Designing the Smart Operator 4.0 for Human Values: A Value Sensitive Design Approach

Emerging technologies such as cloud computing, augmented and virtual reality, artificial intelligence and robotics, among others, are transforming the field of manufacturing and industry as a whole in unprecedent ways. This fourth industrial revolution is consequentially changing how operators that have been crucial to industry success go about their practices in industrial environments. This short paper briefly introduces the notion of the Operator 4.0 as well as how this novel way of conceptualizing the human operator necessarily implicates human values in the technologies that constitute it. Similarly, the design methodology known as value sensitive design (VSD) is drawn upon to discuss how these Operator 4.0 technologies can be design for human values and, conversely, how a potential value-sensitive Operator 4.0 can be used to strengthen the VSD methodology in developing novel technologies

Value-oriented and ethical technology engineering in Industry 5.0: a human-centric perspective for the design of the Factory of the Future

Manufacturing and industry practices are undergoing an unprecedented revolution as a consequence of the convergence of emerging technologies such as artificial intelligence, robotics, cloud computing, virtual and augmented reality, among others. This fourth industrial revolution is similarly changing the practices and capabilities of operators in their industrial environments. This paper introduces and explores the notion of the Operator 4.0 as well as how this novel way of conceptualizing the human operator necessarily implicates human values in the technologies that constitute it. The design approach known as value sensitive design (VSD) is used to explore how these Operator 4.0 technologies can be designed for human values. Expert elicitation surveys were used to determine the values of industry stakeholders and examples of how the VSD methodology can be adopted by engineers in order to design for these values is illustrated. The results provide preliminary adoption strategies that industrial teams can take to Operator 4.0 technology for human values

Shipbuilding Supply Chain Framework and Digital Transformation: A Project Portfolios Risk Evaluation

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Editorial/Preface

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Human Factors, Ergonomics and Industry 4.0 in the Oil & Gas Industry: A Bibliometric Analysis

Over the last few years, the Human Factors and Ergonomics (HF/E) discipline has significantly benefited from new human-centric engineered digital solutions of the 4.0 industrial age. Technologies are creating new socio-technical interactions between human and machine that minimize the risk of design-induced human errors and have largely contributed to remarkable improvements in terms of process safety, productivity, quality, and workers’ well-being. However, despite the Oil&Gas (O&G) sector is one of the most hazardous environments where human error can have severe consequences, Industry 4.0 aspects are still scarcely integrated with HF/E. This paper calls for a holistic understanding of the changing role and responsibilities of workers in the O&G industry and aims at investigating to what extent, what type of, and how academic publications in the O&G field integrate HF/E and Industry 4.0 in their research. Bibliometric analysis has been conducted to provide useful insights to researchers and practitioners and to assess the status quo. Our findings show that academic publications have mainly focused on simulation-based training to increase process safety whereas revealed the lack of specific studies on the application of cognitive solutions, such as Augmented Reality-enabled tools or Intelligent Fault Detection and Alarm Management solutions

Developing an Artificial Intelligence Framework to Assess Shipbuilding and Repair Sub-Tier Supply Chains Risk

The defense shipbuilding and repair industry is a labor-intensive sector that can be characterized by low-product volumes and high investments in which a large number of shared resources, technology, suppliers, and processes asynchronously converge into large construction projects. It is mainly organized by the execution of a complex combination of sequential and overlapping stages. While entities engaged in this large-scale endeavor are often knowledgeable about their first-tier suppliers, they usually do not have insight into the lower tiers suppliers. A sizable part of any supply chain disruption is attributable to instabilities in sub-tier suppliers. This research note conceptually delineates a framework that considers the elicitation of the existing associations between suppliers and sub-tier suppliers. This framework, Shipbuilding Risk Supply Chain (Ship-RISC), offers a simulation framework to leverage real-time and data using an Industry 4.0 approach to generate descriptive and prescriptive analytics based on the execution of simulation models that support risk management assessment and decision-making

Machine Learning approach towards real time assessment of hand-arm vibration risk

In industry 4,0, the establishment of an interconnected environment where human operators cooperate with the machines offers the opportunity for substantially improving the ergonomics and safety conditions of the workplace. This topic is discussed in the paper referring to the vibration risk, which is a well-known cause of work-related pathologies. A wearable device has been developed to collect vibration data and to segment the signals obtained in time windows. A machine learning classifier is then proposed to recognize the worker’s activity and to evaluate the exposure to vibration risks. The experimental results demonstrate the feasibility and effectiveness of the methodology proposed

Fuzzy Cognitive Map-Based Knowledge Representation of Hazardous Industrial Operations

Hazardous industrial operations are highly stochastic, still human-dependent, and risky. Operators working in such an environment must understand the complex interrelation between several factors contributing to safe and effective operations. Therefore, being able to predict the effects of their actions on provoking or mitigating possible accidents is crucial. This study aims to utilize fuzzy cognitive maps (FCM) to model the expert’s reasoning about occupational health and safety (OHS) in confined space. This knowledge is used by operators to build their mental models. The developed FCM displays all the possible incidents of a confined space and links these incidents with all their causing and preventing factors. This approach may facilitate the development of simulation-based training solutions and allow operators to act proactively during the operation

Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images

Wearable and interactive mixed reality solutions for fault diagnosis and assistance in manufacturing systems: Implementation and testing in an aseptic bottling line

Abstract Thanks to the spread of technologies stemming from the fourth industrial revolution, also the topic of fault diagnosis and assistance in industrial contexts has benefited. Indeed, several smart tools were developed for assisting with maintenance and troubleshooting, without interfering with operations and facilitating tasks. In line with that, the present manuscript aims at presenting a web smart solution with two possible applications installed on an Android smartphone and Microsoft HoloLens. The solution aims at alerting the operators when an alarm occurs on a machine through notifications, and then at providing the instructions needed for solving the alarm detected. The two devices were tested by the operators of an industrial aseptic bottling line consisting of five machines in real working conditions. The usability of both devices was positively rated by these users based on the System Usability Scale (SUS) and additional appropriate statements. Moreover, the in situ application brought out the main difficulties and interesting issues for the practical implementation of the solutions tested