Factory Shop Floor Research Articles

Flow shop manufacturing layout as low carbon approach for application in bonding wire drawing industry

Modern manufacturing environments are yet to fulfil the evergrowing competitive supply and demand due to the limitation of conventional production approaches. A low-carbon approach incorporating the change from process to product-centric manufacturing systems could conserve energy while fulfilling supply and demand requirements in a competitive market. Current study had conducted a case study in a bonding wire factory that operates on process-oriented system with job shop layout. Preliminary data collection had assessed the existing practices on the factory's shop floor, and simulation model using WITNESS software was developed using this data. A job shop to flow shop conversion framework was developed with the integration of factors such as shop floor layout, order release, and dispatch rule play crucial roles in the conversion process of the production system. Based on the application of the conversion framework encompassing six future production system models, CL-SPT model which uses flow shop as layout, CONLOAD as order release and shortest processing time as the dispatch rule is selected as the best performing and economical production system. This model enhanced the output and average flow time by 2.7% and 27.7%, respectively, in tandem with low-carbon requisites while retaining machine utilization performance equivalent to the conventional system.

Fault Diagnosis and Identification in AGVs System

This article describes a methodology for the diagnosis of failures in multi-AGV (Automatic Guided Vehicles). Today, AGVs are establishing themselves in the most advanced automatic logistics solutions, providing performance and safety that cannot be achieved with handling solutions with manual forklifts. Furthermore, thanks to the application of Industry 4.0 digital technologies, very advanced tools are available to monitor the performance and diagnose faults of fleets of AGV. In particular, studies on fault diagnosis have mainly focused on (1) the diagnosis of internal components of the automatic truck and (2) the identification of failures in the functionality of the AGV in its interaction with the surrounding environment. This paper shows an approach to fault diagnosis in multi-AGVs system, considering the interaction between each single AGV and the environment, with the scope to help the user increase the system efficiency in an existing layout. The objective of the paper is to introduce and discuss a methodology to study the failure and the available recovery actions of the AGV navigation system. Moreover, the paper presents the real AGV data acquisition and processing architecture actually deployed on the factory shop floor, as well as the result from the experimental study in a real industrial environment.

Becoming ‘farazat’: Re-examining feminisation from a Tunis used clothes sorting factory

In over 40 factories in Tunisia, female sorting workers transform container loads of imported Western used clothing into comparable value categories that are packaged for re-export or sale on the local market. The dominance of women on these factory shopfloors indicates the feminisation of sorting work, typically implying a process of devaluation of labour power. However, this article shows how feminisation has situated outcomes and meanings that are specific to a given labour process. Through an ethnographic account of a Tunis sorting factory, it argues firstly that feminisation cannot be understood separately from the particular production process of used clothes sorting, in which the heterogeneity of used commodities requires female sorters to engage in highly contingent practices of value creation. Situated knowledge is necessary to separate the valuable from the valueless, and to assemble new product categories. Second, the article holds that these hierarches of skill in the factory result in processes of gendered identity construction associated with the vernacular profession ‘farazat’ (pl. sorters). Derived from the activity of sorting, and used exclusively in its feminine form, the identity is collectively asserted to convey a sense of professional pride and authority. Despite the lack of formal recognition, this professional designation is then used to mobilise a language of ‘respect’ that positions the farazat as both ‘workers’ and ‘women’. Far from devaluation, the feminisation of sorting here opens possibilities for women to assert their indispensability to the production process and to challenge the formal bounds of factory work.

Radio Interference of Wireless Networks and the Impact of AR/VR Applications in Industrial Environments

The use of wireless communications systems on the factory shop floor is becoming an appealing solution with many advantages compared to cable-based solutions, including low cost, easy deployment, and flexibility. This, combined with the continuous growth of low-cost mobile devices, creates opportunities to develop innovative and powerful applications that, in many cases, rely on computing and memory-intensive algorithms and low-latency requirements. However, as the density of connected wireless devices increases, the spectral noise density rises, and, consequently, the radio interference between radio devices increase. In this paper, we discuss how the density of AR/VR mobile applications with high throughput and low latency affect industrial environments where other wireless devices use the same frequency channel. We also discuss how the growing number of these applications may have an impact on the radio interference of wireless networks. We present an agnostic methodology to assess the radio interferences between wireless communication systems on the factory floor by using appropriate radio and system models. Several interference scenarios are simulated between commonly used radio systems: Bluetooth, Wi-Fi, and WirelessHART, using SEAMCAT. For a 1% probability of interference and considering a criterion of C/I = 14 dB, the simulations on an 80 m × 80 m factory shop floor show that low-bandwidth systems, such as Bluetooth and WirelessHART, can coexist with high-bandwidth and low-latency AR/VR applications running on Wi-Fi mobile terminals if the number of 11 Wi-Fi access points and 80 mobile AR/VR devices transmitting simultaneously is not exceeded.

Smart factory floor safety monitoring using UWB sensor

Chemical asphyxiation at petrochemical factories can provoke the unconsciousness or death of factory workers through suffocation. Some chemicals vaporize and mix with air without showing any warning properties that raise the risk of oxygen deficiency. In light of this, Industry 5.0 focuses more on human-centricity than technology-driven implementations to ensure secured and work-friendly environments in industries. Recently, research on factory safety management dependent on the Internet of things (IoT) sensors have been executed unwaveringly. In this work, the ultra-wideband (UWB) sensor is adopted to recognize the motion and breathing pattern of workers in smart factory scenarios. After capturing the data from the UWB sensor in real-time, the proposed dataset is further inspected by the deep learning (DL) and traditional machine learning (ML) approaches. Twofold detection schemes are considered where the movement and vital patterns are distinguished first by the stacked ensemble (SE) and the long short-term memory (LSTM) frameworks. The Bayesian optimized ensemble learning (EL) and bidirectional (Bi-LSTM) models are further occupied to analyze abnormalities in the breathing rate of a worker in the smart shop floors. The investigated outcome shows that the DL frameworks (LSTM and Bi-LSTM) outperformed the others by acquiring 99.90% and 99.94% accuracy in 147 s and 293 s, respectively. The devised perception indicates prominent attainment to the smart factory shop floor, Internet of medical things (IoMT), the smart city paradigm, and e-health appliances.

Decentralized Holonic Control System Model for Line-less Mobile Assembly Systems

The Line-less Mobile Assembly System paradigm (short LMAS) provides necessitated flexibility, especially for large-scale products as customer demands for individualized products persist and product life-cycles remain short. To make LMAS advantages operationally usable in an industrial context, it requires a suitable control system to connect multi-purpose assembly resources and to autonomously configure transient assembly stations. Therefore this paper reviews the relevant literature to identify the necessary components of such architectures. Depending on the control system’s intention and use cases, the properties of the organizational paradigm, an adequate ontology and basic design patterns regarding the distribution and order-relationships of the system entities are to be defined conceptually. For the implementation, a role model, an interaction model, and a data model are required. Due to the utilization of mobile multipurpose resources and the possibility of factory shopfloor reconfiguration by transient stations, existing approaches do not meet LMAS inherent properties. Consequently, we present a suitable decentralized multi-agent control system approach.

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain.

An Augmented Reality Framework for Visualization of Internet of Things Data for Process Supervision in Factory Shop-Floor

Modern industrial environments provide a high level of digitalization, enabling great volumes of information to be used for shopfloor monitoring and control purposes. In cases where human operators participate in the production process, it is important to support operator’s intervention with Internet of Things (IoT) data and empower process supervision with constant information over Key Performance Indicators (KPIs). Towards this end, the proposed framework exploits Augmented Reality (AR) for the provision of an IoT data visualization solution to support KPIs based process supervision in the smart factory environment. The solution utilizes IoT-enabled components, in order to receive dynamic information over the status of sensors and resources spread across the factory shop-floor. The AR application facilitates the need for visualization of information available on the network, on the actual shopfloor components, which offer transparency and ease the supervision of production processes. The development of the AR and cloud API has been based on CSharp and Java, in a reusable framework that can be reused for similar industrial cases. The reconfigurability and reuse of the AR solution is supported by a template configuration, allowing industry practitioners to configure application features and external connections without any additional software development. The solution was validated on a thermoforming process supervision case of a white goods production system. The main goal was to use the AR framework to support operators during the process parameters design phase, and dynamic supervision of the shopfloor environment. It is demonstrated that the proposed system can be used for KPIs monitoring of the production cycles and for empowering process supervision and optimization.

Link4Smart: A New Framework for Smart Manufacturing Linking Industry 4.0 Relevant Technologies

Smart Manufacturing (SM) epitomises the idea of connected, technology and data-driven factories that can provide the high levels of innovativeness and responsiveness required in today’s fiercely competitive and unpredictable global market. With numerous SM-driven initiatives worldwide such as Industry 4.0 (I4.0), Made in China 2025 etc., SM developments have been fuelled by technological advances such as Cloud Computing, Big Data, Cyber-Physical Systems and the Internet of Things (IoT). This paper analyses the role of such technologies in SM and how they can be put into a coherent framework for the implementation of SM solutions, termed Link4Smart. This is illustrated with the Link4Smart factory concept, where horizontal integration (across the factory shop floor) and vertical integration (from machine to Engineering Resource Planning). With self-aware, self-predict, self-compare, self-configure, self-maintain and self-organized Link4Smart Machine Tools, smart manufacturing systems can change, update and/or adapt to daily production challenges.

Innovative design on the shop floor of the Saint-Nazaire Airbus factory

In this study, we examine innovative design practices on the Saint-Nazaire Airbus factory shop floor. The engineering and manufacturing engineering departments are in charge of the design of products and their industrialization, even though the factory is usually seen as a place for manufacturing, rather than design. However, there is also design activity in a factory that is devoted to the optimization of manufacturing processes. In this study, we highlight an alternative form of design that relies on a collective exploratory approach. A total of 30 projects from the Saint-Nazaire Airbus factory were selected and analyzed. Of these, two were selected as case studies to illustrate the factory’s different design methods. Subsequently, quantitative analysis provided evidence of the existence of two design regimes: closed prescription and expandable prescription. The resulting solutions were examined, and it was found that designs under the expandable prescription regime provided more robust long-term solutions. This study offers new perspectives for reexamining innovation in manufacturing and exploring design activity on factory shop floors.

Cognitive analytics platform with AI solutions for anomaly detection

This work presents a cognitive analytics platform for anomaly detection which is capable to handle, analyze and exploit resourcefully machine data from a shop-floor of factory, so as to support the emerging and growing needs of manufacturing industry. The introduced system contributes to industrial digitization and creation of smart factories by providing a generic platform which is a complete solution supporting standards-based factory connectivity, data management, various AI models training and comparisons, live predictions and real-time visualizations. The proposed system is built on a micro-service architecture, in order to be extendable and adaptive over time, and contains three core modules, the Data Acquisition, the Knowledge Management and the Predictive maintenance, which contribute to machine failure prediction and activate predictive maintenance procedures, to efficient production schemes and decision making, to monitor anomalies and handle unforeseen conditions, to predict future behaviours on time series etc. The proposed platform utilizes continuous re-training mechanisms enabling a self learning approach for the delivery of AI solutions, usable also for various production data, guaranteeing the quality of results without continuous monitoring and human-resources allocation for AI models’ retraining. This cognitive platform is supported by machine learning techniques and deep learning architectures in order to achieve the desired performance in the management of factory processes and needs. All the information generated by the proposed platform is provided to the end user through a user interface that utilizes advanced visualization techniques.

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

THE UPGRADED ROLE OF THE VALUE STREAM DESIGN FOR REDESIGNING THE FACTORY LAYOUT

The case study shows the re-optimization of an initial new factory layout design with Value Stream Design (VSD). The VSD is a quantitative method and its’ final goal is to make a waste free optimized material flow. The primary goal of arrangement is to reduce transportation distances and frequencies, optimize human load. Initially the whole factory shop floor layout design was already made in push concept. The plans were made by production management, logistics, engineering department at the headquarter of the multinational automotive company with based on VDI2870 holistic concept linking strategy on tactics and operation. On the layout (v1.) the hundreds of machines were placed and arranged by CAD (Computer Design) engineers to fit the space. The factory building has 15,000 m2 with empty shop floor waiting for the final decisions for equipment. The factory production area was shared into six main production areas (P1-P6), which correlates with their product complexity of the product families. Each production area output can be finished product (FP) or semi-finished product (SFP) for the next production areas. To validate the whole factory layout it was necessary to involve lean experts that identified disadvantages and constraints. Without lean implementation the company’s transportation waste would be 49% more per year. The Value Stream Design importance nowadays is upgrading to a higher level, when the whole global business is changed, the labor force fluctuates, and the cost and delivery time reduction plays a vital role in the company’s profit and future. The research shows that if the decision taking is based on real data and facts the controlling and management can do its best in time. Using VSD and re-evaluating the transportation routes, frequency and costs is the first step to define a smooth, low cost, material flow (v2.). This development ensured the company to drive from push to pull production through mixed production system. Originally, the production flow was clockwise orientation. It was changed step by step to mixed production by eliminating work in process storages, implementing FIFO lanes, Milk Run, and Kanban. The total annual transportation distances were reduced from 4,905,000 m between the rump-up and serial production period. The warehouse storage size was reduced to 50% and implementation cost from €75,000 to €32,500. By eliminating work in process storages along production lines it was possible to open a new two way transportation road that also will serve the AGV’s operations in industry 4.0 projects. Due to decreased lead time the logistic labor productivity increased by 45%. Besides taking measurements for the VSD it was used Value Stream Mapping as a lean tool and an own designed VSD evaluation and a simulation software. The VSD team’s cooperative actions reduced the evaluation and validation time with 65% then it was initially planned. The implementations were evaluated from the rump-up phase to the first serial productions and the results were confirmed by controlling and management

Lead Time Reduction for managing Engineering Changes

Engineering changes need to be done in a product because of design improvement, product and process optimization, government regulations, etc. Implementation of those changes in the production shop floor involves multiple stakeholders and functions within and outside the company. Engineering change management (ECM) is the process of communication between all stakeholders to implement and document the engineering change on the shop floor. This involvement of multiple stakeholders and responsibilities increases ECM lead time and creates a challenge for change implementation on the shop floor. This paper presents such a case of engineering changes for factory shop floor implementation. Causes like delay in decisions behind an increase in lead time have been investigated. Process automation solutions that can reduce the ECM lead time drastically have been proposed and implemented. The developed solution for tool selection and layout problems achieves almost a 66% reduction in lead time and helps the factory for process standardization and resource optimization.

Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence

Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.

‘Are they out to get us?’ Power and the ‘recognition’ of the subject through a ‘lean’ work regime

Critical studies of ‘lean’ work regimes have tended to focus on the factory shop floor or public and healthcare sectors, despite its recent revival and wider deployment in neoliberal service economies. This paper investigates the politics of the workplace in a United Kingdom automotive dealership group subject to an intervention inspired by lean methods. We develop Foucauldian studies of governmentality by addressing lean as a technology of power deployed to act on the conduct of workers, examining how they debunk, distance themselves from and enact its imperatives. Our findings support critiques of lean work regimes that raise concerns about work intensification and poor worker health. Discourses of professional autonomy allow workers to distance themselves from lean prescriptions, yet they are reaffirmed in their actions. More significantly, we illustrate the exercise of a more encompassing form of power, showing how lean harnesses the inherently exploitable desire for recognition among hitherto marginalised workers, and its role as a form of ‘human capital’. The paper contributes to critical studies of lean by illustrating its subtle, deleterious and persistent effects within the analytical frame of neoliberal governmentality. We also demonstrate how studies of governmentality can be advanced through the analysis of contested social relations on the ground, highlighting the ethico-political potential of Foucauldian work.

Development of a Smart Gateway for a Label Loom Machine using Industrial IoT Technologies

Constant search for efficiency and productivity has led to innovation on the factory shop floor, representing an evolution of the current production systems combined with new technologies of industrial automation and information technology. This work presents a versatile gateway for experimental demonstration of Industrial IoT technologies in a loom machine, allowing sensing, monitoring and data acquisition that was not originally available. We have implemented an approach, based on the OPC UA communication protocol for real time applications, and OPC UA to MQTT conversion mechanism. Raspberry Pi’s platform act as an OPC UA server. From the measurements, data stored in a cloud server can be accessed remotely with improved security and visualized from a computer dashboard. One of the conclusions that can be drawn is that the proposed gateway allows data to be stored and easily monitored from a smartphone application or a computer web interface.

AUTOMATIC ERGONOMIC POSTURAL RISK MONITORING ON THE FACTORY SHOPFLOOR ‒THE ERGOSENTINEL TOOL

The enabling technologies of the Industry 4.0 program can support the smart factory of the future to face the challenges related to their sustainable growth. In particular, given the progressive ageing of the population, it is mandatory to develop systems able to preserve operators’ wellbeing and to prevent the incidence of work-related musculoskeletal disorders. By exploiting a recently introduced low-cost sensor we developed and validated a reliable prototype for automatic assessment of ergonomic postural risk in the factory shopfloor. Encouraged by the results of the validation process, we enhanced the prototype functionalities. The tool will serve both as a monitoring system for the evaluation of postural risk and a training system for increasing operators’ awareness. In this paper, we describe the design of the prototype and the enhanced functionalities of the final version, ‒ the ErgoSentinel.

An Intelligent Simulation based case study of Indian Micro Small Medium Enterprise (MSME) of farm equipment manufacturing

The manufacturing field is the area where application of simulation is an essential tool for validating the methods and architecture before applying them to factory shop floor. In this paper an Indian Micro Small Medium Enterprise (MSME) is taken into consideration, which manufactures agricultural equipment’s with aim to mechanise the Indian farming techniques. Presented case study is authentic and adopted for Gahir Agro Industries situated in agricultural hub, state of Punjab. Proposed company is all set to launch a new farm equipment called ‘Laser Land Leveller’ which got approval from governing body and ready for its batch production because of seasonal demand. Present study encapsulates the modelling and simulation as tool for analysis of manufacturing process of laser land leveller and its predicted production levels under actual working conditions. Tool used for modelling and simulation is ARENA 15.1 by Rockwell Automation Studio.

Augmented reality system to guide operators in the setup of die cutters

This paper describes an Augmented Reality system for the improvement of the manufacturing process in the packaging sector. It presents a successful use case of how to integrate the Augmented Reality technology in a factory shop floor by providing a tool that helps operators in their daily work. Given a product reference, the proposed system digitizes the setting of the die cutter automatically from an image and stores it in a database to subsequently consult and analyze. Furthermore, the content display is not carried out as a conventional Augmented Reality system (wearable devices such as glasses or mobile), but projecting directly on the workspace to facilitate its interpretation. Compared to the current workflow, where the data is recorded on sheets of paper and stored physically in warehouses, the proposed system offers several advantages such as preventing data loss, reducing costs, or the possibility of increasing knowledge from the post-processing of digitized data.