Occupational Behaviour Study in the Retail Sector

Metaverse, Asia Pacific Academy of Science Pte. Ltd. (APACSCI), 2024

Artificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.

Procedia Computer Science, 2021

The increasing complexity of adapting established assembly processes to fast changing market demands is challenging European industry. Especially for highly individual products the automation of each assembly step is not feasible for both technical and economic reasons. Humans and machines have to work cooperatively in future factories. Like new programming methods for machines, human workers have to be trained for such changed situations. Therefore, this paper presents challenges and lessons learned from a 4-year research project dealing with the reduction of training effort for assembly processes by researching easily configurable, digital assistive systems. These digital assistive systems arranged on a novel 'human centered workplace' range from product-specific work instructions shown on a display and augmented reality solutions for training to collaborative robots. The overall architecture comprises a fully integrated software ecosystem for engineering and operating assistive systems, a prototypical assembly station as well as a corresponding transformation process.

2019

Empowered by machine learning and artificial intelligence innovations, IoT devices have become a leading driver of digital transformation. A promising approach are augmented intelligence solutions which seek to enhance human performance in complex tasks. However, there are no turn-key solutions for developing and implementing such systems. One possible avenue is to complement multipurpose hardware with flexible AI solutions which are adapted to a given task. We illustrate the bottom-up development of a machine learning backend for an augmented intelligence system in the manufacturing sector. A wearable device equipped with highly sensitive sensors is paired with a deep convolutional neural network to monitor connector systems assembly processes in realtime. Our initial study yields promising results in an experimental environment. While this establishes the feasibility of the suggested approach, further evaluations in more complex test cases and ultimately, in a real-world assembly process have to be performed.

Applied Sciences

Due to increasing competition in the global market and to meet the need for rapid changes in product variability, it is necessary to introduce self-configurable and smart solutions within the entire process chain, including manual assembly to ensure the more efficient and ergonomic performance of the manual assembly process. This paper presents a smart assembly system including newly developed smart manual assembly workstation controlled by a smart algorithm. The smart assembly workstation is self-configurable according to the anthropometry of the individual worker, the complexity of the assembly process, the product characteristics, and the product structure. The results obtained by a case study show that is possible to organize manual assembly process with rapid adaptation of the smart assembly system to new products and workers characteristics, to achieve ergonomic working conditions through Digital Human Modelling (DHM), to minimize assembly time, and to prevent error during the...

Journal of emerging technologies and innovative research, 2020

There are levels or revolutions that have taken place in the field of manufacturing, and currently we are in the fourth industrial revolution or Industry 4.0. Industry 4.0 is possible because of Internet of Things (IoT), cyber-physical systems (CPS) and Internet of Systems which together interact and help build a smart factory. Automation and smart decision-making are key characteristics of this revolution. In the early stages of Industry 4.0, various applications of Artificial Intelligence (AI) and Machine Learning (ML) enabling automation in the real world can be seen. Over the coming years, it is expected that the extent of automation will only increase and thus can replace repetitive jobs. The nature of job that a human does will most definitely change. In this era of industrialization, it is very important for humans to understand their role, where do they exactly stand in this industry automation for efficient working of the systems. This paper aims to reflect the role of human intelligence and experience in decision-making which cannot be fulfilled in this era of industrialization. The organizations and governments also have a huge role to play in this industry automation which has been stated in the paper for an in-depth understanding. Human-technology interactions are also explained in this paper to show how the combination of two enables a better working environment. In the end, a broader picture of the future is given in terms of industrial revolution and human involvement to equip the reader with an idea of the upcoming scenario and get a better understanding as a whole.

Gathering structures faces reliably stunning, dynamic and each so regularly even violent practice. Promising a response to boundless the collectible and new issues with hoarding, AI is altogether examined through scientists and masters the same. Regardless, the area is huge and, in any case, astounding which gives a check and an obstacle alarming tremendous application. This paper contributes in giving a diagram of open AI procedures and masterminding this to two or three confirmation speechless quarter. A great feature is laid on the customary favored capacity, and tests of beneficial bundles in an aggregating environmental factor. Realities guaranteeing about, gathering and taking care of at last wraps up being consistently moderate and utilizing AI checks possible inside the subject of social occasion. Anyway, the way that battlefront machine mechanical congregations are stacked down with sensors, the records' choices are hard to survey early. The pre confirmation forestalls the need to explore all chances while not forbidding smart amusement plans. Hoarding is the place the utilization of AI are regularly gainful. Very one AI strategy and looks at bundles wherein they need been effectively dispatched.

2018 13th APCA International Conference on Control and Soft Computing (CONTROLO), 2018

Despite the existence of various solutions in the industrial domain for cooperation between robots and humans, they tend to focus mainly on safety issues with very few advances in the adaptation of industrial equipment to the characteristics of the operator and his way of working. For several years, adaptation in a human-robot collaboration environment was single sided, as only the operator adapts his working operations facing the robot characteristics, which leads to high levels of stress and fatigue of the human operator. Nowadays, the paradigm is changing towards the adaptation of human operator to industrial equipment and vise versa. The adaptation of a robot to the human is achieved by enabling the machine to learn the physical and psychological characteristics of each operator, in order to create a working profile for each individual. Thus, the main objective is to analyze the relationship between human operators and robots in an industrial environment, and therefore explore human-machine collaboration by correlating sensorial data from all the entities involved in the process. With this in mind, by performing sensor fusion and data analysis representing actions and biometric signals from the human operator, industrial robots will be empowered of self-adaptation capabilities. In this dissertation, an industrial collaborative environment is achieved using a Cyber-Physical Production System (CPPS). This CPPS consists in three main parts, namely sensing and actuating equipment, logical entities called Smart Components and a Cloud infrastructure. Sensing devices are based on biometric sensors-BITalino's ECG and EDA-and a vision system-Kinect-in order to monitor the human operator working profile. A robotic arm is used as actuating device. Each equipment is virtualized into an agent-based representation, based on the Smart Component concept, which communicate sensor data with a Cloud infrastructure responsible for data processing and decision making. Sensor data is analyzed in order to infer levels of stress and fatigue through a fuzzy logic system. Decision making is based on the MAPE-K architecture, enabling the robotic arm self-adaptation. Results from human subject tests are presented here to validate the proposed methodology, proving that the system can detect stress with an accuracy of 77,6% and fatigue with an accuracy of 70%, as well as detect the subject's position and movement with a true positive rate of 70,7%. Facing the movement and levels of stress and fatigue of the human operator, the robotic arm should be able to change autonomously it's task execution, namely speed of its movement and the correct operation according to the habits of the operator. i ii First of all I would like to thank Professor Gil Manuel Gonçalves, my supervisor, for the guidance and follow-up of the work done, for all the suggestions and corrections, and for giving me the basis for this work to be possible. The biggest thanks to Rui Pinto and João Reis, for being the co-supervisors every student hopes for and even more, for guiding me and helping me in every occasion with the greatest patience in the world and doing all that with a good sense of humor. Without you, this dissertation would not be half of what it is.

Zenodo (CERN European Organization for Nuclear Research), 2022

Recent advances in AI, above all machine and deep learning, have brought about unprecedented possibilities in automation, prediction and problem solving with impact on operators and their way of working and interacting with automation on the shop floor. While the expected effects are focusing on increasing the efficiency, flexibility, and productivity of operations in the industrial and service sector, there is justified scepticism towards its implementation due also to the challenge of integrating AI into operator's current way of working and practices in a way that actually supports also the human in the loop. Therefore, it is now time to consider the user's side from an employees' point of view in order to foster AI in a human-technology relationship. The present paper is exploring the preliminary steps taken in this direction while trying to identify a problem definition and its suitable solutions for, firstly, improving the human automation interaction and, secondly, reduce the time variability and improve efficiency in a milling process for large metal metal components of a wind turbine at a manufacturing facility. To complement this description, a data analysis of the manufacturing process status is provided. The analysed data sets contain general information of relevant parameters of the manufacturing system as well as the required inputs from the operators. The purpose of this report is to establish the basis on which a thorough operational description of the overall man-automation process is defined and the usefulness of including a better integration for the manual tasks in it. The operational description of the tasks is a key ingredient to achieve better requirements specifications and how we can enhance the human performance of the operators by increasing their situational awareness on the shop floor. Moreover this task mapping can account of a lot of missing information regarding variability of execution time in the process and to support scheduling of manual activities for the operator to perform while the automated task may not need direct supervision.

Procedia CIRP, 2020

In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

DYNA, 2021

Artificial intelligence is already a pulsating reality in society, and all major companies are trying to apply its use to their products. In this context, the manufacturing sector could not be left behind. The industry has more and more alternatives to choose from when it comes to implementing artificial intelligence techniques. In this aspect, Machine Learning can be of great use when it comes to solving the challenges that arise in manufacturing. Therefore, this article presents a brief context of Machine Learning, followed by its current applications in the manufacturing sector together with a historical evolutionary analysis of the publications related to Machine Learning in the manufacturing sector. It ends with some conclusions regarding the current state of Machine Learning in the scientific community and in companies. Keywords: Machine Learning, Review, Manufacturing.