Factory Environment Research Articles

A Preliminary Study on Improving Thermal Comfort of Traditional Factories via Forced Ventilation in Taiwan

In Taiwan, the indoor environment of factories can be classified into precisely controlled environments, austere environments, and eclectic environments. Compared with the precisely controlled environments of the electronics/medicine industries, eclectic environments of such traditional industries as footwear, textiles, or light metal processing generally use forced ventilation instead of HVAC systems to improve indoor thermal comfort. In this study, the design strategies of negative pressure fans in a large-span factory were studied, and 0.6-3.0 m/s was defined as the effective wind speed range via thermal comfort index for evaluating improvement. We first carried out an experimental study to determine an effective distance of fans, and the results were used to validate simulation via computational fluid dynamics. A simulation study was then carried out to study the design strategies of fan installation in factories with 30 m spans. The results showed that the simulation model fit the experimental data well in the first stage and that 15 m was the best distance for installing an auxiliary fan in all cases with various inlet wind velocities. In the simulation studies, the results demonstrated that the effective wind percentage can be improved 40%~60% through proper design, which provides significant potential for energy-saving and thermal comfort in traditional factories.

Implementation of a Prototype Production Line based on concept of Industrial Digitalization in an existing Learning Factory environment

Implementation of a Prototype Production Line based on concept of Industrial Digitalization in an existing Learning Factory environment

Procedure for sustainable product development in the learning factory environment

Procedure for sustainable product development in the learning factory environment

A Cybersecurity Training Concept for Cyber-physical Manufacturing Systems

Networked manufacturing systems based on traditional Industrial Control Systems (ICS) are particularly exposed to cyberattacks. Mostly based on Programmable Logic Controllers (PLCs), they have been designed to isolate manufacturing environments without connection to cyber-infrastructures such as cloud, connectivity with offices, and other manufacturing sites. Hence, low cybersecurity protection levels make such ICS interesting for malicious cyber-attackers. Thus, resilience to such attacks through cybersecurity has become one of the most significant concerns of Industry 4.0. While the technical aspects of cybersecurity for manufacturing systems are increasingly researched, there are few published works on effective methods to train cybersecurity for manufacturing systems. This paper proposes a cybersecurity training concept that builds on teaching cybersecurity in an interactive role-based approach within a teaching factory environment. Both attack and defense strategies can be explored, and student learning performance evaluated. The concept has been implemented to show its feasibility and potential to outperform traditional classroom-based training methods.

ES-SECS/GEM: An Efficient Security Mechanism for SECS/GEM Communications

Industry 4.0, as a driving force, is making massive achievements, notably in the manufacturing sector, where all key components engaged in the production processes are being digitally interconnected. However, when combined with enhanced automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity renders industrial systems more vulnerable to cyberattacks. Cyberattacks may have a variety of different impacts and goals, but they always have negative repercussions for manufacturers. These repercussions include financial losses, disruption of supply chains, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. SECS/GEM is mainly designed to be utilized in a trusted, controlled, and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight, as SECS/GEM is completely devoid of security features. This research proposes ES-SECS/GEM, an Efficient Security mechanism that provides authentication, integrity, and protection against cyberattacks. The proposed mechanism is compared to other security mechanisms in terms of processing time, control overhead, and resilience against cyber-attacks. The ES-SECS/GEM demonstrated promising results, suggesting that it allowed SECS/GEM devices to only connect with authorized industrial equipment, maintained message integrity, discarded forged messages, and prevented cyberattacks on SECS/GEM communications. In terms of processing time and control, ES-SECS/GEM likewise outperformed other mechanisms and incurred the lowest values for these metrics.

Interdisciplinary Communication Training in a Learning Factory Environment to Enforce the Digital Transformation in Producing SMEs

Interdisciplinary Communication Training in a Learning Factory Environment to Enforce the Digital Transformation in Producing SMEs

Queue models Evaluation of Simulation for Managerial Decision Support Systems: Application of Two-Stage Production Control

Managers benefit from information systems in decision-making processes. A number of cognitive tools are included in decision support systems. When these tools are examined, it is seen that most of them produce statistical solutions. However, in some cases, statistical solution approaches cannot produce exact results. At this point, using simulation methods can give effective results in making managerial decisions. In order to benefit effectively from simulation methods in decision support systems and to obtain results that can support decisions, it is necessary to define the system with all its dimensions and features. In this study, queuing models simulation, which is one of the information tools that strengthen management decisions, is introduced and a two-stage production control application, which is possible to be made in a factory environment selected as the application environment, is introduced by using GPSS macros, which is a system simulation package.

Agnostic learning for packing machine stoppage prediction in smart factories

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as "agnostic". In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

SafeFac: Video-based smart safety monitoring for preventing industrial work accidents

This work presents SafeFac, an intelligent camera-based system for managing the safety of factory environments. In SafeFac a set of cameras installed on the assembly line are used to capture images of workers that approach the machinery under hazardous situations to alert system managers and halt the line if needed. Given a challenging set of practical application-level requirements such as multi-camera support and low response latency, SafeFac exploits a YOLOv3-based light-weight human object detection. To address the latency–accuracy tradeoff, SafeFac incorporates a set of algorithms as pre- and post-processing modules and a novel adaptive camera scheduling scheme.Our evaluation with a video dataset containing more that 113,000 frames from real assembly line activity shows that SafeFac achieves high precision (99.93%) and recall (96.44%), and SafeFac successfully satisfies such challenging requirements as a ready-for-deployment system for safe factory management.

Boosting productivity with tech

Boosting productivity with tech

Research on path planning and scheduling of multi AGVs based on a two-way dual path map

With the development of intralogistics technology in factories, multi-AGV scheduling is still an important problem that needs to be solved by the control system. Aiming at the path planning and scheduling problem of automated guided vehicle (AGV) in the factory loading and unloading work, this paper designs a two-way dual path map construction method suitable for the factory, introduces a two-way different weight map construction method suitable for the right path, and uses “simulated road conditions” algorithm to calculate the shorter path, takes the shortest driving distance as the optimization goal to carry out the task scheduling analyzed possible conflicts and gives corresponding solutions. To verify the correctness of the map construction and algorithm, the multi-AGV simulation is carried out by using a robot operating system (ROS). The results show that the proposed method can realize the stable operation of multi-AGV in the factory environment without conflict and collision.

Design of Orbital Mobile Robot System for Electroplating Production Line

For different electroplating processes, there are many automatic electroplating production lines at present, but there is still no long-distance electroplating production line for small batches and multiple varieties of plated parts. Therefore, this paper proposes a set of orbital mobile robot systems to improve the production efficiency and quality of electroplating and reduce the uncertainty of manual control. The hardware of the system is composed of an orbital car and a robotic arm. The orbital car includes mobile positioning and a holding brake mechanism and the robotic arm adopts KUKA kr20-r2100 industrial robot. According to the logic of moving and grabbing, the control framework of the system is determined. A lot of field experiments show that the designed system can adapt to the factory environment and automatically complete the scheduling tasks assigned by the system.

An Analysis of The Issues and Challenges Related to The Status of Child Labour in India

Children are considered to be the group in society that are most vulnerable to abuse and exploitation. He is viewed as the man's father. We ensure the future of the country by securing his present and ensuring that he has access to the fundamental freedoms required for growth and development. More than 10 million children in India are employed as child labourers in both industrial and domestic settings. These children are vulnerable to workplace physical and psychological abuse as well as working in dangerous factory environments for little to no pay. The kid labourer lacks the education, health, and strength necessary for their development. Such pursuits prevent the growth of the mind and personality. Lack of income for families, migration, social situations, etc. forces a youngster to be made to labour under abusive circumstances. The government has a responsibility to recognise the terrible condition of the child and to take action to protect their fundamental human rights. The implementation of constitutional protections, proper enactment of laws, the involvement of the judiciary and the public at large, as well as recognition of the abhorrent conditions of child labourers' workplaces, have all contributed to efforts to alleviate their predicament.

Sensor Effects in LCL-Type Grid-Connected Shunt Active Filters Control Using Higher-Order Sliding Mode Control Techniques.

The effects of measuring devices/sensors on improving the power quality (PQ) of electric networks are studied in this paper. In this context, improving the performance of an LCL-type grid connected to a three-phase three-wire shunt active filter (SAF) in the presence of voltage perturbations is studied. In order to ensure the high-quality performance of LCL-SAF in the presence of voltage perturbations, the robust continuous second-order sliding mode controller (2-SMC), including twisting and super-twisting controllers, and continuous higher-order sliding mode controller (C-HOSMC)-based approaches are employed. These controllers, whose outputs are processed by pulse-width modulation (PWM), allow minimization of the phase shift and prevent the generation of discontinuous chattering commands, which can severely damage the VSI components. Moreover, an integration of a generalized instantaneous power identification algorithm with an advanced phase locked loop (PLL) was proposed and experimentally tested to validate the effective performances of SAF under severe perturbations. Additionally, the studied approaches were tested via simulations taking into account a conventional nonlinear industrial load in a real textile factory environment, using measurements provided by power quality analyzers. Finally, the effects of the measuring devices, including the current and voltage sensors, on the accuracy and reliability of the SAF and, consequently, on the PQ of the electric power grid were studied via simulations and experimentally. The results of this study support the validity of the recently published patent.

Analysis of Beyond 5G Integrated Communication and Ranging Services Under Indoor 3-D mmWave Stochastic Channels

5G-and-beyond (B5G) networks are moving toward the higher end of the millimeter-wave (mmWave) spectrum (i.e., from 25 to 100 GHz) to support integrated communications and ranging (ICAR) services in next-generation factory deployments. The ICAR services in factory deployments require extreme bandwidth/capacity and large ranging coverage, which a mmWave-B5G system can fulfill using massive multi-input and multioutput (mMIMO), beamforming, and advanced ranging techniques. However, as mmWave signal propagation is sensitive to harsh channel conditions experienced in typical indoor factory environments, there is a growing interest in the realistic mmWave indoor channel modeling to evaluate the practical scope of the mmWave-B5G systems. In this article, we study and implement a 3-D stochastic channel model using the baseline third-generation partnership project model. Our channel model employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions. Using the generated CIR, we present the performance analysis of an mmWave-B5G system in terms of power delay profile, path loss, communication and ranging coverage, and mMIMO channel capacity.

Simulation Study of a Smart Factory Lighting and Shading System

Insufficient and uneven light distribution in the workplace affect the quality of work that requires high precision. In addition, inadequate lighting not only affects the health of workers but is also linked with a condition called 'Sick Building Syndrome'. The artificial light in factories is commonly manually controlled, resulting in energy wastage. Hence this paper presents a smart lighting and shading system to create both comfort lighting and energy-efficient factory environment. Three-dimensional modelling simulations were built using Tinkercad and Revit. The control system built utilizes the motorized skylight solar shades and the artificial high LED Bay, which able to adapt with the fluctuating factory lighting condition. If the light enters the factory through the motorized shade on the factory ceiling is insufficient or uneven, the artificial light will be switched on. The implementation of both mechanisms managed to improve the light distribution evenness and keep the light intensity level in the factory in the range between 885 lux and 1306 Lux from 9am to 6pm, while from 6pm onwards at average of about 900 lux. Furthermore, the electrical energy consumption is reduced significantly during the daytime as the control system has utilized the sunlight as part of the light source for the factory. The smart system can save as much as 22.39%, or 55.2128kWh from 9am to 7pm. In conclusion, the smart lighting and shading control system has proven the effectiveness theoretically, and the objectives were achieved.

Beer Safety: New Challenges and Future Trends within Craft and Large-Scale Production.

The presence of physical, chemical, or microbiological contaminants in beer represents a broad and worthy problem with potential implications for human health. The expansion of beer types makes it more and more appreciated for the sensorial properties and health benefits of fermentation and functional ingredients, leading to significant consumed quantities. Contaminant sources are the raw materials, risks that may occur in the production processes (poor sanitation, incorrect pasteurisation), the factory environment (air pollution), or inadequate (ethanol) consumption. We evaluated the presence of these contaminants in different beer types. This review covers publications that discuss the presence of bacteria (Lactobacillus, Pediococcus), yeasts (Saccharomyces, Candida), moulds (Fusarium, Aspergillus), mycotoxins, heavy metals, biogenic amines, and micro- and nano-plastic in beer products, ending with a discussion regarding the identified gaps in current risk reduction or elimination strategies.

Analysis of OM-Based Literature Reviews on Facility Layout Planning

This work consists of a systematized review of the state of the art of reviews for the problem of Facility Layout Planning (FLP) within the Operations Management (OM) field to support the decisions taken for the improvement of the manufacturing and logistics in a factory environment. The first phase begins by defining the search strategies for obtaining the scientific literature, for which we used ten databases. With these, a base of 112 articles was obtained, but after the systematized process was reduced to 32 directly related articles. In the second phase, we executed a Dimensional analysis of these literature review articles employing a quantitative analysis of the sections and subsections of the selected articles. The third phase comprises the identification of gaps and future research lines. Finally, the conclusions obtained from the systematized review process are presented.

Industry 4.0 Innovations in Construction

The dawn of the Fourth Industrial Revolution, better known as Industry 4.0, is set to enable global networks of machines and equipment in an environment of smart factories, capable of instantly exchanging information in an independent manner and having the Internet of Things and Cyber-Physical Systems as backbone for an autonomous operation. Great gains in productivity and flexibility are expected, which tend to intensify global competitiveness, promote social changes and economic development, besides stimulating new academic works, such as the proposal of models capable of identifying the maturity level of an organization in this context. Therefore, the present work proposes a maturity model to measure the maturity level of civil construction companies regarding the adoption of technologies and practices of Industry 4.0. Based on a systematic literature review, the proposed model was built from existing maturity models and their characteristics and relevant components. The model is supported by a elaborated questionnaire of twenty-three questions considering four dimensions (Technology, Organization, People and Sustainability) which was submitted to seven different companies in the city of Manaus/Brazil. The results after the practical application of the model showed that most companies had a low level of adoption of Industry 4.0 principles and there is plenty of room for improvement.

Evaluation of PV Panel Power Loss Using Gabor Filter Bank

Photovoltaic panels are subject to thermomechanical stresses during their production and subsequent life stages. These conditions give rise to cracks and other defects in panels that can affect power output. Cell cracking is one of the most important causes of power loss in photovoltaic panels. Therefore, photovoltaic panels and cells need to be monitored to achieve the maximum output during production and further downstream. Electroluminescent imaging is a powerful and established technique consisting of many electrically connected solar cells arranged on a grid, and it is employed in order for assessing the quality of photovoltaic panels. In this study, the detection of photovoltaic panel defects in electroluminescent images was examined through image processing methods. PV panels can consist of different numbers of cells. Performance evaluation is made on a cell-based and whole module basis. In PV panel production, unlike EL images taken in standard environments in the factory environment, EL images taken under field conditions require preprocessing before actually being processed. Features were extracted from the preprocessed EL images by exploiting Gabor filter. The obtained features were evaluated as cell-based and the stability of the cells was determined. The performance of the panel was calculated according to the power loss of the cells of the panel. When the calculated performance values were compared with the power values obtained by I-V measurement, the highest error was found to be 0.059, the lowest error was 0.004, and the average error was 0.0213. As a result, the highest success rate was 99.99%.