Xin Lin

Bio: Xin Lin is an academic researcher from Wuhan University of Science and Technology. The author has contributed to research in topics: Process control & Matching (statistics). The author has an hindex of 3, co-authored 5 publications receiving 13 citations. Previous affiliations of Xin Lin include National University of Singapore.

Metal-based additive manufacturing condition monitoring methods: From measurement to control

Abstract: Compared with other additive manufacturing processes, the metal-based additive manufacturing (MAM) can build higher precision and higher density parts, and have unique advantages in the applications to automotive, medical, and aerospace industries. However, the quality defects of builds, such as dimensional accuracy, layer morphology, mechanical and metallurgical defects, have been hindering the wide applications of MAM technologies. These decrease the repeatability and consistency of build quality. In order to overcome these shortcomings and to produce high-quality parts, it is very important to carry out online monitoring and process control in the building process. A process monitoring system is demanded which can automatically optimize the process parameters to eliminate incipient defects, improve the process stability and the final build quality. In this paper, the current representative studies are selected from the literature, and the research progress of MAM process monitoring and control are surveyed. Taking the key components of the MAM monitoring system as the mainstream, this study investigates the MAM monitoring system, measurement and signal acquisition, signal and image processing, as well as machine learning methods for the process monitoring and quality classification. The advantages and disadvantages of their algorithmic implementations and applications are discussed and summarized. Finally, the prospects of MAM process monitoring researches are advised.

Online monitoring method of laser additive manufacturing process based on multi-source heterogeneous data

Abstract: The invention discloses an online monitoring method of a laser additive manufacturing process based on multi-source heterogeneous data. The online monitoring method of the laser additive manufacturingprocess based on the multi-source heterogeneous data comprises the steps of 1, building laser energy distribution; 2, obtaining a medium vibration speed; 3, obtaining the maximum surface temperatureof a powder bed; 4, obtaining the width of a molten pool; 5, building a multi-field coupling thermodynamic model during a laser melting process; 6, sparse-coding the multi-source heterogeneous data; and 7, obtaining an online monitoring model. According to the online monitoring method of the laser additive manufacturing process based on the multi-source heterogeneous data provided by the invention, the laser additive manufacturing process can be monitored in real time and controlled, so that process parameters are automatically adjusted when micro defects appear so as to eliminate the defects,the part forming quality is further improved, and the actual precision and reliability demands are met.

Intelligent modeling and monitoring of micro-droplet profiles in 3D printing.

Abstract: The inkjet 3D printing has been one of the most studied and applied additive manufacturing (AM) processes in electronic industry. In this AM process, the forming quality is greatly influenced by the micro-droplet deposition and substrate temperature. While most studies focus on the formation mechanism of droplets, there are few studies on the quantitative evaluation of the droplet surface profile and its qualitative correlation with temperature changes. In this study, the characteristics of droplet profile in three-dimensional inkjet printing were studied from two aspects, the modeling of droplet shape and the estimation of droplet temperature. For this purpose, different types of radial basis function networks (RBFN) are applied. The validity of the regularized RBFN model is developed and verified by experiments. The results show that the droplet shape can be accurately modeled and the drying temperature can be accurately estimated given the model.

Accurate defect fingerprint on-line monitoring method for additional material fabrication and feedback strategy

Abstract: The invention relates to an accurate defect fingerprint on-line monitoring method for additional material fabrication and a feedback strategy. The method comprises defect fingerprint database establishing, defect information matching and automatic correcting. Defects are obtained by a multi-scale map analysis algorithm; data containing information on the size of each defect, manufacturing conditions of defect generation, and a defect elimination strategy are established; and further a defect fingerprint database is established. The multi-scale map analysis algorithm comprises image preprocessing, image sampling, image feature point detecting and edge line extracting. The algorithm combines the multi-scale analysis method with the mapping theory, and provides an iterative algorithm of sampling, dimensionality reducing and on-line recognizing to accelerate calculation. Through the detect fingerprint database, when micro defect occurs during the additional material processing and fabricating, a solution can be found, process parameters in the next layer of powder laying and scanning can be adjusted automatically to eliminate the detect during shaping, and therefore quality of a finished accessory can be improved.

Efficient training of RBF neural networks for pattern recognition

Abstract: The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in R/sup n/ is considered. The network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns. In this paper, taking into account the specific feature of classification problems, where the goal is to obtain that the network outputs take values above or below a fixed threshold, we propose an approach alternative to the classical one that makes use of the least-squares error function. In particular, the problem is formulated in terms of a system of nonlinear inequalities, and a suitable error function, which depends only on the violated inequalities, is defined. Then, a training algorithm based on this formulation is presented. Finally, the results obtained by applying the algorithm to two test problems are compared with those derived by adopting the commonly used least-squares error function. The results show the effectiveness of the proposed approach in RBF network training for pattern recognition, mainly in terms of computational time saving.

Machine learning and deep learning based predictive quality in manufacturing: a systematic review

Abstract: Abstract With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided.