Deep Learning in Manufacturing
What is Deep Learning in Manufacturing?
Deep learning, or DL, is one of the many types of artificial intelligence that are implemented in process manufacturing. It’s often used interchangeably with machine learning (ML), but it’s actually a subset of ML with small but significant differences.
Both DL and ML are a series of methods and models that enable machines to analyze enormous datasets, recognize patterns, and derive insights and predictions that business leaders can rely on. But DL algorithms involve neural networks with more layers than the average machine learning model, which is why these AI models are called “deep” learning.
DL models are typically more complicated to build and deploy and require more compute power, and are used for more complex and specific problems, unstructured data and/or extremely large datasets, which are more difficult to analyze. ML models are usually faster, lower-cost, and easier to build and use, which is why they are applied more widely in process manufacturing. But process manufacturing plants produce massive amounts of data and frequently encounter difficult analytical issues, making DL valuable for manufacturing companies.
Why Does Deep Learning Matter to Process Manufacturing Plants?
DL and ML are the foundation for many tools that are quickly becoming vital for process manufacturing, including predictive analytics, predictive monitoring, automation, and digital twins.
Deep learning algorithms can help process manufacturers to:
- Optimize supply chain to reduce the risks of delays in deliveries
- Automate manufacturing processes with robotic process automation
- Monitor the entire plant in real time to quickly spot and resolve bottlenecks and drops in production quality
- Lower carbon emissions and reduce waste to improve sustainability
- Predict customer demand more accurately so as to seize business opportunities and mitigate emerging risks
- Carry out reliability analysis to optimize plant operations
- Refine maintenance scheduling to extend equipment lifetime and improve overall equipment efficiency (OEE)
- Raise the bar for quality assurance, scanning product faster and more accurately
- Detect nascent issues within the plant so they can be investigated and corrected before escalating to affect product quality or quantity, or require unexpected downtime
How Can Process Plants Implement Deep Learning?
Determine your goals
There are many ways that deep learning tools can deliver value for process plants, which can lead to “analysis paralysis.” The first step is to decide on your business goals and consider the best ways to achieve each one. Deep learning platforms are powerful, but require a lot of resources, so it may be better to use a simpler ML tool if it can achieve the same or similar results.
Gather the necessary resources
Deep learning models are more resource-hungry than some other forms of AI, so you should make sure that you have everything you need before you embark on implementation. You might need to upgrade computing networks to ensure sufficient compute power, hire additional data scientists to manage more complex tools, and/or rethink your data processing pipelines.
Grow trust in DL insights
One of the main features of DL models is that they tend to produce “black box” insights which can’t be easily verified independently. This can lead people who aren’t used to working with AI and/or feel nervous about the potential of automation to dismiss predictions and alerts from DL systems. Before you bring in DL solutions, you need to educate your entire workforce about the basics of AI, to ensure they take DL insights seriously and act upon their recommendations.
How Do Process Plants Benefit from Deep Learning in Manufacturing?
Applying deep learning platforms can help process plants meet many business goals. By implementing deep learning, process manufacturing companies can increase their sustainability profile, improve productivity and product quality, and raise efficiency across the entire plant.