Simone Massulini Acosta's research while affiliated with Federal University of Technology - Paraná/Brazil (UTFPR) and other places

Purpose Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing. Design/methodology/approach A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation. Findings The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models. Originality/value This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

The existence of contaminants in metal alloys products is the main problem affecting the product quality, which is an important requirement for competitiveness in industries. This paper proposes the application of a relevance vector machine for regression (RVR) and a support vector machine for regression (SVR) optimized by a self-adaptive differential evolution algorithm for regression to model the phosphorus concentration levels in a steelmaking process based on actual data. In general, the appropriate choice of learning hyperparameters is a crucial step in obtaining a well-tuned RVM and SVM. To address this issue, we apply a self-adaptive differential evolution algorithm, which is an evolutionary algorithm for global optimization. We compare the performance of the RVR and SVR models with the ridge regression, multiple linear regression, model trees, artificial neural network, and random vector functional link neural network models. RVR and SVR models have smaller RMSE values and better performance than the other models. Our study indicates that the RVR and SVR models are adequate tools for predicting the phosphorus concentration levels in the steelmaking process.

In the past decade, relevance vector machines have gained the attention of many researchers, and this machine learning technique is a Bayesian sparse kernel method, both for classification and regression problems. In general, the choice of appropriate learning hyperparameters is a crucial step in obtaining a well-tuned model. To overcome this issue, we apply a self-adaptive differential evolution algorithm. In this paper, we propose a relevance vector machine for regression combined with a novel self-adaptive differential evolution approach for predictive modelling of phosphorus concentration levels in a steelmaking process with real data. We compared the performance of proposed relevance vector machine (RVM) with other machine learning techniques, such as random forest (RF), artificial neural network (ANN), K-nearest neighbors (K-NN), and also with statistical learning techniques as, Beta regression model and multiple linear regression model. The RVM has performance better than RF, ANN, K-NN, and statistical techniques used. Our study indicates that RVM models are an adequate tool for the prediction of the phosphorus concentration levels in the steelmaking process.

Livro da Editora Conhecimento Livre composto por diversos artigos científicos sobre aplicações relacionadas à Engenharia da Produção.