Deep learning (DL) is a branch of artificial intelligence (AI) that investigates and comprehends data patterns, relationships, and themes to facilitate learning, processing, and decision-making without using humans. In its most basic form, deep learning makes computers and machines "intelligent" enough to expand their understanding and enable them to make decisions and predictions similar to humans.
Deep learning solutions approaches have grown in popularity over the last two decades due to various causes, including improved accessibility and capabilities of existing deep learning systems. To train a machine with massive volumes of data is fed into deep learning algorithms, which then analyze and deliver data-driven and data-centric suggestions based only on the data presented. Industry leaders are implementing deep learning in various aspects of manufacturing, including data analysis, process management, optimization, monitoring, control, and diagnostics.
The ability of deep learning services to efficiently train from and adjust to varied settings is a significant feature. Given the difficulties of a rapidly changing and uncertain industrial environment, ML, as part of AI, can learn and adapt to changes, removing the need for owners, managers, designers, and engineers to predict and respond to all potential circumstances throughout the entire production process. As a result, given most first-principle modeling techniques' difficulties in dealing with adaptation, deep learning (DL) algorithm makes a convincing case for its use in manufacturing.
The deep learning algorithm can analyze the data to improve various process segments, including performance enhancement, tracking and management applications, and predictive maintenance. Deep learning solutions can potentially improve quality control optimization in production systems, especially in complex manufacturing environments where trouble spots are difficult. Deep learning developers can help reduce cycle time and waste while increasing resource utilization in specific NP-hard manufacturing situations. Furthermore, deep learning provides powerful tools for continuous quality improvement in large and complex production processes.
One of the essential goals of deep learning solutions is to produce high-quality results at the lowest possible cost. It is making things that may be enormously costly and time-consuming for businesses lacking the necessary resources and talents to create and manufacture high-quality items. However, the manufacturing industry's face has changed dramatically in recent years due to deep learning in manufacturing. The use case application list below illustrates the most common areas where you can implement deep learning algorithms in the manufacturing industry.
Manufacturers applying deep learning algorithms in their processes get a high-quality reputation in the market for a better customer satisfaction index. From industrial efficiency to product personalization may be transformed with the possibility of connectivity, intelligent analytics, automation, and more. By merging these technologies, manufacturers can improve time to market and service efficacy and create a new business model for enhanced productivity.
Predictive Maintenance is a modern technique in which you monitor your rotating equipment using sensors that measure characteristics such as vibration, temperature, magnetic flux, etc. The health of assets gets assessed on your cloud platform and can be viewed over the internet globally.
The use of AI/ML in logistics and SCM entails developing self-learning intelligent robots that can assist with strenuous activities such as moving heavy items or lifting objects to avoid workplace hazards, as well as tackling the problem of overstacking or understocking commodities. For example, food manufacturer Danone Group uses machine ML to improve their demand vs. supply estimate.
Smart manufacturing includes intelligent robots, AI-powered robots that do not need to be programmed each time they do essential duties. Furthermore, because they are robots, they do not require a break (or a long break) from their jobs and can execute repetitive activities without complaint. According to a McKinsey analysis, collaborative and context-aware robots can increase productivity by up to 20% in labor-intensive areas.
Some examples of companies using smart manufacturing inside their plants are Fanuc – a Japanese automation company, and BMW Group.
Product development does not end with simply selling a product to a buyer; it continues with customer service to fully please a consumer. Deep learning algorithms always play a vital role in this domain.
Smart AI applications produced using the finished product can assist customers by swiftly identifying their problems and giving superior solutions. Furthermore, due to its cognitive powers, the software may self-learn and thus provide a more tailored experience to customers over time.
It may surprise you, but AI/DL technology is now advanced enough to generate new product designs. A generative design program with deep learning capabilities, in which engineers only need to enter input parameters such as raw material, size and weight, production processes, budget, and so on, can generate several product designs from which to choose. Nissan is one example of how AI is used in the design process. The deep learning algorithm can learn and grow on its own by analyzing and making connections between various data points. This quality has made deep learning a powerful tool in many fields.
One of the most promising applications of deep learning is in the field of manufacturing. By using deep understanding, manufacturers can create more intelligent factories to produce higher-quality products more efficiently. If you are a manufacturer, there are a few ways you can utilize deep learning to create a brilliant factory—this blog overviews how deep learning understanding is used in manufacturing and gives tips on getting started.