Longwei Cheng

Bio: Longwei Cheng is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Statistical model & Statistical process control. The author has an hindex of 4, co-authored 4 publications receiving 85 citations.

Statistical transfer learning: A review and some extensions to statistical process control

Abstract: The rapid development of information technology, together with advances in sensory and data acquisition techniques, has led to the increasing necessity of handling datasets from multiple domains. In recent years, transfer learning has emerged as an effective framework for tackling related tasks in target domains by transferring previously-acquired knowledge from source domains. Statistical models and methodologies are widely involved in transfer learning and play a critical role, which, however, has not been emphasized in most surveys of transfer learning. In this article, we conduct a comprehensive literature review on statistical transfer learning, i.e., transfer learning techniques with a focus on statistical models and statistical methodologies, demonstrating how statistics can be used in transfer learning. In addition, we highlight opportunities for the use of statistical transfer learning to improve statistical process control and quality control. Several potential future issues in statistic...

A Statistical Transfer Learning Perspective for Modeling Shape Deviations in Additive Manufacturing

Abstract: Quality control of additive manufacturing applications is required to improve the shape fidelity of the products, which relies on increasing the predictive performance of statistical deviation models for any new shape. Building a single comprehensive model for a wide range of shapes is a very challenging problem, since the error generating mechanism of additive manufacturing applications is usually of high complexity, the amount of training data is usually limited, and the connection among different shapes is unknown. In this study, a novel shape deviation modeling scheme is proposed. In this scheme, the dimensional error of the product is modeled in a parameter-based transfer learning approach. In particular, the shape deviation is decomposed into two components: the shape-independent error and the shape-specific error. The shape-independent error is described by a statistical model that incorporates the engineering knowledge. Guidelines to investigate modeling of the shape-specific error are also given.

A prediction and compensation scheme for in-plane shape deviation of additive manufacturing with information on process parameters

Abstract: Shape fidelity is a critical issue that hinders the wider application of Additive Manufacturing (AM) technologies. In many AM processes, the shape of a product is usually different from its input design and the deviation usually depends on certain process parameters. In this article, we aim to improve the shape fidelity of AM products through compensation, with the information on these parameters. To achieve this, a two-step hierarchical scheme is proposed to predict the in-plane deviation of the product shape, which relates to the process parameters and the two-dimensional input shape. Based on this prediction procedure, a shape compensation strategy is developed that greatly improves the dimensional accuracy of products. Experimental studies of fused deposition modeling processes validate the effectiveness of our proposed scheme in terms of both predicting the shape deviation and improving the shape accuracy.

A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing

Abstract: Shape accuracy control is one of the quality issues of greatest concern in Additive Manufacturing (AM). An efficient approach to improving the shape accuracy of a fabricated product is to compensat...

Statistics for Spatio-Temporal Data

Abstract: Noel Cressie and Christopher WikleHardcover: 624 pagesYear: 2011Publisher: John WileyISBN-13: 978-0471692744Here is the new reference book about spatial and spatio-temporal statistical modeling! No...

Opportunities and challenges of quality engineering for additive manufacturing

Abstract: Additive manufacturing (AM), commonly known as three-dimensional printing, is widely recognized as a disruptive technology, and it has the potential to fundamentally change the nature of future man

Machine learning in predicting mechanical behavior of additively manufactured parts

Abstract: Although applications of additive manufacturing (AM) have been significantly increased in recent years, its broad application in several industries is still under progress. AM also known as three-dimensional (3D) printing is layer by layer manufacturing process which can be used for fabrication of geometrically complex customized functional end-use products. Since AM processing parameters have significant effects on the performance of the printed parts, it is necessary to tune these parameters which is a difficult task. Today, different artificial intelligence techniques have been utilized to optimize AM parameters and predict mechanical behavior of 3D-printed components. In the present study, applications of machine learning (ML) in prediction of structural performance and fracture of additively manufactured components has been presented. This study first outlines an overview of ML and then summarizes its applications in AM. The main part of this review, focuses on applications of ML in prediction of mechanical behavior and fracture of 3D-printed parts. To this aim, previous research works which investigated application of ML in characterization of polymeric and metallic 3D-printed parts have been reviewed and discussed. Moreover, the review and analysis indicate limitations, challenges, and perspectives for industrial applications of ML in the field of AM. Considering advantages of ML increase in applications of ML in optimization of 3D printing parameters, prediction of mechanical performance, and evaluation of 3D-printed products is expected.

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

Abstract: The possibility of using a variety of sensor signals acquired during metal powder bed fusion processes, to support part and process qualification and for the early detection of anomalies and defects, has been continuously attracting an increasing interest. The number of research studies in this field has been characterised by significant growth in the last few years, with several advances and new solutions compared with first seminal works. Moreover, industrial powder bed fusion (PBF) systems are increasingly equipped with sensors and toolkits for data collection, visualisation and, in some cases, embedded in-process analysis. Many new methods have been proposed and defect detection capabilities have been demonstrated. Nevertheless, several challenges and open issues still need to be tackled to bridge the gap between methods proposed in the literature and actual industrial implementation. This paper presents an updated review of the literature on in-situ sensing, measurement and monitoring for metal PBF processes, with a classification of methods and a comparison of enabled performances. The study summarises the types and sizes of defects that are practically detectable while the part is being produced and the research areas where additional technological advances are currently needed.