Using AI To Increase Asset Utilization And Production Uptime For Manufacturers

Today, manufacturers are advancing on their factory digitalization journey, betting on innovative technologies to strengthen competitiveness, deliver sustainable growth, and offer new services. Macroeconomic factors – such as high energy costs, increasing labor, and raw material shortages – drive the need for urgent operational optimizations and automation.Cloud capabilities have matured at an accelerated pace, giving manufacturers practical avenues to achieve these goals. Manufacturers are finding new ways to bring AI and machine learning (ML) to practical use cases, like predictive maintenance, anomaly detection, and asset utilization management. However, manufacturers struggle to adopt AI at scale due to challenges around data accessibility, infrastructure, and technology.

Google Cloud created purpose-built tools and solutions to organize manufacturing data, make it accessible and useful, and help manufacturers to quickly take significant steps on this journey by reducing the time to value. In this post, we will explore a practical example of how manufacturers can use Google Cloud manufacturing solutions to train, deploy and extract value from ML-enabled capabilities to predict asset utilization and maintenance needs.

The journey to machine learning insights starts with accessible data

The first step to a successful machine learning project is to unify necessary data in a common repository. For this, we will use Manufacturing Connect, the factory edge platform co-developed with Litmus Automation, to connect to manufacturing assets and stream the asset telemetries to Pub/Sub.

After the telemetry messages are published to Pub/Sub, Dataflow will identify each message based on its structure and apply corresponding normalizations and transformations, which are preconfigured in Manufacturing Data Engine. Once the messages are processed, the messages will be routed to Cloud Storage, BigQuery, and/or Cloud BigTable based on user configuration.

Figure 1. High level architecture diagram of machine learning with Manufacturing Data EngineTo train a machine learning model, manufacturers can use Vertex AI AutoML to build a no-code model based on the training data stored in the Manufacturing Data Engine.Then, users can trigger a batch prediction job in Vertex AI or export the AutoML model to run on the edge component of Manufacturing Connect for real-time prediction. Regardless of the model deployment methods, the prediction and explanation results will be ingested into Manufacturing Data Engine, which can be analyzed and visualized in Looker.

A blueprint for classifying asset condition

The following scenario is based on a hypothetical company, Cymbal Materials. This company is a factitious discrete manufacturing company that runs 50+ factories in 10+ countries. 90% of Cymbal Materials manufacturing processes involve milling, which are accomplished using industrial computer numerical control (CNC) milling machines. Although their factories implement routine maintenance checklists, there are unplanned and unknown failures that happen occasionally. However, many of the Cymbal Materials factory workers lack the experience to identify and troubleshoot failures due to labor shortage and high turnover rate in their factories. Hence, Cymbal Materials is working with Google Cloud to build a machine learning model that can identify and analyze failures on top of Manufacturing Connect, Manufacturing Data Engine, and Vertex AI.

For the pilot, Cymbal Materials forms a team of manufacturing engineers and data scientists to evaluate the feasibility of solving the tool wear detection problem. To avoid compliance concern, the Cymbal Materials team chooses to start with a public tool wear detection dataset hosted on Kaggle. This dataset is collected from running machining experiments on 2″ x 2″ x 1.5″ wax blocks in a CNC milling machine. The dataset contains measurements from the 4 motors (X,Y, Z axes and spindle) and program values in the CNC machine, which maps well to the data that Cymbal Materials collects for their CNC milling machines.

Figure 2. Architecture diagram of machine learning with Manufacturing Data Engine.To start, the Cymbal Materials data scientists download the tool wear detection dataset from Kaggle and upload the dataset to Cloud Storage. Then, the data scientists use Vertex AI to:

Figure 3. Vertex AI AutoML model performanceAfter seeing great performance of the AutoML tabular model, the Cymbal Materials data scientists decide to use the AutoML model to predict on the actual CNC milling machine telemetries from their factories and validate the generalizability of the AutoML model. They ask the manufacturing engineers to deploy Manufacturing Connect in a Cymbal Materials factory and stream telemetries for one CNC milling machine to the Manufacturing Data Engine.Manufacturing Connect includes an edge component that can gather data from manufacturing assets via an extensive library of 250+ communication protocols. The edge component of Manufacturing Connect comes with built-in Node-RED and Docker runtime, which support running custom workflows and machine learning models at the edge.Using pre-defined hierarchies, Manufacturing Connect pushes asset telemetries and states to Pub/Sub.

Figure 4. Manufacturing Connect user interfaceAfter the factory operational data are ingested into Pub/Sub, Cymbal Materials uses Manufacturing Data Engine to:

  • Normalize, transform, and contextualize real-time operational data with slowly changing metadata
  • Batch ingest historical operational data and prediction results
  • Route data dynamically to Cloud Storage, BigQuery, and/or Cloud BigTable

Figure 5. Manufacturing Data Engine configuration in Manufacturing Connect.Using the trained AutoML tabular model and real-time telemetries from CNC milling machines, the data scientists trigger a batch prediction job on the CNC milling machine telemetries in BigQuery. The data scientists configure the batch prediction to output prediction results in Cloud Storage such that Manufacturing Data Engine can batch ingest the prediction results after the batch prediction job completes.To consume the prediction results, the Cymbal Materials manufacturing engineers use Looker to create visualizations. The dashboard allows the manufacturing engineers to:

  • Visualize the CNC milling machine actual and predicted tool conditions over time
  • Explain the prediction results by summarizing the top attributing features
  • Create alerts based on the predicted tool condition for their assets
  • Take actions by contacting the supplier and/or scheduling maintenance for their assets

Figure 6. CNC mill wear prediction displayed in a Looker dashboard.

From edge to cloud, improving production efficiency for manufacturers

To support the entire factory digitalization value journey, manufacturers are looking for capabilities from simple visualizations to predictive ML models. Robust solutions, such as the one covered here, provide rapid paths for engineers to extract insight from their factory data.

Having a common data repository for manufacturing data, industry-leading machine learning platform, and versatile dashboard components accelerate manufacturer’s digital transformation.

This solution brings the best of Google Cloud’s data analytics and artificial intelligence capabilities in an industrial environment. Manufacturing Connect creates the link between industrial machinery and Manufacturing Data Engine, the cloud platform where the manufacturing data are processed, normalized, contextualized, and stored in a ready-to-consume format. Vertex AI can build, deploy, and scale machine learning models using data stored in the Manufacturing Data Engine. Vertex AI includes AutoML and Workbench for training models without code and training custom models with code-first experience respectively.

Learn more about how Google Cloud is transforming manufacturing to meet changing customer expectations at our Google Cloud Next Manufacturing playlist.

What’s next

  1. Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers
  2. Manufacturing Data Engine | Solutions | Google Cloud
  3. GitHub – GoogleCloudPlatform/mfg-ml-examples

By: Ka Wo Fong (Industry Solutions Architect, Cloud Manufacturing Solutions, Google Cloud) and Charlie Sheridan (Global Technical Director, Manufacturing, Industrial & Transportation, Google Cloud)
Source: Google Cloud Blog

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