Factory Floor Research Articles

Wearable network for multilevel physical fatigue prediction in manufacturing workers.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

Hierarchical Semi-Supervised Approach for Classifying Activities of Workers Utilising Indoor Trajectory Data

Activity recognition refers to the process of automatically identifying or interpreting activities of objects based on the data captured from different sensing devices. While previous research on indoor activity recognition predominantly relies on visual data like images or video recordings, we present a novel approach based on spatiotemporal trajectory data recorded by IoT based sensors. The proposed approach is tailored for indoor manufacturing applications leveraging trajectory partitioning, hierarchical clustering, and convolutional neural networks. Moreover, a vast majority of activity recognition models that have been used in different industrial settings are supervised methods requiring large manually labelled datasets to be collected. This manual annotation process is unwieldly and labour-intensive, and hence, often infeasible to deploy for practical applications. In contrast, our proposed activity recognition approach is semi-supervised meaning it can be trained with far less labelled data; significantly reducing the effort and costs associated with the manual annotation process. The proposed approach is evaluated using two indoor trajectory datasets related to different manufacturing assembly processes. Experimental results demonstrate its effectiveness for activity recognition: the classification accuracy (measured using F-score) varies between 0.81 to 0.95 and 0.88 to 0.92 across indoor trajectory datasets. A comparison with a baseline model indicates that it achieves up to a 18% improvement in classification accuracy. Furthermore, the classification results enable insights into factory floor states, aiding in decision-making for operational efficiency and resource allocation.

Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

Electrolyte jet tomography: Three-dimensional microstructure mapping with an electrochemical machine tool and an optical microscope

There is a general separation between the manufacturing processes that add value to materials on the factory floor and the techniques engineers use in the laboratory to evaluate the microstructures and the surface integrity that results. These techniques are often destructive or require a vacuum and are incompatible with production lines. However, this information has intrinsic value and could be exploited to inform production decisions during manufacture. In this study, a novel approach to acquire this information is presented that is underpinned by electrolyte jet machine tool coupled with optical microscopy, which can allow the extraction of both grain-wise partial orientation and morphological information, and crystallographic macro textures in three dimensions. Here, iterative sections are precisely machined into the near surface of a commercially pure titanium alloy using an electrochemical jet and subsequently imaged, allowing the reconstruction of high-fidelity microstructure models rapidly and under ambient conditions. In doing so, new insights into the specific orientation-dependent dissolution mechanisms are offered, and the acquisition of appropriate conditions that result in nanoscale roughness surfaces (avoiding the dominance of pitting and preferential grain removal) is firstly explored. Building on prior work, a piecewise approach is presented to analyse the acquired image stacks to map partial crystal orientations, while different approaches are proposed to account for jet-specific surface artefacts and waviness. This is repeated over 20 layers in an individual specimen and layer-wise orientation maps are used to construct volumetric models of the specimen. These data sets are then explored from the perspective of materials/manufacturing engineers, who may use to this information to effect advancements to materials processing technologies.

PRESCRIPTIVE MAINTENANCE SYNCHRONIZED TO THE CHARACTERISTICS OF INDUSTRY 5.0: A THEORETICAL DISCUSSION

This article aims to examine and understand the current scope of Prescriptive Maintenance integrated with the human-centric characteristics of Industry 5.0. Therefore, the main research question is the following: “In the view of the managers of the companies visited, located in the Southeast Region of Brazil, what are the main technical and administrative difficulties that companies encounter in successfully implementing proactive combinations on the factory floor, such as Prescriptive Maintenance integrated with the characteristics of Industry 5.0”?. The evolution of industrial processes in recent decades and the advent of Industry 4.0 integrated with human-centered characteristics have placed maintenance as one of the protagonists of industrial processes. In a scenario where automation and humans are gaining more and more space, the future of industries lies in Prescriptive Maintenance. To achieve the objective of the article, professional experiences from participation in congresses, seminars and technical visits to several factories located in the Southeast Region of Brazil are used. In data collection, we explored documentary observation and bibliographic research in the Science Direct and Web of Science databases. The study focuses on research areas related to maintenance integration, Industry 4.0 (Techno centric) and human-centric characteristics (Industry 5.0). The article brought important conclusions, as it addresses the impact and trends on the factory floor so that managers can develop competitiveness and productivity. The article highlights the role of maintenance methods in improving factory floor performance, emphasizing human knowledge and Prescriptive Maintenance practices to ensure operations within World Class standards.

Effects of the Human Presence among Robots in the ARIAC 2023 Industrial Automation Competition

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems.

3D Printing Systems Designed for the Factory Floor

Markforged is enabling more resilient and flexible supply chains with its 3D printers by bringing industrial production to the point of need. We caught up with Daniel Leong, senior product manager, to hear more

Trade law policing on the factory floor: next generation agreements and their corporate accountability tools

Trade law policing on the factory floor: next generation agreements and their corporate accountability tools

Considering the Factory Floor

ABSTRACT This article focuses on a professional space that technical and professional communication with which students might not be familiar: a factory. In unionized factory workplaces, particularly, the interactions between the factory floor workers (the unionized group) and the salaried professionals can be complicated, making effective communication difficult. From the perspective of one factory floor worker, we examine some of these complications and difficulties as a way to provide insight into such workspaces.

Managing safety of the human on the factory floor: a computer vision fusion approach

PurposeMaintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.Design/methodology/approachA computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.FindingsComputer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R2 is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.Originality/valueIn this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

Implementations of Digital Transformation and Digital Twins: Exploring the Factory of the Future

In the era of rapid technological advancement and evolving industrial landscapes, embracing the concept of the factory of the future (FoF) is crucial for companies seeking to optimize efficiency, enhance productivity, and stay sustainable. This case study explores the concept of the FoF and its role in driving the energy transition and digital transformation within the automotive sector. By embracing advancements in technology and innovation, these factories aim to establish a smart, sustainable, inclusive, and resilient growth framework. The shift towards hybrid and electric vehicles necessitates significant adjustments in vehicle components and production processes. To achieve this, the adoption of lighter materials becomes imperative, and new technologies such as additive manufacturing (AM) and artificial intelligence (AI) are being adopted, facilitating enhanced efficiency and innovation within the factory environment. An important aspect of this paradigm involves the development and utilization of a modular, affordable, safe human–robot interaction and highly performant intelligent robot. The introduction of this intelligent robot aims to foster a higher degree of automation and efficiency through collaborative human–robot environments on the factory floor and production lines, specifically tailored to the automotive sector. By combining the strengths of human and robotic capabilities, the future factory aims to revolutionize manufacturing processes, ultimately driving the automotive industry towards a more sustainable and technologically advanced future. This study explores the implementation of automation and the initial strides toward transitioning from Industry 4.0 to 5.0, focusing on three recognized, large, and automotive companies operating in the north of Portugal.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry.

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1 MB of sensor data per robot.

BOLD: Color from Test Tube to Textile

Abstract Red can be the color of passion and danger, or luck and prosperity. Blue can be the shade of sadness—singing the blues—or a sign of tranquility and serenity. As the Russian painter Wassily Kandinsky once affirmed, in his 1911 treatise Concerning the Spiritual in Art , “Color is a power which directly influences the soul.” Color is also culture: we’ve colored our environments, clothes, and bodies for thousands of years, since early humans applied simple organic stains derived from the plants around them. The art and science of dyeing is an ancient one; today it’s an economic and commercial powerhouse. The arrival of the first synthetic dyes in the 1850s—discovered by happenstance from experiments with coal tar waste—brought us a rainbow of new possibilities for fashion and textiles, from color “fads” and trends to new means for self-expression. But synthetic color’s dark side lingers in polluted waterways. A new exhibit at the museum of the Science History Institute explores these complex legacies. Bold: Color From Test Tube To Textile takes visitors on a colorful 150-year journey through the history of synthetic dyes, examining the people and places who’ve shaped our modern understanding of color—from the laboratory to the factory floor, from the runway to retail, and beyond.

Emphasizing worker identification with skills to increase helping and productivity in production: A field experiment

AbstractCan productivity improve if workers identify more with the skills they use in their work environment? This paper reports the results of an experimental design that was peer‐reviewed prior to collecting data. The research setting is a global manufacturer using a novel smartwatch‐based system for distributing work tasks among factory floor workers. Drawing on the concepts of identification and helping in organizations, we hypothesized that fostering workers' identification with their own skills could serve as a mechanism to enhance helping behavior on the factory floor, which should improve productivity. We designed a compound skill‐fostering treatment consisting of communication, meetings, and exercises regarding individual skills. We treat one large factory area for 2 weeks and keep a similar area in a sister factory as a control group for comparison in a difference‐in‐difference model. The results show that nudging skill identification increases workers' identification with skills, but we do not find evidence for increased helping behavior or increased productivity. Our results help develop theory around multiple sub‐identities and provide guidance for future studies seeking to enhance identification in organizations.

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms.

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

Self-Defense and the Surround in Finally Got the News (1970)

Abstract: Drums and DRUM, percussion and organization: these are prominent motifs in Finally Got the News (1970), the documentary that chronicles the activities of the League of Revolutionary Black Workers and their struggle against the exploitative labor practices of the Big Three automakers in late 1960s Detroit. One hears the drums immediately in Finally Got the News , playing over a montage of images that trace the history of exploitation and oppression of Black workers in the United States from the plantation to the factory floor. While Finally Got the News explicates the calculable dimensions of the history of racial capitalism in Detroit, positioning the DRUM and the League at the intersection of revolutionary Black nationalism and revolutionary Marxism, the beat of the drums during the film's opening montage signals another dimension of resistance against or over which these intersecting trajectories of social struggle play out, one that defines the limit of the dialectical narrativization of resistance and progress articulated in the film. Drawing on the work of Fred Moten and Stefano Harney, this essay argues that the beat of the goblet drums in the film's opening montage signals the presence of the blackness that surrounds the structures of racial capitalism in US-American society. It is against this backdrop, the essay argues, that Finally Got the News improvises and stages its own political intervention.

A New, Ultra-Low-Cost Power Quality and Energy Measurement Technology Bringing SmartGrid on the Factory and Automation Floor

This paper introduces a new application for Power Quality and Energy Monitoring: embedding power quality monitors inside sensitive industrial, commercial, automation, manufacturing and medical equipment – and bringing SmartGrid to the factory and automation floor. Using power quality monitors to solve intermittent problems has also been limited by the cost of monitors. In this case, the monitors are generally installed at the equipment terminals. So the monitors can either directly interface with the load, or they can interface via e-mail or WebServer with a remotely connected user. In addition the new technology also combines a high accuracy energy monitor that can precisely control loads based on the load profile. It also introduces IEC 61000-4-30 which is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce the same results when connected to the same signal. However, instruments that comply with the Class A requirements of this standard have, until now, been too expensive for common use. Now a new set of technologies developed by an American company, in cooperation with a Japanese company, demonstrate that it is possible to manufacture three-phase power quality instruments that are fully compliant with the Class A requirements of IEC 61000-4-30, and to do so at ultra-low-cost, allowing these monitoring devices to be used even at entry levels of individual loads.

Intelligent Assistant for Smart Factory Power Management

This article presents the development and implementation of an intelligent assistant for smart energy management in an industrial setting. The project, developed in collaboration with Bosch Termotecnologia Aveiro, aims to leverage the advancements of Industry 4.0 and address the increasing demands for efficiency, quality and sustainability in industrial processes. The intelligent assistant implements machine learning algorithms, specifically autoencoders, to predict energy consumption related variables and detect anomalies in data collected from the factory floor. In the models’ building process, an AutoML hyperparameter optimization tool is utilized to further enhance their predictive abilities. The implementation of the assistant showcases its capabilities not only regarding the predictions, but also in terms of data visualization and alarm generation. The results obtained through the use of Auto ML showcase remarkable improvements compared to traditional regression models and basic autoencoders, proving its effectiveness in terms of making accurate predictions, with improvements up to 90% in some indicators when compared to those basic models. Alsointerms of the anomaly detection task, the chosen methodology proves to be effective in detecting different kinds of anomalies. The integration of the assistant with a Grafana dashboard and the exploration of additional communication tools, such as a Telegram bot, further improve the user experience and expand the potential applications of the assistant.

IoT-Driven Innovations: A Case Study Experiment and Implications for Industry 5.0

This paper uses a thorough case study experiment to examine the real-world applications of IoT-driven innovations within the context of Industry 5.0. The factory floor has a temperature of 32.5°C, a warehouse humidity of 58%, and a safe pressure level of 102.3 kPa on the manufacturing line, according to an analysis of IoT sensor data. A 5.7% decrease in energy use was made possible by the data-driven strategy, as shown by the office's CO2 levels falling to 450 parts per million. The case study participants, who had a varied range of skills, were instrumental in the implementation of IoT, and the well-organized schedule guaranteed a smooth deployment. Key Industry 5.0 indicators, such as +2% in production efficiency, -5.7% in energy usage, -29% in quality control flaws, and +33.3% in inventory turnover, show significant gains. Key metrics evaluation, data-driven methodology, case study, Industry 5.0, IoT-driven innovations, and revolutionary potential are highlighted by these results.