n
1.- Predictive maintenance<\/h3>\n
2.- Artificial vision techniques<\/h3>\n
3.- Quality control<\/h3>\n
4.- Resource optimization<\/h3>\n
5.- Product classification<\/h3>\n
What is machine learning?<\/h2>\n
Machine learning and Industry 4.0<\/h2>\n
\n
Advantages of applying machine learning in industry<\/h2>\n
\n
![\"SIXPHERE](\"https:\/\/sixphere.com\/wp-content\/uploads\/2021\/02\/SIXPHERE-Machine-learning-en-la-industria.jpg\")
5 applications of machine learning in industry<\/h2>\n
\n
1.- Predictive maintenance<\/h3>\n
2.- Artificial vision techniques<\/h3>\n
3.- Quality control<\/h3>\n
4.- Resource optimization<\/h3>\n
5.- Product classification<\/h3>\n