Jie Sun

Bio: Jie Sun is an academic researcher from Xi'an Jiaotong-Liverpool University. The author has contributed to research in topics: Computer science & Coating. The author has an hindex of 20, co-authored 78 publications receiving 1621 citations. Previous affiliations of Jie Sun include National University of Singapore.

An Overview of 3D Printing Technologies for Food Fabrication

Abstract: Different from robotics-based food manufacturing, three-dimensional (3D) food printing integrates 3D printing and digital gastronomy to revolutionize food manufacturing with customized shape, color, flavor, texture, and even nutrition. Hence, food products can be designed and fabricated to meet individual needs through controlling the amount of printing material and nutrition content. The objectives of this study are to collate, analyze, categorize, and summarize published articles and papers pertaining to 3D food printing and its impact on food processing, as well as to provide a critical insight into the direction of its future development. From the available references, both universal platforms and self-developed platforms are utilized for food printing. These platforms could be reconstructed in terms of process reformulation, material processing, and user interface in the near future. Three types of printing materials (i.e., natively printable materials, non-printable traditional food materials, and alternative ingredients) and two types of recipes (i.e., element-based recipe and traditional recipe) have been used for customized food fabrication. The available 3D food printing technologies and food processing technologies potentially applicable to food printing are presented. Essentially, 3D food printing provides an engineering solution for customized food design and personalized nutrition control, a prototyping tool to facilitate new food product development, and a potential machine to reconfigure a customized food supply chain.

Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing

Abstract: This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.

Multiclassification of tool wear with support vector machine by manufacturing loss consideration

Abstract: Tool wear is a dynamic process, as a tool progresses from sharp to worn state and possibly to breakage. Thus the multiclassification of tool states is preferred, which can provide more timely and accurate estimation of tool states. Based on acoustic emission (AE) sensing, this paper proposes a new performance evaluation function for tool condition monitoring (TCM) by considering manufacturing loss. Firstly, two types of manufacturing loss due to misclassification (loss caused by under prediction and loss caused by over prediction) are analyzed, and both are utilized to compute corresponding weights of the proposed performance evaluation function. Then the expected loss of future misclassification is introduced to evaluate the recognition performance of TCM. Finally, a revised support vector machine (SVM) approach coupled with one-versus-one method is implemented to carry out the multiclassification of tool states. With this approach, a tool is replaced or continued not only based on the tool condition alone, but also the risk in cost incurred due to underutilized or overused tool. The experimental results show that the proposed method can reliably perform multiclassificaion of tool flank wear, and reduce the potential manufacturing loss.

Polymers for 3D Printing and Customized Additive Manufacturing

Abstract: Additive manufacturing (AM) alias 3D printing translates computer-aided design (CAD) virtual 3D models into physical objects. By digital slicing of CAD, 3D scan, or tomography data, AM builds objects layer by layer without the need for molds or machining. AM enables decentralized fabrication of customized objects on demand by exploiting digital information storage and retrieval via the Internet. The ongoing transition from rapid prototyping to rapid manufacturing prompts new challenges for mechanical engineers and materials scientists alike. Because polymers are by far the most utilized class of materials for AM, this Review focuses on polymer processing and the development of polymers and advanced polymer systems specifically for AM. AM techniques covered include vat photopolymerization (stereolithography), powder bed fusion (SLS), material and binder jetting (inkjet and aerosol 3D printing), sheet lamination (LOM), extrusion (FDM, 3D dispensing, 3D fiber deposition, and 3D plotting), and 3D bioprinting....

Design for additive manufacturing: trends, opportunities, considerations, and constraints

Abstract: The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry.