What is popular in the manufacturing industry today? I think it’s going to be digital conversion, Industry 4.0, artificial intelligence… Let’s take a look at how AI is changing manufacturing.
1. Deep Learning for defect detection
In Manufacturing, the process of defect detection in the production line becomes more and more intelligent. Deep neural network integration enables computer systems to identify surface defects such as scratches, cracks, and leaks.
By applying image classification, object detection and instance segmentation algorithms, data scientists can train visual inspection systems to detect defects in a given task. Combining a high optical resolution camera with a GPU, a deep learning driven detection system would be more perceptible than traditional machine vision.
For example, coca-cola has built an AI-based visual inspection application. The application diagnoses the facility system and detects problems, then notifies the technical experts of the problems detected, enabling them to take further action.
2. Predictive maintenance via machine learning
Rather than fixing a problem or arranging equipment inspections in the event of a failure, it is better to anticipate problems before they occur.
By using time series data, ML algorithms can fine-tune predictive maintenance systems to analyze fault patterns and predict possible problems.
When the sensor tracks parameters such as humidity, temperature, or density, the data is collected and processed using machine learning algorithms.
There are several ML models that can predict equipment failure based on predictive goals, such as the time remaining before a failure, and obtaining failure probabilities or exceptions.
2.1. Regression model for predicting the remaining useful life (Rul).
By using both historical and static data, this method can predict how many days remain before the failure.
2.2. A classification model for predicting faults in a predetermined period of time.
To define when a machine is about to fail, we can develop a model that predicts failure within a defined number of days.
2.3. The anomaly detection model can mark the device.
This approach can predict faults by identifying differences between normal system behavior and fault events.
The main benefits of predictive maintenance based on machine learning are accuracy and timeliness. By revealing anomalies in production equipment and analyzing their nature and frequency, performance can be optimized before failures occur.
3. Artificial intelligence will create digital twins
Digital twins are virtual copies of physical production systems. In Manufacturing, there are digital twins made up of specific mechanical assets, whole mechanical systems, or components of specific systems. The most common uses of digital twins are real-time diagnosis and evaluation of production processes, prediction and visualization of product performance, etc.
To teach the digital twin model how to optimize physical systems, data science engineers use supervised and unsupervised machine learning algorithms. By processing historical and unlabeled data collected from continuous real-time monitoring, ML algorithms can look for patterns of behavior and look for anomalies. These algorithms help to optimize production planning, quality improvement, and maintenance.
In addition, NLP techniques can be used to process external data from research, industry reports, social networks, and mass media. It not only enhances the function of digital twins, not only can design future products but also can simulate its performance.
4. Generation design of intelligent manufacturing
The idea of generative design is the generation of all possible design options for a given product based on machine learning. By selecting parameters such as weight, size, material, operation, and manufacturing conditions in the generated design software, engineers can generate many design solutions. They can then select the most appropriate design for a future product and put it into production.
The use of advanced deep learning algorithms makes generation design software intelligent. One of the new trends in artificial intelligence is the generation of countermeasure networks (Gan). Gan in turn uses two networks. The Generator Network and the discriminator, where the generator network generates a new design for a given product, while the discriminator network classifies and differentiates the real product design and the generated product.
Therefore, data scientists develop and teach deep learning models to define all possible design variants. The computer becomes the so-called design partner, which generates unique design ideas according to the constraints given by the product designer.
5.ML based energy consumption forecasting
The growth of the Industrial Internet of things (IIoT) has not only automated most production processes but has also made them frugal. Energy consumption can be predicted by collecting historical data on temperature, humidity, lighting usage, and activity levels of the facility. Machine Learning and artificial intelligence were then responsible for most of the implementation.
The idea of using ML for energy consumption management is to detect patterns and trends. By processing historical data on past energy consumption, ML models can predict future energy consumption.
The most common machine learning method for predicting energy consumption is based on sequential data measurements. To do this, data scientists use autoregressive models and deep neural networks.
Autoregressive models are great for defining trends, periodicity, irregularity, and seasonality. However, applying only one method based on autoregression is not always sufficient. To improve the accuracy of their predictions, data scientists use several methods. The most common complementary approach is factor engineering, which helps transform raw data into elements that specify tasks for the prediction algorithm.
Deep neural networks are well suited for processing large data sets and finding patterns quickly. They can be trained to automatically extract features from the input data without the need for feature engineering.
To use internal memory to store information from previously entered data, data scientists use the recurrent neural network, which specializes in patterns that span longer sequences. RNN with circulation can read input data and transmit data across neurons at the same time. This helps to understand time dependencies, defines patterns in past observations, and links them to future predictions. In addition, RNNs can dynamically learn to define what input information is valuable and quickly change context if necessary.
Thus, using machine learning and artificial intelligence, manufacturers can estimate energy bills, understand how energy is consumed, and make optimization processes more data-driven.
6. Cognitive supply chain is driven by artificial intelligence and machine learning
When you look at how much data is growing with the Internet of things, it’s clear that intelligent supply chains are just a matter of choosing the right solution.
Artificial Intelligence and machine learning not only automate supply chain management but also enable cognitive management. Supply chain management systems based on machine learning algorithms can automatically analyze data such as material inventory, inbound shipments, work in process, market trends, consumer sentiment, and weather forecasts. As a result, they are able to define the best solution and make data-driven decisions.
The entire cognitive supply chain management system may involve the following functions
6.1. Demand forecasting
By applying time series analysis, functional engineering, and NLP techniques, ml prediction models can analyze customer behavior patterns and trends. Therefore, manufacturers can rely on data-driven forecasting to design new products, optimize logistics and manufacturing processes.
The Demand Forecasting System Adidas uses a good example of how machine learning algorithms can affect the customer experience. By analyzing trends in buying behavior and involving consumers in product design, the company has greatly optimized its manufacturing and delivery processes.
6.2. Transportation optimization
ML and deep learning algorithms can be used to evaluate shipping and deliverables and determine how their performance is affected.
6.3. Optimization of logistics routes
The generic ML algorithm examines all possible routes and defines the fastest one.
6.4. Warehouse control
The computer vision system based on deep learning can detect the shortage and overstock, and optimize the replenishment in time.
6.5. Human Resources Planning
When machine learning algorithms collect and process production data, they can show how many people are needed to perform certain tasks.
6.6. Supply chain security
ML algorithms analyze data about requests for information, who, where, and what information is needed, and evaluate risk factors. As a result, the cognitive supply chain ensures data privacy and protects against hackers.
6.7. End to end transparency
Advanced IoT data analysis based on machine learning deals with data received from IoT devices. ML algorithms can detect hidden interconnections between multiple processes in a supply chain and identify weaknesses that require an immediate response. Therefore, everyone involved in the operation of the supply chain can request the required information if necessary.
Finally, we can see a bright future for AI in manufacturing. Manufacturing AI technology is expected to grow rapidly over the next five years.
But it’s important to note that AI and machine learning are not instant success stories. Because the point is that any innovative technology should solve existing business problems, not imaginary ones.
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