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Designing a product service system begins with understanding customer voices. Compared to the traditional methods such as survey, interview, customer review represents a particular kind of big data that contain rich information that is useful for the design of product service system. This paper presents a new framework that integrates a variety of artificial intelligence and machine learning techniques. All proposed operations of the framework can be realized based on the Google Cloud Platform. A case study is conducted to showcase the practical applicability of some key operations such as opinion mining. More than 40,000 customer reviews about the product Kindle White E-reader were analyzed. [ABSTRACT FROM AUTHOR]

Over the past few years, a few experimental failure data of composites have been collected. It would be of interest to leverage the existing data to improve the prediction of failure criteria. In this paper, we developed a framework that combines sparse regression with compressed sensing to discover failure criteria of composites from experimental data, which leveraging advances in sparsity techniques and machine learning. This framework does not need Bigdata to train the model and is remarkably robust to the noised data, which satisfies the constraints of the current failure data. To test the performance of the proposed method, we collected the experimental data from the first Worldwide Failure Exercise (WWFE I) and fed it to the proposed method. To satisfy the engineering design needs, we proposed an optimization approach to enforce a constraint to the discovered failure criterion to yield a conservative model. Three examples were presented to demonstrate the proposed framework. The result shows that the proposed framework can identify the most important features and the discovered failure criterion match the experiment result well. Besides, with the enforced constraint, the proposed method can yield a conservative failure criterion. [ABSTRACT FROM AUTHOR]

Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. The most popular machine learning model in recent years is artificial neural networks (ANN). Although many ANN models are used in the constitutive modeling of composite materials, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posing new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling. This review focuses on the general frameworks, benefits, and challenges and opportunities of ANN models to the constitutive modeling of composite materials. Moreover, potential applications of ANN-based constitutive models in composite materials and structures are also discussed. This review is intended to initiate discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures. • Applications of ANN models in composites constitutive modeling are reviewed. • Challenges of discovering constitutive laws are discussed. • Challenges of accelerating multiscale modeling are summarized. • Potential solutions to the challenges of ANN constitutive models are proposed. • Future opportunities for ANN-based constitutive models are discussed. [ABSTRACT FROM AUTHOR]

• A practical and effective method is presented for energy prediction of machine tool. • Energy consumption features are extracted by using unsupervised deep learning. • Supervised deep learning is used to develop energy prediction model of machine tool. • This method is a generalized way for identifying energy of different machine tools. • The results show that the method could improve the energy prediction performance. Energy prediction of machine tools plays an irreplaceable role in energy planning, management, and conservation in the manufacturing industry. In the era of big machinery data, data-driven energy prediction models of machine tools have achieved remarkable results in the identification of energy consumption patterns and prediction of energy consumption conditions. However, existing data-driven studies towards the energy consumption of machine tools focus on the utilization of handcrafted-feature learning methods, which are inefficient and exhibit poor generalization. Moreover, considering variations in energy consumption characteristics among different machine tools, it is impractical to identify energy consumption features manually for energy model development. Therefore, this paper proposes a novel data-driven energy prediction approach using deep learning algorithms. Here, deep learning is used in an unsupervised manner to extract sensitive energy consumption features from raw machinery data, and in a supervised manner to develop the prediction model between the extracted features and the energy consumption of machine tools. The experiments conducted on a milling machine and a grinding machine are exploited and compared with those conducted in conventional studies. The results show that the proposed method can improve the energy prediction performance from 19.14% to 74.13% for the grinding machine and from 64.89% to 85.61% for the milling machine, and it achieves a better performance than the conventional methods in terms of effectiveness and generalization. [ABSTRACT FROM AUTHOR]