Open Challenges of On-Machine and In-Process Metrology for Precision Manufacturing

A special issue of Metrology (ISSN 2673-8244).

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 2412

Special Issue Editors


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Guest Editor
Mechanical Research and Development, Kulicke and Soffa Industries, Fort Washington, PA 19034, USA
Interests: manufacturing metrology; precision machine design; dynamics and vibrations; materials characterization; actuators

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Guest Editor
Key Laboratory of Polymer Processing Engineering of the Ministry of Education, National Engineering Research Center of Novel Equipment for Polymer Processing, Guangdong Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou 510641, China
Interests: modelling, measurement and compensation of machining errors; error separation techniques; uncertainty analysis; signal processing and fault diagnosis

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Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: surface metrology; dimensional metrology; on-machine metrology for advanced manufacturing systems

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Guest Editor
Departamento de Ingeniería de Diseño y Fabricación, Centro Politécnico Superior, Universidad de Zaragoza, C/María de Luna, 3 50018 Zaragoza, Spain
Interests: dimensional metrology; machine vision metrology; portable metrology; robot metrology; gear metrology; surface inspection; numerical uncertainty analysis; numerical models for kinematic parameters identification; additive manufacturing; artificial intelligence and machine learning

Special Issue Information

Dear Colleagues,

This Special Issue will cover the current and most-pressing open challenges in the application of on-machine and in-process metrology for precision manufacturing.

Submissions are particularly welcomed in the following subject areas:

1. Measurement for applications in process monitoring and control

- Innovative measuring technologies for on-machine and in-process metrology of fundamental quantities;

- On-Machine and in-process measurement for monitoring components of manufacturing systems or entire manufacturing machines (e.g. rotary spindle metrology, linear axis metrology, etc.);

- On-Machine and in-process measurement of roundness/cylindricity/straightness/flatness;

- On-Machine and in-process measurement of surface topography;

- On-Machine and in-process measurement at micrometric and sub-micrometric scales.

2. Measurement uncertainty estimation and calibration

- Estimation of measurement uncertainty and assessment of measurement error sources for on-machine and in-process measurement;

- Calibration and self-calibration of measuring systems for on-machine and in-process metrology.

3. Data processing

- Novel computational solutions for fast and lightweight measurement data processing, for application in on-machine and in-process metrology;

- AI and machine learning for on-machine and in-process metrology: novel frontiers in data processing, data interpretation, uncertainty estimation and calibration;

- Digital twins to support on-machine and in-process metrology.

Dr. Sudhanshu Nahata
Dr. Shengyu Shi
Prof. Dr. Nicola Senin
Prof. Dr. Jorge Santolaria Mazo
Guest Editors

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. Metrology 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) for publication in this open access journal is 1000 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

  • machine tool metrology
  • dimensional metrology
  • spindle metrology
  • error separation techniques
  • novel measurement approaches
  • micro- and nano-manufacturing
  • computational metrology
  • uncertainty analysis
  • surface metrology

Published Papers (1 paper)

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Research

17 pages, 12417 KiB  
Article
Creepage Distance Estimation of Hairpin Stators Using 3D Feature Extraction
by Niklas Grambow, Lennart Hinz, Christian Bonk, Jörg Krüger and Eduard Reithmeier
Metrology 2023, 3(2), 169-185; https://doi.org/10.3390/metrology3020010 - 08 May 2023
Cited by 1 | Viewed by 1726
Abstract
The increasing demand for electric drives challenges conventional powertrain designs and requires new technologies to increase production efficiency. Hairpin stator manufacturing technology enables full automation, and quality control within the process is particularly important for increasing the process capacity, avoiding rejects and for [...] Read more.
The increasing demand for electric drives challenges conventional powertrain designs and requires new technologies to increase production efficiency. Hairpin stator manufacturing technology enables full automation, and quality control within the process is particularly important for increasing the process capacity, avoiding rejects and for safety-related aspects. Due to the complex, free-form geometries of hairpin stators and the required short inspection times, inline reconstruction and accurate quantification of relevant features is of particular importance. In this study, we propose a novel method to estimate the creepage distance, a feature that is crucial regarding the safety standards of hairpin stators and that could be determined neither automatically nor accurately until now. The data acquisition is based on fringe projection profilometry and a robot positioning system for a highly complete surface reconstruction. After alignment, the wire pairs are density-based clustered so that computations can be parallelized for each cluster, and an analysis of partial geometries is enabled. In several further steps, stripping edges are segmented automatically using a novel approach of spatially asymmetric windowed local surface normal variation, and the creepage distances are subsequently estimated using a geodesic path algorithm. Finally, the approach is examined and discussed for an entire stator, and a methodology is presented that enables the identification of implausible estimated creepage distances. Full article
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