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 9385
Special Issue Editors
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
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
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Keywords
- In-process sensing
- Data analytics
- IIoT
- Digital twins
- Quality control
- Fault diagnosis
- Predictive maintenance
- Data-based and Engineering-based models
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