Category: Software
Organization: National Aluminium Company Limited (NALCO)
Abstract: This project aims to develop an AI-Based System. It will be able to estimate the physical characteristics of aluminum wire rods. It will control a number of production parameters to achieve better productivity as well as better quality control.
Key Components:
- AI Integration: AI as well as ML algorithms will be employed to analyze the casting and rolling parameters.
- Quality Prediction: Predict the physical characteristics like UTS, elongation, and conductivity of the wire rod.
- Process Optimization: Suggestion of the ways of improving the manufacturing process using the predictions.
Expected Outcome: The quality and uniformity of the aluminum wire rod shall be improved, which will increase the productivity and decrease the wastage in the manufacturing line.
Download Dataset for AI-Based Aluminium Wire Rod Prediction
However, since more real-world datasets for this particular application were not possible due to the existence of confidentiality agreements, we have instead worked on preparing a synthetic dataset that is very similar to the original data. This dataset provides critical features, such as casting temperature, rolling speed, cooling rate, etc., along with their corresponding relationships to important mechanical properties like UTS, elongation, and conductivity. In conclusion, the dataset is ultimately aimed towards machine learning models that seek to forecast and improve the process of making aluminum wire rods.
Table: Download link for the dataset:
| Dataset | Description | Download Link |
|---|---|---|
| Aluminium Wire Rod Properties | A synthetic dataset containing casting parameters (temperature, rolling speed, cooling rate etc) and physical parameters (UTS, elongation, conductivity etc) for the purpose of predictive models. | Download Here |
How to Use the Synthetic Dataset
The purpose of the dataset is to represent the core aspects. And it will have influence on the production of aluminium wire rods in terms of quality and quantity. This offers the predictive wire rod characteristics integration into the machine learning model and production optimization. We have added the dataset to help in the creation of AI models with the primary aim of:
The dataset we have provided is well-suited for helping create AI models with the primary aim of:
- Predicting Quality Attributes:This dataset can be used to train any models that are capable of predicting UTS, elongation, conductivity etc with respect to production parameters.Enhancing the Manufacturing Process:Using the predictions, the manufacturers are able to change certain process variables like casting temperature and rolling speed for modifying the quality of the wire rods.
Note:
Such a synthetic dataset is not too far from any real data. It is important not to confuse it with the actual production data. It has its own perspective in terms of machine learning application. For datasets from real-world scenarios, the suggestion is to getting in touch with research groups working in this area.
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