Special Issue "Selected Papers from ICI2017 and Spintech Thesis Awards"

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and innovation in Advanced Manufacturing".

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Chien-Hung Liu

Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
Website | E-Mail
Fax: +886 4 2287 7170
Interests: high precision instrument design; laser engineering; smart sensors and actuators; optical device; optical measurement; metrology

Special Issue Information

Dear Colleagues,

This Special Issue will select the papers from ICI2017 (2017 3rd International Conference on Inventions, http://sciforum.net/conference/ICI2017) and Spintech Thesis Awards. The aims and scope of the 2017 3rd International Conference on Inventions is to make researchers focus on patent-based research. Papers with innovative ideas in all aspects of science and engineering are also encouraged in this Special Issue. 

Prof. Dr. Chien-Hung Liu
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 papers will be 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. Inventions is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • Invention and innovation in advanced manufacturing
  • Invention/innovation in applied optics and lasers
  • Invention/innovation in devices, sensors and actuators
  • Invention/innovation in energy and thermal/fluidic science
  • Invention/innovation in biotechnology/materials
  • Invention/innovation surface science/ nanotechnology technology
  • Invention/innovation in design/modeling/computing methods

Published Papers (6 papers)

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Research

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Open AccessArticle Identification of Milling Status Using Vibration Feature Extraction Techniques and Support Vector Machine Classifier
Received: 11 February 2018 / Revised: 5 April 2018 / Accepted: 11 April 2018 / Published: 17 April 2018
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Abstract
The objective of this study is to use the vibration signal features of spindles during the cutting processing to identify the different milling statuses in cases of diverse tooling parameter combinations. Accelerometers were placed on a spindle to measure vibration behaviors, and the
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The objective of this study is to use the vibration signal features of spindles during the cutting processing to identify the different milling statuses in cases of diverse tooling parameter combinations. Accelerometers were placed on a spindle to measure vibration behaviors, and the milling status could be divided into idle cutting, initial feeding, and stable cutting. Vibration signal processing and analysis were conducted in the time domain, as well as in the frequency domain. The original vibration measurements were separated using empirical mode decomposition (EMD) in the time domain, so that the signal features could be extracted in certain frequency bands and the useless signal components and trends could be removed. Multi-scale entropy (MSE) and root mean square (RMS) were computed to extract the time domain features. In the frequency domain, the specific intrinsic mode functions (IMFs) that were decomposed using the EMD method were analyzed by fast fourier transform (FFT) and a frequency normalization technique to extract the features of apparent physical representations. The Fisher scores (FS) of the extracted features are calculated to select the high-priority signal features. The selected high-priority signal features are utilized to identify the different milling statuses through a support vector machine (SVM). The results show that an identification accuracy of 98.21% could be obtained at the Z axis, and the average accuracy would be 95.91% for the three axes combination. Full article
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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Open AccessArticle Development of a Dung Beetle Robot and Investigation of Its Dung-Rolling Behavior
Received: 27 February 2018 / Revised: 3 April 2018 / Accepted: 4 April 2018 / Published: 10 April 2018
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Abstract
In this study, a bio-inspired dung beetle robot was developed that emulated the dung rolling motion of the dung beetle. Dung beetles, which can roll objects up to 1000 times their own body weight, are one of the strongest insect species in the
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In this study, a bio-inspired dung beetle robot was developed that emulated the dung rolling motion of the dung beetle. Dung beetles, which can roll objects up to 1000 times their own body weight, are one of the strongest insect species in the world. While the locomotion of many insects, such as cockroaches, inchworms, and butterflies, has been studied widely, the locomotion of dung beetles has rarely been given attention. Here, we report on the development of a dung beetle robot made specifically to investigate dung-rolling behavior and to determine and understand the underlying mechanism. Two versions of the robot were built, and the leg trajectories were carefully designed based on kinematic analysis. Cylinder and ball rolling experiments were conducted, and the results showed that the dung beetle robot could successfully and reliably roll objects. This further suggests that the dung beetle robot, with its current morphology, is capable of reliably rolling dung without the need for complex control strategies. Full article
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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Open AccessArticle Carbon Nanotubes Grown Using Solid Polymer Chemical Vapor Deposition in a Fluidized Bed Reactor with Iron(III) Nitrate, Iron(III) Chloride and Nickel(II) Chloride Catalysts
Received: 2 January 2018 / Revised: 22 February 2018 / Accepted: 24 February 2018 / Published: 15 March 2018
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Abstract
In this study, multi-walled carbon nanotubes (MW-CNT) were successfully synthesized using a chemical vapor deposition-fluidized bed (CVD-FB), with 10% hydrogen and 90% argon by volume, and a reaction temperature between 750 and 850 °C in a specially designed three-stage reactor. A solid state
[...] Read more.
In this study, multi-walled carbon nanotubes (MW-CNT) were successfully synthesized using a chemical vapor deposition-fluidized bed (CVD-FB), with 10% hydrogen and 90% argon by volume, and a reaction temperature between 750 and 850 °C in a specially designed three-stage reactor. A solid state of polyethylene (PE) was used as a carbon source and iron(III) nitrate, iron(III) chloride, and nickel(II) chloride were used as catalysts. Scanning and transmission electron microscopy and Raman spectrum analysis were used to analyze and examine the morphology and characteristics of the CNTs. A thermogravimetric analyzer was used to determine the purification temperature for the CNTs. Experimental results showed that the synthesis with iron-based catalysts produced more carbon filaments. Nickel(II) chloride catalysis resulted in the synthesis of symmetrical MW-CNTs with diameters between 30 and 40 nanometers. This catalyst produced the best graphitization level (ID/IG) with a value of 0.89. Excessively large particle size catalysts do not cluster carbon effectively enough to grow CNTs and this is the main reason for the appearance of carbon filaments. Full article
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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Open AccessArticle A Remote Controlled Robotic Arm That Reads Barcodes and Handles Products
Received: 5 February 2018 / Revised: 23 February 2018 / Accepted: 8 March 2018 / Published: 12 March 2018
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Abstract
In this study, a 6-axis robotic arm, which was controlled by an embedded Raspberry Pi with onboard WiFi, was developed and fabricated. A mobile application (APP), designed for the purpose, was used to operate and monitor a robotic arm by means of a
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In this study, a 6-axis robotic arm, which was controlled by an embedded Raspberry Pi with onboard WiFi, was developed and fabricated. A mobile application (APP), designed for the purpose, was used to operate and monitor a robotic arm by means of a WiFi connection. A computer vision was used to read common one-dimensional barcode (EAN code) for the handling and identification of products such as milk tea drinks, sodas and biscuits. The gripper on the end of the arm could sense the clamping force and allowed real-time control of the amount of force used to hold and handle the products. The packages were all made of different material and this control allowed them to be handled without danger of damage or deformation. The maximum handling torque used was ~1.08 Nm and the mechanical design allowed the force of the gripper to be uniformly applied to the sensor to ensure accurate measurement of the force. Full article
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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Open AccessArticle Numerical Analysis of CNC Milling Chatter Using Embedded Miniature MEMS Microphone Array System
Received: 22 November 2017 / Revised: 11 January 2018 / Accepted: 12 January 2018 / Published: 16 January 2018
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Abstract
With the increasingly common use of industrial automation for mass production, there are many computer numerical control (CNC) machine tools that require the collection of data from intelligent sensors in order to analyze their processing quality. In general, for high speed rotating machines,
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With the increasingly common use of industrial automation for mass production, there are many computer numerical control (CNC) machine tools that require the collection of data from intelligent sensors in order to analyze their processing quality. In general, for high speed rotating machines, an accelerometer can be attached on the spindle to collect the data from the detected vibration of the CNC. However, due to their cost, accelerometers have not been widely adopted for use with typical CNC machine tools. This study sought to develop an embedded miniature MEMS microphone array system (Radius 5.25 cm, 8 channels) to discover the vibration source of the CNC from spatial phase array processing. The proposed method utilizes voice activity detection (VAD) to distinguish between the presence and absence of abnormal noise in the pre-stage, and utilizes the traditional direction of arrival method (DOA) via multiple signal classification (MUSIC) to isolate the spatial orientation of the noise source in post-processing. In the numerical simulation, the non-interfering noise source location is calibrated in the anechoic chamber, and is tested with real milling processing in the milling machine. As this results in a high background noise level, the vibration sound source is more accurate in the presented energy gradation graphs as compared to the traditional MUSIC method. Full article
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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Graphical abstract

Review

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Open AccessReview A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools
Received: 19 April 2018 / Revised: 8 June 2018 / Accepted: 26 June 2018 / Published: 27 June 2018
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Abstract
This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in
[...] Read more.
This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results. Full article
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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