Do you want to learn how to apply machine learning (ML) to manufacturing industry?
In this post, I’ll explain why ML is important to manufacturers and how they can benefit from using it. Then I’ll go over some specific examples of how companies are already applying ML to solve problems in the manufacturing industry.
I’ll also share with you how you can get started and become a machine learning expert yourself.
What is Machine Learning (ML)?
ML is an area of computer science that gives computers the ability to learn without explicitly programmed. It involves training machines to perform tasks based on past experiences. This technology is widely used in various industries such us healthcare, finance, retail etc. However, its adoption in manufacturing industry is still very limited.
ML is an emerging field of study where algorithms are trained to solve specific problems. These algorithms are capable of automatically discovering patterns within data sets. They can be used to predict future outcomes or identify unknown relationships between variables. The main advantage of ML is that it does not require programming and thus can be applied to any type of problem.
In recent years, artificial intelligence has been used in manufacturing industry to improve product quality and reduce production costs. ML is one of the most popular AI techniques used in manufacturing. This technique uses data mining to analyze information and make predictions about future events.
The Birth of Machine Learning in Manufacturing Industry
Machine learning has been used in manufacturing industry for years. In fact, it was first introduced in the 1950s. Since then, it has become an integral part of manufacturing processes. This technology allows machines to make decisions based on data. For example, if a machine detects that a product is defective, it could automatically notify the manufacturer. It is one of the most important technologies in manufacturing today.
A lot of manufacturers use ML to improve efficiency and reduce costs. Companies such as Nissan, Ashok Leyland, Tata, and Maruti Suzuki use ML to provide customers with personalized recommendations, identify supply chain issues, and identify external factors most likely to production.
ML can also be used to predict customer behavior. Manufacturers can use this information to optimize production lines and inventory management.
Yet, there are a few challenges in adopting ML methods in the manufacturing industry.
Challenges in manufacturing industry
Manufacturing industry faces many challenges due to the complexity of their processes and products. For example, the production line may consist of multiple steps, each step requiring different materials and equipment. Moreover, the product itself may contain several components that need to be assembled together. As a result, the manufacturing process becomes extremely complex.
The traditional approach to solving these problems is to develop a manual system that requires human intervention. Such systems are prone to errors and cannot adapt to changes quickly. Furthermore, they do not scale well as they become increasingly difficult to maintain.
To address these issues, some companies use software applications to automate certain parts of the manufacturing process. However, these applications are often expensive and require extensive customization.
Moreover, most of these applications are designed for single-purpose purposes. Thus, they lack flexibility and scalability.
Finally, these applications are usually developed by experts and thus are not accessible to non-technical users.
Machine Learning Models in Manufacturing
There are several different types of machine learning models, including supervised, unsupervised, and semi-supervised learning. Supervised learning involves training a computer program with labeled data. Unsupervised learning does not require labels, but instead looks at unlabeled data to find patterns. Semi-supervised learning use partially labelled data.
Designing a rocket launch vehicle or an aircraft wing is a complex task with a huge number of variables and data sources. Supervised learning method may be necessary in such applications of machine learning.
Supply Chain management is a lifeline for any manufacturing business. You can build smart supply chain processes to augment smart manufacturing operation using AI and Industrial Internet of Things (IIoT) through supervised learning method.
This method trains the algorithm without knowing the right answers. Instead, it looks at the structure of the data set and tries to find meaningful clusters. Once the algorithm is trained, it can detect new patterns in the data set.
Unsupervised machine learning methods are useful for finding patterns in data without having to label each example beforehand. In other words, these models learn from unlabeled examples rather than labeled ones. One example of an unsupervised model would be clustering algorithms. Clustering algorithms group similar items together into clusters based on similarity.
In manufacturing, clustering algorithms are used to classify products based on their features. For example, if you were making a car part, you could use clustering algorithms to determine whether two parts were similar enough to be grouped together. The same concept applies to customer service calls. If you had a large number of calls coming in asking questions about a particular topic, you could use clustring algorithms to group similar questions together.
Clustering algorithms work best when you have a large amount of data. However, they can still be effective when applied to smaller datasets. In fact, many companies use clustering algorithms to analyze small amounts of data to predict future events. For example, you might use clustering algorithms to predict which customers are likely to buy a certain product.
Semi-supervised learning works by training the system on a large amount of labeled data, then letting it analyze unlabeled data. The system learns from both sets of data, and eventually becomes able to recognize patterns and make predictions based on the unlabeled data alone.
In practice, this means that instead of having to manually label each piece of data, we can just give the system enough examples to learn from. Once the system has learned everything it needs to know, we can then use it to predict the labels of new pieces of data.
For example, imagine that we had a set of pictures of machine parts. We’d label these pictures as either “parts” or “not parts,” depending on whether or not they were actually parts. Then, we’d train our system to recognize parts by showing it lots of images of parts. After that, we’d be able to tell the system to look through a picture of a machine tool and determine whether or not it was a part.
The benefits of semi-supervised learning are numerous. First, it allows us to build systems that can learn from massive amounts of unlabeled data. Second, it lets us avoid labeling every single piece of data, saving us tons of work. Finally, it makes it possible to build systems that can understand concepts and relationships that we humans can’t.
Which Machine Learning Techniques work best for a Manufacturing company?Your choice of algorithm will depend on the available data and the manufacturing challenge you are trying to solve. A good study of comparison of machine learning is usually undertaken before deciding for the exact applications in manufacturing.
Many manufacturers find it good to use a mix of supervised, unsupervised, and semi-supervised methods.
How does machine learning work?
First, we need to prepare datasets.
Then, we feed the data into the algorithm.
After that, the algorithm predicts the labels of each sample.
Finally, we evaluate the accuracy of the prediction results.
The most important step is to select the right algorithm.
How to Choose the Right Machine Learning Algorithm?
We should consider many factors before choosing an algorithm to create a Smart Factory.
- The size of the dataset.
- How much computation power we have.
- Whether there are missing values.
- Whether the data is sparse or dense.
- Whether the data is continuous or discrete.
- Whether the data is categorical or numerical.
- Whether the data is static or dynamic.
- Whether the data is structured or unstructured.
- Whether the data is sequential or non-sequential.
- Whether the data is spatial or non-spatial.
- Whether the data is temporal or non-temporal.
- Whether the data is streaming or batch.
- Whether the data is real-time or offline.
- Whether the data is large or small.
- Whether the data is noisy or clean.
- Whether the data is stationary or non-stationary.
- Whether the data is linear or nonlinear.
- Whether the data is time series or not.
- Whether the data is periodic or not.
- Whether the data is redundant or not.
- Whether the data is independent or dependent.
- Whether the data is clustered or not.
- Whether the data is distributed or centralized.
- Whether the data is random or not.
- Whether the data is ordered or not.
- Whether the data is parallelizable or not.
- Whether the data is stochastic or deterministic.
- Whether the data is online or offline.
- Whether the data is asynchronous or synchronous.
- Whether the data is streamable or not.
- Whether the data is scalable or not.
- Whether the data is elastic or not.
- Whether the data is heterogeneous or homogeneous.
- Whether the data is encrypted or not.
- Whether the data is sensitive or not.
- Whether the data is confidential or not.
- Whether the data is private or public.
- Whether the data is secure or insecure.
- Whether the data is reliable or unreliable.
- Whether the data is consistent or inconsistent.
- Whether the data is transparent or opaque.
- Whether the data is local or remote.
In conclusion, ML algorithms are powerful tools that allow computers to learn from data and apply their knowledge to new situations. They’re used in everything from self-driving cars to medical diagnosis to online shopping recommendations. But choosing the right algorithm for your problem can be tricky. Choose wisely. Hire an expert. At Intuceo, we have helped over 100 companies build AI and ML algorithms to create smart manufacturing solutions for their smart factories. Contact our experts team to have a chat at no cost to you. We will be happy to guide you through a preliminary discussion to identify a AI and ML roadmap for you.
4 Benefits of Machine Learning in Manufacturing Industry
1. Improve Product Quality
Manufacturing companies often face challenges in maintaining quality standards. This is especially true for small and medium sized businesses.
But with the help of ML, manufacturers can improve their products without compromising quality.
For example, a manufacturer may use visual inspection AI’s deep learning to detect defects in its products. If the defect rate exceeds certain threshold, the manufacturer can then decide whether to recall the defective products or not.
This helps the manufacturer maintain its reputation and avoid bad publicity.
2. Reduce Production Costs
Manufacturers can also use ML to reduce production costs. For example, a company may use ML to predict demand for its products. Based on the predicted demand, the company can adjust its production capacity accordingly.
Boeing is using AI models in A320 final assembly line to detect production anomalies and attend to them before they become a show stopper.
By doing so, the company can save money on overproduction and underproduction.
3. Increase Customer Satisfaction
Manufacturers can also leverage ML to enhance customer satisfaction. For example, a manufacturer can use ML to identify customers who are most likely to buy its product.
Based on these insights, the manufacturer can personalize its marketing strategies to target those customers.
4. Enhance Business Operations
Finally, manufacturers can use machine learning to enhance business operations. For example, a factory can use ML to automate repetitive tasks.
The factory can then focus on higher value-added processes instead.
These are just some examples of how ML can benefit manufacturers.
Machine learning provides a powerful tool to solve many problems in manufacturing industry. It allows manufacturers to automate repetitive tasks, reduce costs, improve quality, and increase productivity.
However, there are still many challenges facing manufacturers who want to apply ML in their business. Some of these challenges include:
- Lack of expertise: Most ML applications are developed by expert developers. Intelligence manufacturing may require modelling through artificial neural networks. Therefore, they are not accessible to non technical users.
- High cost: Many ML applications are expensive and require extensive customization before deployment.
- Limited scalability: Most ML applications have been designed for single purpose. Thus, they lack scalability and flexibility.
- Data privacy: Many ML applications collect sensitive information from customers. Thus, they must comply with strict regulations regarding data protection.
Manufacturers are facing challenges such as increased competition, changing customer expectations, and rising costs.
They need to innovate and develop products faster than ever before. But they also need to do this in a cost effective manner.
To address these challenges, manufacturers need to adopt digital technologies.
However, there are several barriers to doing this. These include lack of knowledge, skills, and resources.
As well as issues related to the availability of technology and infrastructure.
But digital transformation is possible. And manufacturers can reap significant benefits.
If you want to develop a machine learning solution to build a smart factory for you, do contact us today!