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Machines
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Precision Manufacturing Processes
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Precision Manufacturing Processes

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 18001

Special Issue Editor

Assist. Prof. Xiaoliang Jin

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Interests: precision manufacturing; micro machining; vibration assisted machining; material behavior; machining mechanics; machining dynamics; bone drilling; control design

Special Issue Information

Dear Colleagues,

The precision manufacturing process plays a significant role in the development of various industrial sectors, such as optics, aerospace, electronics, and biomedicine. There is a critical need to enhance manufacturing processes to fulfill the stringent requirements of system miniaturization and surface integrity. In recent years, new materials, and new processing and characterization techniques have brought both opportunities and challenges in the research field of precision manufacturing processes. This Special Issue aims to bring together the research in addressing critical problems and proposing novel solutions to the state-of-art technology, in order to improve the process efficiency and product quality. This Special Issue will cover original research contributions in all aspects of precision manufacturing processes. We hope to deliver readers promising new ideas and directions for future developments in the field of precision manufacturing.

Assist. Prof. Xiaoliang Jin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Micro/nano manufacturing
  • Precision machining, additive manufacturing, embossing, forming and molding
  • Mechanical, electrical and chemical manufacturing processes
  • Process modeling and simulation
  • Experimental design and investigation
  • Process monitoring and control
  • Material behavior in precision manufacturing processes
  • Mechanics and dynamics in precision manufacturing processes
  • Surface characterization

Published Papers (2 papers)

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Research

2105 KiB  
Article
Physics-Embedded Machine Learning: Case Study with Electrochemical Micro-Machining
by Yanfei Lu, Manik Rajora, Pan Zou and Steven Y. Liang
Machines 2017, 5(1), 4; https://doi.org/10.3390/machines5010004 - 17 Jan 2017
Cited by 34 | Viewed by 8331
Abstract
Although intelligent machine learning techniques have been used for input-output modeling of many different manufacturing processes, these techniques map directly from the input process parameters to the outputs and do not take into consideration any partial knowledge available about the mechanisms and physics [...] Read more.
Although intelligent machine learning techniques have been used for input-output modeling of many different manufacturing processes, these techniques map directly from the input process parameters to the outputs and do not take into consideration any partial knowledge available about the mechanisms and physics of the process. In this paper, a new approach is presented for taking advantage of the partial knowledge available about the mechanisms of the process and embedding it into the neural network structure. To validate the proposed approach, it is used to create a forward prediction model for the process of electrochemical micro-machining (μ-ECM). The prediction accuracy of the proposed approach is compared to the prediction accuracy of pure neural structure models with different structures and the results show that the Neural Network (NN) models with embedded knowledge have better prediction accuracy over pure NN models. Full article
(This article belongs to the Special Issue Precision Manufacturing Processes)
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8682 KiB  
Article
Open Source Laser Polymer Welding System: Design and Characterization of Linear Low-Density Polyethylene Multilayer Welds
by John J. Laureto, Serguei V. Dessiatoun, Michael M. Ohadi and Joshua M. Pearce
Machines 2016, 4(3), 14; https://doi.org/10.3390/machines4030014 - 1 Jul 2016
Cited by 9 | Viewed by 9060
Abstract
The use of lasers to weld polymer sheets provides a means of highly-adaptive and custom additive manufacturing for a wide array of industrial, medical, and end user/consumer applications. This paper provides an open source design for a laser polymer welding system, which can [...] Read more.
The use of lasers to weld polymer sheets provides a means of highly-adaptive and custom additive manufacturing for a wide array of industrial, medical, and end user/consumer applications. This paper provides an open source design for a laser polymer welding system, which can be fabricated with low-cost fused filament fabrication and off-the-shelf mechanical and electrical parts. The system is controlled with free and open source software and firmware. The operation of the machine is validated and the performance of the system is quantified for the mechanical properties (peak load) and weld width of linear low density polyethylene (LLDPE) lap welds manufactured with the system as a function of linear energy density. The results provide incident laser power and machine parameters that enable both dual (two layers) and multilayer (three layers while welding only two sheets) polymer welded systems. The application of these parameter sets provides users of the open source laser polymer welder with the fundamental requirements to produce mechanically stable LLDPE multi-layer welded products, such as heat exchangers. Full article
(This article belongs to the Special Issue Precision Manufacturing Processes)
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