In the Industry 4.0 era, the focus has been on replacing human operators with industrial robots to reduce labors costs. However, it has led to various social and environmental challenges. Industry 5.0 aims to enhance human-robot collaboration (HRC) in intelligent manufacturing environments, promoting both efficiency and flexibility while prioritizing human operators’ health and well-being. This study reconfigures a new HRC systems, optimizes layout, and introduces a new Internet of Things (IoT) structure to monitor human-centric manufacturing processes. By utilizing medical-grade sensors for real-time collection of physiological data, the system ensures privacy compliance. The collected data are input into a artificial intelligence algorithm to achieve two main objectives: evaluating operator’s health levels and identifying the most efficient movement patterns during the manufacturing process. This research has significant implications for enhancing operator health and well-being in industrial settings.

Integrating process-support functionalities within legal for trade-certified metering heads is a significant challenge in the digital transformation of industrial metering. In this context, we present a virtualized software architecture successfully implemented on the TEX® electronic metering head by IDEX/Sampi S.p.A., a commercially available, legally certified metrological device. The resulting system includes a host machine running legal metrology software and a virtualized guest machine managing auxiliary functions, which may be developed by third-party integrators. The guest handles the display, keypad, and I/O ports when no measurement is in progress. During measurement, the legal metrology software takes preemptive control to ensure legally relevant pulse counting, visualization of metrological data, printouts via sealed printers, and data storage in a database with read-only access for the guest system. This approach enables software extensibility without requiring frequent re-certifications while maintaining regulatory compliance, data integrity, and system flexibility. The proposed solution advances modern industrial metering, as demonstrated in a real-world use case.

The mining sector is undergoing a profound shift as Industry 4.0 technologies—IoT, AI, robotics—reshape operational planning and execution. This study explores the integration of Mine IoT to modernize mining practices, emphasizing metrology-driven advancements in real-time monitoring, predictive maintenance, and autonomous systems. Accurate measurement and standardized data are central to improving efficiency, enhancing safety, and advancing sustainability goals. A smart ventilation case study illustrates how RAMI 4.0, combined with digital metrology, enhances interoperability, enables seamless integration, and supports scalable, adaptable systems for improved energy use, safety, and long-term resilience. Metrological traceability is embedded throughout system layers to support interoperability and long-term performance. RAMI 4.0’s structured framework ensures traceable data from calibrated sensors and uncertainty-aware analytics, aiding reliable decision-making and regulatory compliance. The study also highlights the role of standardization in facilitating communication across devices, platforms, and vendors. Achieving these outcomes requires strategic planning, skilled personnel, and cross-sector collaboration.

A data driven methodology for sensor fault diagnosis in sensor network using principal component analysis (PCA) of coherence spectrum and convolutional neural network (CNN) deep learning is proposed. The methodology was evaluated with the measurement data of a sensor network for ambient relative humidity (RH) monitoring of a chemical laboratory. The results demonstrated accuracy up to 99% for sensor fault diagnosis in the sensor network functioning across a large spectrum of frequencies for environmental monitoring.

This work explores the automation of a photometry laboratory by integrating LabVIEW as the primary control and data acquisition platform, coupled with a Python-based artificial intelligence (AI) algorithm for intelligent analysis and optimization. The system is designed to measure and analyze critical photometric parameters such as luminous intensity, luminous responsivity, illuminance, and luminous flux—all of which are essential for evaluating the performance of light sources like tungsten halogen lamps, LEDs, displays, and optical sensors. By automating traditionally manual processes, the system enhances both the accuracy and efficiency of photometric testing. LabVIEW provides an intuitive graphical interface to control instruments, log data, and visualize results in real time. Meanwhile, the Python AI component improves decision-making by learning from historical data, predicting trends, and detecting anomalies or inconsistencies in measurements. The AI model optimizes calibration routines, reduces human error, and enables adaptive testing scenarios based on environmental conditions or device behavior. Together, LabVIEW and Python form a powerful, flexible platform that brings modern intelligence to photometry labs, making them faster, smarter, and more reliable. This approach demonstrates how combining industrial automation tools with machine learning can revolutionize traditional optical testing environments, paving the way for advanced lighting technologies and robust quality assurance systems.