Mechanical Engineering Training Program in the Manufacturing Industry

Example: © IoT For All

Manufacturers need to learn one of the most important elements of driving a business into the future in order to keep up with the latest technology changes: machine learning. Let’s talk about the most important applications and innovations that ML technology will offer in 2022.

Machine Learning and AI: What’s the Difference?

Mechanical engineering is a subset of artificial intelligence, but not all AI technologies are considered as mechanical engineering. There are different types of AI that play a role in many fields, such as robotics, natural language processing, and computer vision. If you are curious about how these technologies are impacting the manufacturing industry, check out our review below.

Basically, machine learning algorithms use learning information to reinforce an algorithm that allows the software to solve a problem. This data can be obtained from real-time IoT sensors on the factory floor or can be obtained from other methods. Mechanical engineering has a variety of methods such as neural networks and in-depth learning. Neural networks mimic biological neurons to detect patterns in a set of data to solve a problem. In-depth study uses different layers of neural networks, in which the first layer uses raw data input and transfers the processed information from one layer to another.

Factory in the box

Let’s start by imagining a box with mounting robots, IoT sensors and other automation techniques. On the one hand you provide the necessary materials to complete the product; on the other hand the product leaves the conveyor. The only intervention required for this device is regular maintenance of the equipment inside. This is the best future of manufacturing and machine learning can help us understand the full picture of how to achieve this.

In addition to the advanced robotics required for automatic installation work, machine learning can help ensure quality: quality assurance, NDT analysis and localization of the causes of defects, and so on.

You can think of this enterprise in the example of a box as a way to simplify a larger enterprise, but in some cases it is completely original. Nokia uses mobile manufacturing sites in the form of shipping containers equipped with advanced automation equipment. You can use these portable containers at any location you need, allowing manufacturers to collect the product on site, instead of shipping the product over long distances.

Quality assurance

Using neural networks, high-quality optical cameras, and powerful GPUs, real-time video processing combined with machine learning and computer vision can perform visual test tasks better than humans. This technology ensures that the factory works properly in the boxes and the unusable products are removed from the system.

In the past, the use of machine learning in video analysis has been criticized for the quality of the video used. This is because the images may be blurry from frame to frame and the test algorithm may be subject to more errors. With high-quality cameras and greater graphics processing power, neural networks can search for defects more efficiently in real time without human intervention.

Using a variety of IoT sensors, machine learning can help test built-in products without damage. The algorithm can search for patterns in real-time data associated with the defective version of the unit and allows the system to record potentially unwanted products.

Non-destructive testing

Another way we can detect flaws in the material is by indestructible testing. This includes measuring the stability and integrity of the material without causing damage. For example, you can use an ultrasound machine to detect anomalies such as cracks in material. The machine can measure data that people can analyze to manually search for these indicators.

However, external detection algorithms, object detection algorithms, and segmentation algorithms can automate this process by analyzing data for recognizable patterns that people cannot see with greater efficiency. Mechanical engineering is also not subject to the same number of errors that people are prone to.

Predictable maintenance

One of the main tenants of the role of machine learning in production is predictive maintenance. PwC reported that forecast maintenance will be one of the largest machine learning technologies in production, with market value expected to increase by 38% from 2020 to 2025.

With unscheduled maintenance, which has the deep potential to the bottom line of business, predictive maintenance can allow businesses to make appropriate adjustments and corrections before machines run into costly failures. We want to make sure our plant has a box in the box with the least time lag, and predictable maintenance can do that.

Extensive IoT sensors that record important information about the operating conditions and condition of the machine make predictive maintenance possible. This can include humidity, temperature, and so on.

ML models are used for predictive maintenance

A machine learning algorithm can analyze samples of data collected over time and reasonably predict when a machine needs maintenance. There are several ways to achieve this goal:

  • Regression models: they predict the remaining useful life of the equipment (RUL). It uses historical and static data, and manufacturers can see how many days are left before the car breaks down.
  • Classification models: these models predict failure over a predetermined time period.
  • Anomaly detection models: These are flag devices when detecting system abnormal behavior.

Problem localization

Thanks to IoT sensors that provide predictive maintenance, machine learning can analyze data samples to see which parts of the machine need to be stored to prevent failure. If certain patterns lead to a tendency to deficiencies, it is possible that hardware or software behavior can be identified as the cause of these deficiencies. From here, engineers can find ways to fix the system to prevent these flaws in the future. This allows us to reduce the margin of error of our enterprise in a box scenario.

Digital twins

Digital twins are a virtual entertainment of the production process based on data from IoT sensors and real-time data. They can be seen as an original hypothetical depiction of a system that does not yet exist, or they can be a reconstruction of an existing system.

Digital twins are a test box in which machine learning can be used to analyze samples in simulation to optimize the environment. It also helps to support quality assurance and maintenance prediction efforts. We can also use machine learning alongside digital twins to optimize design. This works when planning an enterprise design or to optimize an existing design.

ML models for predicting energy consumption

If we want to optimize every part of the plant, we must also pay attention to the energy it requires. The most common way to do this is to use sequential measurements of data that can be analyzed by data scientists using machine learning algorithms used by autoregressive models and deep neural networks.

  • Autoregressive models: Great for identifying trends, periodic, irregular and seasonal power consumption. To improve accuracy, data scientists can turn raw data into features that can help define the task for prediction algorithms.
  • Deep neural networks: Data scientists use these to process large sets of data to quickly find data consumption patterns. These can be learned by automatically extracting features from input data without feature engineering such as autoregressive models.
  • Neural networks for sequential information: RNN (Repeat Neural Networks), LSTM (Long Short Term Memory) / GRU (Repeat Gate Unit), Focused Neural Networks to store previously entered information about energy usage using internal memory.

Generative design

We used machine learning to optimize factory production processes, but what about the product itself? BMW introduced the BMW iX Flow at CES 2022 with a special black electronic cover that can allow the car’s color (or more precisely, the shade) to change between black and white. BMW explained that “the manufacturing design processes to provide the segments reflect the specific contours of the car and the resulting changes in light and shadow.”

Manufacturing design is where machine learning is used to optimize the design of a product, whether it be a machine, electronic equipment, toy, or other object. With the information and the desired goal, machine learning can turn out all the possible steps to find the best design.

ML algorithms can be studied for design optimization for weight, shape, duration, cost, strength, and even aesthetic parameters.

The production design process can be based on the following algorithms:

  • Reinforcement training
  • In-depth study
  • Genetic algorithms

Improving supply chain management: knowledge supply chains

Let’s move away from the factory a bit in the box example and look at a broader picture of production needs. Production is just one element. The roles of the supply chain from the production center will also be enhanced with machine learning technologies, such as logistics route optimization and warehouse inventory control. They form a supply chain of knowledge that continues to evolve in the manufacturing industry.

Warehouse inventory control

Artificial logistics solutions use object identification models instead of barcode identification, thus replacing manual scanning. Computer vision systems can detect flaws and excessive resources. By identifying these patterns, managers can become more aware of what is happening. Computers can even be assigned to act automatically to optimize inventory storage.

In MobiDev, we explored an example of the use of creating a system that is able to identify objects for logistics. Learn more about object detection using small sets to automate inventory counting in logistics.

Demand forecasting

How many products should the factory produce and export? This is a question that can be difficult to answer. However, with access to relevant data, machine learning algorithms can help businesses understand how much work they need to do without overproduction. The future of machine learning in manufacturing depends on innovative solutions.

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