How Artificial Intelligence and Machine Learning is Changing Manufacturing: Insights from Industry Experts

Manufacturing is evolving. With the advent of artificial intelligence (AI) and machine learning (ML), manufacturers can now harness the power of data to create efficiencies and solve business problems that were once impossible to tackle. Industry experts are bullish on the role of AI and ML in manufacturing, with many predicting significant improvements in terms of efficiency, quality, and even new business opportunities. In this blog post, we will take a look at some of the ways that AI and ML are changing manufacturing today.

What Is Production Optimization?

Source:?Gordon Chen ?Medium

Production optimization is a machine learning method that helps to identify inefficiencies in manufacturing processes and suggest ways to improve them.

In many cases, production optimization can be used to automate tasks that are currently done by human workers, such as quality control or assembly. By doing so, production optimization can help to improve the efficiency of manufacturing processes and reduce the overall cost of production. In some cases, production optimization can also be used to customize products for individual customers, which can further improve efficiency and reduce waste.

The goal of production optimization is to improve quality and productivity while reducing costs.?To achieve this, production optimization typically involves analyzing data from multiple sources, such as machine sensors, machine logs, and production records.?By identifying patterns in this data, production optimization can help to improve machine utilization, reduce downtime, and streamline the manufacturing process. In addition, by automating the optimization process, production optimization can help to reduce the need for manual intervention and improve the responsiveness of the manufacturing process to changes in demand.

What Is Predictive Analysis?

The use of advanced analytics to make predictions about future events that will impact manufacturing yield rates is known as Predictive Analytics.?It applies methods including data mining, statistics, data modeling, and machine learning to pore over historical data for patterns that can be used to forecast future events.

Predictive analytics has been used in manufacturing for some time now, but the adoption of machine learning algorithms is giving the technology a boost. Machine learning can automatically identify patterns in data sets that are too large or complex for humans to discern. This capability is particularly valuable in the manufacturing sector, where there is often a large amount of data to sift through.

One company that is using machine learning for predictive analytics in manufacturing is Bosch.?The company has developed an algorithm that can predict failures in industrial machines with up to 90% accuracy.?By detecting potential failures before they happen, the algorithm helps Bosch avoid downtime and save money.

What are some sources of data collected for Predictive Analytics?

1. Equipment Performance: Sensors monitor the equipment’s performance and send data to help forecast production quality and maintenance needs.
2. Machine Utilization: When a machine is used, it’s vital to determine whether it has been overused between scheduled services. Data can also assist in predicting if conditions such as power outages are playing a role.
3. Raw Material Properties: The methods used to create a product and the resulting quality might be influenced by the material properties. Material data may assist predict possible failures owing to changes in material qualities.
4. Tolerance Engineering: Manufacturing tolerances have a major influence on product yields. Data can be utilized to provide feedback to engineers, allowing them to adjust tolerances for more manufacturability.
5. Environmental Conditions: The quality of the goods can be impacted by temperature and humidity, depending on their sensitivity to these fluctuations. Weather reports from previous years might assist predict the product’s performance.
6. Purchase Order Scheduling:?When one operation is bottle-necked, it has the potential to cascade into other processes down the line. Information on when a product is delivered may help you plan future purchases.
7. Resource Utilization:?The overall cost of manufacturing a product is crucial for assessing the potential of a process. The amount of time and effort it takes to create a product is critical to assess whether the process is profitable.

How Is It Used?

Source:?https://www.emersonautomationexperts.com/2019/industry/life-sciences-medical/taming-big-data-life-sciences/

Production optimization in machine learning can be a highly complex process, involving countless data sets and algorithms. To begin, a machine learning system must first be trained on a large body of data, utilizing various prediction models and analytical techniques to sift through the information and identify patterns and trends. Once the system has been adequately trained, it must then go through a testing stage in order to determine its accuracy and overall performance. Purging false positives, tweaking algorithms, and fine-tuning inputs are just some of the many tasks that may need to be performed during this process.

Some examples of machine learning algorithms include decision trees, support vector machines, and artificial neural networks.

• Decision Trees —?A decision tree is a machine learning algorithm that can be used for both classification and regression tasks. In general, a decision tree is composed of a series of nodes, each of which represents a test on an attribute value.
• Support Vector Machines?— A support vector machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression tasks. SVMs are based on the concept of finding a hyperplane that maximally separates two classes of data points.
• Artificial Neural Networks?— An artificial neural network (ANN) is a machine learning algorithm that is used for both classification and regression tasks. ANNs are composed of a series of interconnected nodes, or neurons, that process information in a similar way to the human brain.

These algorithms are used in a variety of applications including image recognition, spam detection, and fraud detection. As machine learning algorithms become more sophisticated, they will continue to revolutionize the way we process and understanding data.

Manufacturing companies are under pressure to improve quality and productivity while reducing costs. As a result, many are turning to machine learning for help. Machine learning can be used in a variety of ways to improve manufacturing processes, including production optimization, quality control, and assembly. By harnessing the power of machine learning, manufacturers can improve quality and productivity while reducing costs.

In the end, successful production optimization requires tremendous skill, expertise, and attention to detail on the part of both the machine learning engineer and the end-users who rely on these systems for critical decision-making purposes.

How Does An Optimization Algorithm Work?

A machine learning algorithm that can predict the production rate based on the controls you change is a very useful tool. With its peaks and valleys representing high and low production, the machine learning-based prediction model gives us a “production-rate landscape” with its peaks and valleys indicating high and low production.

The algorithm can provide advice on how to achieve this peak, for example, which control variables to change and how much to change them, based on moving through the “production rate landscape.” Such a machine learning-based production optimization thus consists of three main components:

1. Prediction algorithm?— The first, and most essential step, is to ensure that you have a machine-learning algorithm that can correctly predict the appropriate production rates given all operator-controllable variables.
2. Multi-dimensional optimization —?You may apply the prediction algorithm as the starting point for an optimization routine that adjusts which control variables to improve in order to maximize output.
3. Actionable output?— You get recommendations for which control variables to adjust and the potential production rate increase from these modifications as a result of the optimization algorithm.

Source:?https://mech.iitm.ac.in/nspch52.pdf

What Are the Benefits of Production Optimization?

There are many benefits of using machine learning in manufacturing. Machine learning can help reduce waste, increase efficiency, and improve product quality. Additionally, machine learning can help you develop new products and find new business opportunities. What’s more, machine learning can be used to automate mundane tasks, such as resource management and cybersecurity. In the future, machine learning will become increasingly important in manufacturing as it will help factories optimize their production processes.

Here are some potential benefits of production optimization:

• ·?Improved quality:?By identifying and addressing inefficiencies in the manufacturing process, production optimization can help to improve the overall quality of products.
• ·?Reduced costs:?By automating tasks and streamlining the manufacturing process, production optimization can help to reduce the overall cost of production.
• ·?Increased efficiency:?By improving machine utilization and reducing downtime, production optimization can help to increase the overall efficiency of the manufacturing process.
• ·?Improved responsiveness: By automating the optimization process, production optimization can help to improve the responsiveness of the manufacturing process to changes in demand.

Common Issues With Production Optimization

There are several key issues that need to be addressed when trying to optimize machine learning models for production.

• Overfitting?— which can occur when a model is trained on too few data points or using too complex of a model. This can lead to the model not performing well on unseen data, which is often the case in production environments.
• Training-serving skew?— which happens when the distribution of training data and production data differ. This can lead to differences in accuracy and performance between the training environment and production environment.
• Poor feature engineering?— can also be an issue, as it can lead to suboptimal performance due to features that are not informative or predictive. incorrect feature scaling can also
• Consistency —?ensuring that the model works consistently across different data sets, which requires careful tuning of parameters and hyperparameters.
• Scalability —?determining how to scale up prediction computation while minimizing loss per prediction, an area that is still relatively poorly understood.
• Capacity —?it can be difficult to ensure that the model has enough capacity considering fluctuations in the size and type of data, especially when dealing with large datasets and streaming data.
• Hyperparameter tuning?— if the model is too finely tuned to the training data, it may overfit and again not perform well on new data.
• Concept drift?— where the distribution of the data changes over time and causes previously inaccurate models to become less accurate or even completely break.

While there are many potential benefits to production optimization, there are also a number of key issues that need to be addressed. In order to overcome these issues, manufacturers need to have a deep understanding of machine learning and the manufacturing process. They also need to have access to quality data sets and the resources necessary to tuning models for their specific needs.

Use Cases

Source:?https://www.sciencedirect.com/science/article/abs/pii/S0278612518300037?via%3Dihub

1.?Predictive Maintenance

By predicting equipment failures before they occur, scheduling timely maintenance, and reducing unnecessary downtime, machine learning enables predictive maintenance. Manufacturers devote far too much time and money to repair breakdowns rather than devoting resources to planned maintenance. Machine learning algorithms can identify equipment failure with a degree of accuracy of 92%, allowing businesses to better plan their maintenance schedules while also enhancing asset durability and product quality. According to a recent Accenture survey, predictive analytics and machine learning have resulted in an overall equipment efficiency improvement from 65% to 85%, with improvements in everything from asset utilization to problem resolution.

2.?Quality Control

Machine learning methods are being used in a wide range of application areas, including product inspection and quality control. ML-based computer vision algorithms can learn from historical data to separate good products from defective ones, automating the inspection and supervision process. TAs a result, the programs are more adaptable and function at lower defect levels. Negative cases should be avoided; they only cause trouble. Examine your processes and convert them to digital documents: you’ll have far greater control than before over changes in procedure or automation procedures. In manufacturing, machine learning can save significant amounts of time in visual quality control. According to Forbes, machine learning-based automated quality testing may improve detection rates by up to 90%.

3.?Logistics and Inventory Management

The manufacturing sector needs a high degree of logistics know-how to execute the full production process. Machine learning-based technology may automate several logistics operations, resulting in greater efficiency and reduced expenses. It’s been estimated that the typical US company loses $171,340 each year due to manual, time-consuming activities such as delivery and manufacturing paperwork handling. These mundane operations may be automated using machine learning to save thousands of hours every year. Artificial intelligence algorithms may also be used to automate resource management — Google, for example, was able to cut its data center cooling cost by 40% while using DeepMind AI.

4.?Product Development

Machine learning is frequently used in product development. Both the creation of new items and the improvement of existing ones necessitate substantial data analysis in order to achieve the greatest results. Machine learning solutions may assist in collecting and analyzing a lot of product data to better understand consumer demand, find flaws that had previously gone unnoticed, and propose new business opportunities.

This can assist in the improvement of existing product designs as well as the development of new products that may generate new revenue streams for your company. Companies may reduce the dangers involved in the creation of innovative items by getting more informed about their development.

5.?Cybersecurity

Network, data, and technological platforms — both on-premises and in the cloud — are essential components of machine learning solutions. As machine learning matures and grows in popularity, it is being used to protect systems, data, and information. Regulating access to important digital platforms and knowledge is critical, which machine learning may assist with. Machine learning may help automate how specific users gain access to sensitive data, which programs they use, and how they connect to it.

This can help organizations safeguard their digital assets by detecting and reacting to anomalies rapidly.

6.?Robotics

Although manufacturing is still heavily reliant on human labor, it is changing. Automation in manufacturing, on the other hand, is increasing as robots are now able to do a number of complex activities, with the exception of a few high-precision tasks that can be done only by human experts. Robots that are flexible enough to collaborate with people in the future might take over a significant portion of production. They’ll be able to work in changing and dynamic environments with little human involvement. Robotics offers significant potential for advanced machine learning techniques, allowing businesses to create sophisticated plans and production processes.

What Is the Future of Production Optimization?

The future of production optimization is machine learning. Machine learning methods can be used to automatically optimize production processes.

For example, a machine learning algorithm could be used to control the speed of a conveyor belt so that it churns out products at the optimal rate. By using machine learning, manufacturers can reduce waste and increase efficiency. In the future, more and more factories will use machine learning to streamline their production processes.

Conclusion

As the benefits of production optimization become more widely known, it is likely that the technology will be adopted by a wider range of manufacturers. This could potentially lead to a virtuous circle, in which the use of production optimization leads to further improvements in quality and efficiency, which in turn leads to more widespread adoption. Ultimately, this could help to transform the manufacturing industry, making it more efficient and responsive to the needs of customers.

Thanks for reading!

Follow me on my channels for more content!

Disclaimer

I cannot express strongly enough that you should use caution with the information supplied. I am not a professional financial adviser nor a fortune teller who can predict your circumstances or future. The data provided is meant to help individuals learn and adapt it for their own use, however it won’t always apply in every use case. I can confirm that these strategies have been extremely profitable in my personal life, contributing to my professional success.