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Multistage Manufacturing Processes in the Industry 4.0 for Zero-Defect Products
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Multistage Manufacturing Processes in the Industry 4.0 for Zero-Defect Products

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (1 October 2022) | Viewed by 7286

Special Issue Editors

Dr. Jose Vicente Abellan-Nebot

E-Mail Website
Guest Editor
Department of Industrial Systems Engineering and Design, Universitat Jaume I, Avinguda de Vicent Sos Baynat, s/n, 12071 Castelló de la Plana, Castelló, Spain
Interests: intelligent machining; multistage manufacturing processes; quality control; design for manufacturing and assembly sustainable machining; green manufacturing; advanced manufacturing techniques; additive manufacturing; high-speed machining
Special Issues, Collections and Topics in MDPI journals
Dr. Ignacio Peñarrocha-Alós

E-Mail Website
Guest Editor
Department of Industrial Systems Engineering and Design, Universitat Jaume I, Avinguda de Vicent Sos Baynat, s/n, 12071 Castelló de la Plana, Castelló, Spain
Interests: networked control systems; fault diagnosis; systems identification; signal processing

Special Issue Information

Dear Colleagues,

One of the most important challenges in modern industry is the implementation of manufacturing systems that are capable of producing “zero-defect” products. In most cases, manufacturing consists of a sequence of stages where manufacturing operations are sequentially conducted to manufacture a part or product. These multistage manufacturing processes (MMP) show complex error interactions among stages, which makes it difficult to control product quality, and tasks such as predictive maintenance, process control, quality assurance and fault diagnosis are challenging.

Under the new paradigm of Industry 4.0, sensing networks based on IIoT and the implementation of digital twins based on engineering and data-based models is set to have a major impact on these processes. The implementation of this new paradigm is expected to lead to manufacturing systems with self-adjust and self-optimization capabilities, optimal decision making based on simulated-driven strategies, correction actions for error compensation, optimal predictive maintenance actions, and so on.

In this Special Issue, we encourage scholars to share recent advances in the field of MMPs and Industry 4.0. Investigations related to in-process sensing and data analytics, IIoT, fault diagnosis, digital twins, predictive maintenance, quality assurance and quality control are welcome, especially those focused on strategies for “zero defect” manufacturing.

Dr. Jose Vicente Abellan-Nebot
Dr. Ignacio Peñarrocha-Alós
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • In-process sensing
  • Data analytics
  • IIoT
  • Digital twins
  • Quality control
  • Fault diagnosis
  • Predictive maintenance
  • Data-based and Engineering-based models

Published Papers (3 papers)

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Research

22 pages, 5005 KiB  
Article
Trajectory Optimization in Terms of Energy and Performance of an Industrial Robot in the Manufacturing Industry
by Carlos Garriz and Rosario Domingo
Sensors 2022, 22(19), 7538; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197538 - 05 Oct 2022
Cited by 9 | Viewed by 1797
Abstract
Currently, the high demand for new products in the automotive sector requires large investments in factories. The automotive industry is characterized by high automatization, largely achieved by manipulator robots capable of multitasking. This work presents a method for the optimization of trajectories in [...] Read more.
Currently, the high demand for new products in the automotive sector requires large investments in factories. The automotive industry is characterized by high automatization, largely achieved by manipulator robots capable of multitasking. This work presents a method for the optimization of trajectories in robots with six degrees of freedom and a spherical wrist. The optimization of trajectories is based on the maximization of manipulability and the minimization of electrical energy. For this purpose, it is necessary to take into account the kinematics and dynamics of the manipulator in order to integrate an algorithm for calculation. The algorithm is based on the Kalman method. This algorithm was implemented in a simulation of the trajectories of a serial industrial robot, in which the robot has a sealer gun located on its sixth axis and the quality of the sealer application depends directly on the orientation of the gun. During the optimization of the trajectory, the application of the sealer must be guaranteed. This method was also applied to three different trajectories in the automotive sector. The obtained results for manipulability and electrical energy consumption prove the efficiency of the algorithm. Therefore, this method searches for the optimal solution within the limits of the manipulator and maintains the orientation of the final effector. This can be used for a known trajectory. Full article
(This article belongs to the Special Issue Multistage Manufacturing Processes in the Industry 4.0 for Zero-Defect Products)
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16 pages, 2788 KiB  
Article
A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes
by Rubén Moliner-Heredia, Gracia M. Bruscas-Bellido, José V. Abellán-Nebot and Ignacio Peñarrocha-Alós
Sensors 2021, 21(22), 7524; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227524 - 12 Nov 2021
Cited by 3 | Viewed by 1978
Abstract
Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure [...] Read more.
Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure to detect the process fault based on a sequential testing algorithm and a minimum monitoring system is proposed. After the monitoring system detects that the process is out of statistical control, the features to be inspected (end of line or in process measurements) are defined sequentially according to the expected information gain of each potential inspection measurement. A case study is analyzed to prove the benefits of this approach with respect to a predefined inspection scheme and a randomized sequential inspection considering both the use and non-use of fault probabilities from historical maintenance data. Full article
(This article belongs to the Special Issue Multistage Manufacturing Processes in the Industry 4.0 for Zero-Defect Products)
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22 pages, 3103 KiB  
Article
Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine
by Francisco Javier Álvarez García and David Rodríguez Salgado
Sensors 2021, 21(20), 6809; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206809 - 13 Oct 2021
Cited by 4 | Viewed by 2401
Abstract
The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming [...] Read more.
The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming machine. Specifically, two strategies based on preventive maintenance, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) are compared with two new strategies based on predictive maintenance, namely Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT). The condition of machine components can be assessed with the latter two proposals (ALOP and DBT) using sensors and algorithms, thus providing a warning value for early decision-making before unexpected faults occur. The study shows that the ALOP and DBT models detect unexpected failures early enough, while the PPM and IPPM strategies warn of scheduled component replacement at the end of their life cycle. The ALOP and DBT strategies algorithms can also be valid for managing the maintenance of other multi-stage industrial manufacturing machines. The authors consider that the combination of preventive and predictive maintenance strategies may be an ideal approach because operating conditions affect the mechanical, electrical, electronic and pneumatic components of multi-stage industrial manufacturing machines differently. Full article
(This article belongs to the Special Issue Multistage Manufacturing Processes in the Industry 4.0 for Zero-Defect Products)
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