Abstract
Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.
Highlights
Workpiece quality prediction is very important in manufacturing industry since defects have negative impacts on the products quality and could reduce the sales volume and even cause irreparable losses to the enterprises.[1]
Though most machine learning (ML) methods have been successfully applied in manufacturing industry, their performance heavily relies on the hyperparameters.[3]
The results show that adaptive Tree Pazen Estimator (ATPE) can provide the state-of-the-art performance for Hyperparameter optimization (HPO) in high dimension by comparing with Random search (RS), annealing, and Tree Pazen Estimator (TPE)
Summary
Workpiece quality prediction is very important in manufacturing industry since defects have negative impacts on the products quality and could reduce the sales volume and even cause irreparable losses to the enterprises.[1]. Though most ML methods have been successfully applied in manufacturing industry, their performance heavily relies on the hyperparameters.[3] Since default hyperparameters cannot guarantee the performance of ML models,[4] tuning the hyperparameters becomes the essential process for ML methods. Various tuning approaches, such as trial and error, manual search, are developed to obtain the best configuration of hyperparameters. The tuning process is very time-consumption and labor-intensive, and the results of tuning process is easy to converge to the suboptimal hyperparameters configuration
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