Shop Floor Research Articles

DIGITALIZATION OF SHOP FLOOR MANAGEMENT AT SELECTED SEGMENT IN THE INDUSTRIAL ENTERPRISE IN THE CONTEXT OF INDUSTRY 4.0

As businesses gradually transition to the concept of Industry 4.0, they strive to digitize all necessary data. The aim of this article is to demonstrate how the digitalization of shop floor management (dSFM) can enhance the efficiency of production process management, and to propose steps for faster and more effective implementation of dSFM in industrial enterprises. The digitalization of shop floor management increases agility, responsiveness, and overall improves decision-making processes. In the analyzed enterprise, the traditional shop floor management was overly burdensome, accompanied by excessive documentation and inefficient processes. It was necessary to analyze three areas: data collection, information visualization, and the flow of shop floor meetings. Based on this analysis, shortcomings were identified, and measures were proposed to automate routine activities and streamline processes. This case study thus fills a gap in the research by focusing on specific challenges in implementing dSFM in an industrial enterprise.

Analysis and optimization of the efficient press shop operation

Abstract. In the last decade, stamping and forming manufacturing methods have been widely used in the manufacturing industry, and the stamping and forming industry has gradually shifted from the competition of scale and quantity to the competition of high quality, cost-effectiveness, and multi-category. Therefore, it is of far-reaching significance to study the efficient operation method of the press shop. In this paper, the efficient operation modeling method, digital simulation platform, and production line improvement of the press shop are reviewed. According to analysis, it can be concluded that the dynamic data-driven modeling method is perfect, but the theoretical basis involved in the practical application process is too complicated, therefore, press shop managers may be required to have a certain degree of theoretical basis so that the method can be effectively realized. Besides, the virtual-real combination of digital simulation platforms is easy to operate in the press shop, but the model assumptions analyzed in this paper are too idealized, and potential unresolved problems may arise in the practical application of the press shop with a larger scope and wider coverage. Finally, the production-based transformation of the shop floor production line is a more suitable management method for older factories that are ready for upgrading.

Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review

The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization of equipment idle time. In this context, this study aims to characterize the current knowledge about the challenges of the biopharmaceutical industry in the application of prescriptive maintenance, which derives from predictive maintenance, in the context of I4.0. To achieve this, a systematic review of the literature was carried out in the scientific knowledge bases IEEE Xplore, Scopus, Web of Science, Science Direct, and Google Scholar, considering works such as Reviews, Article Research, and Conference Abstracts published between 2018 and 2023. The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. There are significant advances in this area including the implementation of more elaborate algorithms used in artificial intelligence neural networks, the advancement of the use of decision support systems as well as the collection of data in a more structured and intelligent way. It is concluded that for the adoption of prescriptive maintenance in the pharmaceutical industry, issues such as the definition of data entry and analysis methods, interoperability between “shop floor” and corporate systems, as well as the integration of technologies existing in the world, must be considered for I4.0.

Remanufacturing implementation on the shop floor – Insights from an industrial perspective

Remanufacturing implementation on the shop floor – Insights from an industrial perspective

Should I Evaluate My Augmented Reality System in an Industrial Environment? Investigating the Effects of Classroom and Shop Floor Settings on Guided Assembly.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

Negotiating Plastics: De-mythicizing Individual Responsibility and Plastic Materialities in Japan, from the 1940s to the 1970s

ABSTRACT Japan is the second-largest producer of plastic waste. The government has implemented policies that encourage individual citizens to recycle and reduce plastic products to promote sustainability. This study into the history of women’s relationship with plastics de-naturalises two assumptions embedded in such environmental policies: firstly, the idea that it is the responsibility of individuals to combat the proliferation of plastics and pollution, and secondly, the notion of ‘plastics’ as a singular element to be accepted or rejected. Based on archival research of articles published in the Asahi shimbun and women’s magazines from the 1940s to the 1970s, this article chronicles changing themes in the depiction of women – plastics relationships. The history of women that emerged from the analysis shows that during this period women exercised their power collectively to negotiate the introduction of various plastic elements into the household. Women collectively determined what could be sold as a plastic product, changed the industry and shop floor practices, and influenced law and government policies regarding plastic waste disposal. The analysis reveals that such collective activities played a role in shaping a diverse history of plastics that is often lost in contemporary discourses of a single ‘plastic’ element and individual responsibility.

Real-time rescheduling for smart shop floors: an integrated method

Real-time rescheduling for smart shop floors: an integrated method

Prozessüberwachung durch bildverarbeitende KI

Abstract The proceeding automation of industrial manufacturing processes has reduced human presence on modern shop floors. Since industrial processes are still afflicted with malfunctioning, autonomous monitoring solutions are demanded. This article presents a forming use case in which AI-based image recognition is used to monitor process behaviour and prompt stops when needed. The knowledge base utilized by the AI is obtained by examining descriptive examples of abnormal process behaviour.

MXene anchored fabric for room-temperature operated ammonia sensing

Low-cost and fast-response fabric sensors are highly desirable for shop floor worker daily monitoring and early trace ammonia leak detection. However, the difficulty is how to establish a high bonding capacity between the active material and the fabric to meet the needs of sensing performance on practical applications. Herein, MXene anchored fabric sensors have been fabricated by spray casting technology and the impacts of the preparation process on sensor performance have been investigated. When the casting concentration of MXene is 10 mg/mL and the fabric is spandex, the ammonia sensor exhibits the best sensing performance with a response time of 0.62 s for 100 ppm ammonia and a low limit of detection of 0.4 ppm. Notably, when the sensor used for detection ammonia in wafer cleaner volatiles, the responsivity was up to 96.61 %. This research offers an efficient route for developing MXene anchored fabric for room-temperature operated ammonia sensing.

The role of digital technologies in the circular transition of the textile sector

In recent years, the Circular Economy has gained significant traction, driven by increased awareness among governments, companies, and consumers about the imperative to optimize resource consumption. The textile industry, acknowledged as one of the most resource-intensive sectors, is now actively adopting circular practices to supersede traditional linear strategies. These circular practices may cover all the aspects of the product lifecycle from the design and production usage to the end of life, aiming to value as much as possible the resources used. In parallel, the fourth industrial revolution reshapes manufacturing companies by integrating vital enabling technologies on the shop floor, enhancing operational efficiency alongside existing information systems. Nevertheless, exploring how these technologies may facilitate the circular transition of the textile industry has been performed in a scattered manner by some authors, and a comprehensive picture covering the entire product lifecycle opportunities still needs to be included. To address this gap, the authors analyzed 20 academic articles published till November 2023, retrieved from the Scopus database. The findings from the literature provided the use of digital technologies to implement the transition of a circular economy within the textile industry. The framework is developed to integrate existing studies, assisting textile industries in leveraging digital transformation for a circular transition across product lifecycle management. Finally, this study highlights future research directions that could offer valuable insights to practitioners and researchers.

A study on road noise reduction using recycled material

Recently, there has been a shift in the paradigm of the automobile industry from internal combustion engines to electric vehicles. An NVH research has also evolved rapidly from studying noise and vibration in conventional internal combustion engines to exploring methods for reducing road noise. A road noise during vehicle operation can be amplified by the resonance of the cavity inside chassis. As a countermeasure, so, filler materials have been applied inside the chassis to reduce resonance, However, fillers applied to existing cavities, such as pads and polyurethane foam, generally focus on blocking resonance inside the cavity rather than reducing vehicle road noise. In this study, a filler has been developed to fill the cavities of vehicles; Filler resources are from residual scrap gathered in manufacturing shop floor of automobile interior and exterior parts. As a result, fillers introduced in the study offer not only excellent acoustic performance but also are recyclable. The effect in terms of acoustic performance of recycled fillers was confirmed by conducting tests, sound transmission loss in reverberation and anechoic chambers. Also, actual vehicle driving tests were conducted on a proving ground.

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor

AbstractArtificial intelligence (AI) among other digital technologies promise to deliver the next level of process efficiency of manufacturing systems. Although these solutions such as machine learning (ML) based condition monitoring and quality inspection are becoming popular, these work under very limited conditions. Solutions do not scale-up in the real environment, where there is a mix of manufacturing equipment, where the quality and quantity of data available changes from machine to machine, or where the process changes, changing the distribution of data (i.e. concept drift). This is particularly challenging in highly reconfigurable and flexible environments. Having to develop machine learning models from scratch every single time is not cost-effective, time-consuming, requires expert knowledge that is typically not available in the manufacturing environment as well as can be challenging when data is not available in high volumes. Model robustness, reusability, adaptability and life cycle management are the keys to scale-up this technology in the manufacturing industry. In this work, a conceptual framework to enable simple and robust ML model development for the shop floor is introduced. Referred here as Frugal Industrial AI, the approach takes advantage of existing models and their context to build more robust ones in a data-efficient manner. Using a semantic knowledge base of how to construct these models for different manufacturing applications and semi-automating the development or reuse of solutions through semantic similarity, it is demonstrated how models can be developed in a more streamlined way. In addition, it is demonstrated how capturing process context information is important for the effective reuse of existing models through continual learning. This is key to building more robust ML solutions that can deal with real changing manufacturing environments, avoiding retraining from scratch as well as enabling the non-expert to use AI effectively on the shop floor.

Manufacturing floor mapping and presence tracking with a physics-based game engine

The manufacturing shop floor exists in a dynamic environment with constantly changing components regarding position and features. Presence tracking on the manufacturing shop floor is essential to enhance effective operations and safety. Internet of Things technology solutions have been developed to track and monitor machine and operators’ movements and materials on the shop floor to enhance machine availability and worker safety and improve overall operational effectiveness. However, for SMMs with limited financial resources and infrastructure, there is a need for a financially friendly approach to shop floor presence tracking and monitoring. This work presents the use of Internet-of-things and physics-based engines to develop a scalable and customizable solution to track presence on a manufacturing floor. The paper aims to improve and promote the adoption of I-4.0 technologies and digital manufacturing by SMMs with the development of an affordable, repeatable, customizable, open-source solution. The article describes a methodology for mapping, modeling, monitoring, and reporting presence on a manufacturing floor using physics-based engines. The methodology describes mapping the manufacturing floor into zones, the design of the IoT solution, selecting and placing sensors, and presenting the data using dashboards and physics-based engines. The paper investigates the methodology by implementing a case study on the Interdisciplinary Center for Advanced Manufacturing Systems (ICAMS) manufacturing floor using the Unity3D game engine, open-source software, and off-the-shelf motion sensors.

Warfare and wind tunnel: engineers, physicists, and the evolution of combat aircraft 1914-1918

Abstract The British aircraft industry was transformed across the course of the First World War. Annual output grew exponentially, while by 1917-1918 the intensity of wartime research and development saw technologically advanced machines seeking air supremacy in all theatres of operation, most notably the Western Front. Capability had been inhibited by the absence of reliable, high performance power units, but ultimately production and procurement problems were resolved. This heroic narrative warrants qualification given the high ratio of poor to outstanding aircraft and the reality that in August 1914 Britain was by no means wholly unequipped to wage war in the air: the development programmes of the National Physical Laboratory and Royal Aircraft Factory, and the Royal Naval Air Service’s collaboration with science-based companies such as Short Brothers run counter to a popular belief that machines were still crudely built and militarily ineffective. There existed a working partnership between pioneer engineers and designers, schooled in applied science at metropolitan technical colleges, and graduate physicists and mathematicians encouraged by far-sighted dons to join a fledgling but fast-growing industry. That fusion of theory and practice, academic and industrial, was consolidated once British science’s human assets were mobilised to wage ‘industrial war’. Yes, aviation saw huge changes across the War, but the continuity is striking: before 1914 designers and air frame manufacturers were drawn from both the shop floor and the varsity, a wartime infusion of research scientists generated remarkable advances in aeronautics and in consequence a vital extension of Allied air power; and after 1918 companies survived retrenchment thanks to a durable combination of the entrepreneurial engineer and the experimental analyst.

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Dynamic workload balancing to part input sequencing for integrated scheduling decision-making of flexible manufacturing systems

Flexible manufacturing systems (FMSs) are crucial in contemporary manufacturing environments because they provide the required agility and adaptability to face volatile customer demands. Balancing workload among different work stations of an FMS is essential to ensure optimal resource utilization, minimize idle times, and improve overall system performance. This research is aimed to study the application of dynamic workload balancing to part input sequencing for integrated FMS scheduling decision-making. In this work, we propose an online algorithm that balances workload dynamically in FMSs. A mathematical model of FMS scheduling integration is presented. The proposed approach schedules tasks not only on machines but also on the robot responsible for part movement within a shop floor. The proposed approach is evaluated in two aspects of comparison and analysis. Data analysis is made by simulation experiment and statistical analysis. Conclusions with managerial implications for practical applications and future research suggestions are provided.

An adaptable Digital Twin model for manufacturing

In a smart manufacturing system, physical assets are often connected to their Digital Twins in the virtual world that composes a cyber-physical production system (CPPS). Digital Twin is synchronized with the physical asset. Digital Twin is therefore considered an enabling technology of smart manufacturing. In the process of developing Digital Twins, the Digital Twin model is of great significance. Many Digital Twin models have been proposed in the past decades, including geometry models, information models, and ontology models. However, there has not been an adaptable and generic Digital Twin model that can be used for various scenarios in a manufacturing system. This paper proposes an adaptable Digital Twin model based on the object-oriented concept. The proposed Digital Twin model can be applied to different situations using object-oriented concept features. Firstly, the base Digital Twin model is developed according to ISO 23247, including the essential attributes of a Digital Twin. The aggregation and composition mechanism of the proposed Digital Twin model is also described based on the object-oriented concept. After that, according to the categories of the physical assets on the shop floor, Digital Twin models of different physical assets are developed based on the base Digital Twin model, inheriting the features of the base Digital Twin model. In the end, a case study is carried out for verification on a machine tool in the Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS).

A federated information system framework for vertical integration

The convergence of operational and information technology in the era of Industry 4.0 has significantly increased the volume of data being generated on the manufacturing shop floors. Current research trends have focused on integrating this data with information present throughout the different systems in manufacturing facilities from Enterprise Resource Planning (ERP) to Manufacturing Execution System (MES) software, otherwise known as vertical integration. More specifically, previous works have looked at aggregating this data being read from Open Platform Communication Unified Architecture (OPC UA) servers by utilizing centralized aggregation servers to create information systems. This article, however, proposes a framework that adopts a federated information system designed to fulfill the manufacturing industry’s requirements and is capable of connecting to different OPC UA servers and storing the data within knowledge graph-based information models. GraphQL servers are also utilized in this system for easy accessibility of information by users. This system provides modularity, scalability, and interoperability to supplement the capabilities of information systems previously implemented. A prototype was also developed in this article for a federated information system for a boiler monitoring system.

Risk and Resistance: Ageing in the Charity Retail Sector

Abstract Background Charity shops have long been found to promote sociability, nurture experiences of belonging and act as spaces for community, caring and well-being. Older people remain likely to participate in this setting and charity shop volunteering is typically associated with events of positive ageing. However, the sector's growing emphasis on professionalism and profit raises questions about the potential impact of organisational change on this group. This research seeks to address this gap by exploring how older volunteers experience their roles within the evolving context of the modern UK charity shop. Methods This PhD study employed an ethnographic approach, involving 268 hours of immersive fieldwork across 3 charity shops in Greater Manchester. Ethnographic data was complemented by 17 semi-structured interviews with volunteers and store managers. Results Ageist discourses have been embedded into the sector, which work to reconstitute older volunteers as a type of ‘risky subject'. Such discourses manifest in unique ways on the charity shop floor and comprise of a range of practices, both subtle and overt, that are driven by the organisations focus on an intensified form of productivity. This prompts diverse forms of resistance from older volunteers, which are underpinned by the aim to not only counter negative perceptions of ageing but be just ‘visible’ enough in order to get by. Conclusion This paper unpacks the multifaceted nature of ageism in the charity retail sector. Whilst it is not as simple as suggesting that charity shops are solely characterised by ageism - especially considering the volunteer experience varies considerably across different contexts and places - age-related tensions are nonetheless more prevalent in the everyday. Older volunteers are not passive in the face of such change and continually work to avoid exclusion and remain integrated in this space in diverse ways.