Machine Learning and Manufacturing: Advantages and Disadvantages of Open-Source Datasets
Machine learning (ML) can be used in manufacturing in a variety of ways to optimize production processes and improve product quality. Common applications include:
- Predictive maintenance: ML models can be trained to analyze sensor data from equipment to predict fault timelines and when maintenance will be needed. This can help manufacturers to schedule maintenance at the optimal time, reducing downtime and improving equipment efficiency.
- Quality control: ML models can be used to analyze data from multiple quality control inspections to identify patterns and trends that indicate potential issues with product quality. This can help manufacturers to identify and correct quality issues before they impact customers.
- Supply chain optimization: By analyzing data from the supply chain to identify bottlenecks and inefficiencies, ML models can help manufacturers optimize their supply chain operations, reduce costs, and improve delivery times.
- Product design: Analyzing customer data and feedback with ML models to identify trends and preferences can assist manufacturers in designing products that better meet the needs and wants of customers.
As ML gains traction in the manufacturing industry, there is an increasing need for high-quality datasets to train and evaluate them. Open-source datasets, which are freely available for anyone to use, can be valuable in this context as they provide access to large amounts of data without the need for costly licenses or permissions.
Here are some notable open-source datasets in the manufacturing machine learning space:
- The CMAPSS dataset: Developed by the NASA Ames Prognostics Data Repository, this dataset includes data from turbofan engine simulations and can be used to train ML models to predict when maintenance will be needed.
- The PdM dataset: This dataset, developed by the Prognostics and Health Management (PHM) Society, includes wave signals data from piezo sensors (as well as loading conditions) collected from a number of aluminum lap joint specimens. Ground truth data (actual crack length) is also available for the test and validation data sets.
- The Glass identification dataset: Includes data on the chemical composition of different types of glass and can be used to train a classifier to predict types of glass based on composition.
Overall, these datasets provide a wealth of information and data that can be used to train and evaluate a variety of ML models for critical applications in manufacturing. However, we must also remember that open-source datasets can vary in quality—many open datasets, especially those maintained by scientific institutions, can be clean, well-structured, and well-maintained, making model training easy and straightforward. This is not always the case, however, as other open-source datasets that are less well maintained can contain errors and biases that can negatively impact the performance of the ML models trained on them. Furthermore, some open-source datasets have licensing or terms of use restrictions, making them difficult or impossible to use in commercial endeavors. Certain open datasets might also have a limited scope that does not cover a specific domain of interest. This makes training ML models that are related but not wholly applicable to a given dataset a dangerous proposition.
In cases such as those mentioned above, it may be helpful—if not critical to safety, efficiency, legal requirement, and customer need—to use a proprietary or custom dataset specifically tailored to your manufacturing needs. As veterans of the ML/AI data trade, Defined.ai specializes in not just providing businesses the off-the-shelf data their ML models need for model training, but we’re also invested in bespoke data collection to ensure that our clients have exactly the data they need—no more or no less.
If you have a manufacturing machine learning project that can use high-quality data unique and specific to your needs, get in touch today to see how Defined.ai can help.
