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A punching test for simply estimating the tensile strength and total elongation of steel sheets and formed parts was proposed. The tensile strength and total elongation were estimated from the shear stress at the maximum punching load and percentage of the burnished depth at the sheared edge of the slug measured without cutting, respectively. For a variety of steel sheets with a range of the tensile strength from 360 to 1500 MPa, linear functions for the estimation were experimentally obtained. The correlation of the estimated tensile strength of the steel sheets with the measured one from the uniaxial tensile test was considerably high, and the correlation of the estimated total elongation was high. The distributions of tensile strength and total elongation for hot- and cold-stamped parts were estimated. The proposed punching test is available under not only a laboratory environment but also a factory environment.

In this work, the results of Ultra-Wideband air-to-ground measurements carried out in a real-world factory environment are presented and discussed. With intelligent industrial deployments in mind, we envision a scenario where the Unmanned Aerial Vehicle can be used as a supplementary tool for factory operation, optimization and control. Measurements address narrow band and wide band characterization of the wireless radio channel, and can be used for link budget calculation, interference studies and time dispersion assessment in real factories, without the usual limitation for both radio terminals to be close to ground. The measurements are performed at different locations and different heights over the 3.1-5.3 GHz band. Some fundamental propagation parameters values are determined vs. distance, height and propagation conditions. The measurements are complemented with, and compared to, conventional ground-to-ground measurements with the same setup. The conducted measurement campaign gives an insight for realizing wireless applications in smart connected factories, including UAV-assisted applications.

As manufacturing environments are getting increasingly decentralized, while the customer diversity of requirements is continuously growing, it becomes important for manufacturers to optimize comple...

Alguns autores têm associado o ambiente escolar aos das fábricas, relacionando a sirene que marca a troca de aulas como uma das principais características para a demonstração da proximidade do ambiente fabril com aquele empregado para o ensino-aprendizagem. Com o advento das Tecnologias de Informação e Comunicação esta característica pode ser modificada. Considerando-se a relevância do sinal escolar para a organização do ensino nos moldes atuais, o presente projeto tem por objetivo desenvolver um protótipo de sinal escolar interativo e de baixo custo para a reprodução de músicas escolhidas pelos estudantes para a delimitação do intervalo de suas aulas. Para a consecução deste propósito, levou-se a efeito uma pesquisa de natureza tecnológica, empregando-se a metodologia Design Science Research. A construção de um protótipo capaz de integrar modernas tecnologias com o objetivo de desenvolver um sinal escolar interativo pode agregar tecnologia e dinamismo a uma das mais tradicionais características das instituições de ensino atuais. Além disto, o emprego de tecnologias de baixo custo como o Raspberry PI torna a solução escalável, reduzindo as dificuldades para a sua adoção por outras instituições de ensino públicas e privadas.

Artificial Intelligence (AI) and especially machine learning (ML) become increasingly more frequently applicable in factory operations. This paper presents a systematic review of today’s applications of ML techniques in the factory environment. The utilization of ML methods related to manufacturing process planning and control, predictive maintenance, quality control, in situ process control and optimization, logistics, robotics, assistance and learning systems for shopfloor employees are being analyzed. Moreover, an overview of ML training concepts in learning factories is given. Furthermore, these concepts will be analyzed regarding the implemented ML method. Finally, research gaps are identified.

Within the development of a material, which forms a multifunctional coating on the threaded surface of tubes and joints, the authors identified the requirements specified for a coating. One of the main requirements is the possibility to perform the repetitive screwing of threaded connections without thread damage. The necessity to obtain a high level of anti-friction properties of a coating aroused the study of the influence of anti-friction additives of various effect on the tribological properties of a coating: the decrease in the friction coefficient of conjugate surfaces and ensuring the abrasion resistance of a coating. To identify the influence of anti-friction additives of various effects on the functional properties of a coating, the authors carried out the comparative laboratory tests of thread coatings containing various anti-friction additives under the sliding friction at the reciprocation according to the “fixed plate - finger” scheme. As the anti-friction components, the authors used graphite, molybdenum disulfide, and polytetrafluoroethylene (PTFE), which have different anti-friction properties. The tests were carried out for the coatings containing the complex of additives as well: graphite - PTFE, molybdenum disulfide - PTFE. The authors evaluated the tribological properties of the resulting coatings according to their friction coefficient value and abrasion resistance. To select optimum concentrations of anti-friction components, the authors tested the materials containing the complex of additives with different concentrations of graphite and molybdenum disulfide. To confirm the results of laboratory tests, the authors carried out full-scale tests in the factory environment when applying the selected coating to the threaded surface of a tube and a joint. The research identified the positive effect of the multipurpose utilization of anti-friction components. The authors determined the systems of anti-friction components (graphite - PTFE and molybdenum disulfide - PTFE) and their optimum concentrations (graphite and molybdenum disulfide - 8 wt. %, PTFE - 6 wt. %) in the thread coating material. The full-scale tests in the factory environment confirmed the results of laboratory tests.

In this paper, using the time-series throughput data generated from a simulated factory scenario, we study throughput prediction using convolutional neural network (CNN). Different with image or numerical recognition using CNN, in which the distribution of the prediction target during training stage usually has the similar level, the distribution of the throughput data concentrates only on several values. This centralized distribution may degrade the prediction accuracy. Therefore, we will propose a new CNN prediction method employing target vectorization which can mitigate the centralization of distribution. This method makes training process of CNN hold more possibility and improves the prediction accuracy of the throughput.

Modern factory planning requires a holistic perspective taking economic as well as environmental sustainability over the entire factory life cycle into account. As a complex socio-technical system, the factory life cycle consists of multiple life cycles of the inherent factory elements. A holistic understanding of the individual life cycles and their interdependencies is missing for both planning and operation of a factory. Therefore, the goal is to develop a system understanding about life cycle-oriented factory planning and to analyze the contribution of relevant factory elements to the sustainability of a factory. As a result, a knowledge base for life cycle costing and assessment of the entire factory is established using an impact path model. The qualitative model supports factory planners in deriving planning measures for the sustainable design of a factory and in determining data requirements for the quantitative evaluation of the economic and environmental sustainability of a factory. It shows that the production and logistics concepts essentially define the sustainability potential during planning, while the resulting life cycle behavior of the process facilities and workers is responsible for the majority of costs and environmental impacts of a factory. Factory planners must therefore become aware of the implications of planning decisions on factory operation when developing concepts in the future.

In the era of Industry 4.0, the vast development of Smart Factory is always followed by the advancement of Deep Learning technology. To avoid the smart factory system from unwanted losses because of defects in its output production in the steel factory, defect classification on steel sheets based on Deep Learning should be developed precisely. This paper explains how the Deep Learning technique was used to implement defect detection in a smart factory. For this study, we used an open dataset of steel defects. The result of the Deep Learning method for the defect detection system generates 96% accuracy, 0.95 recall, and a precision of 0.97 on the training process. This research goal may contribute to enhancing efficiency and cost reduction in the smart steel factory environment.

This paper investigates a framework for localization, mapping and path planning algorithm for mobile robots in indoor environments such as factory sites. For precise localization and mapping, this framework employs 3d lidar-based mapping and 2D mapbased localization algorithm. From the given 2D map, the desired path is optimized to minimize the travel distance and to satisfy the constraints of the mobile robot. In addition, this paper proposes a trajectory re-generation algorithm to avoid the dynamic or unknown obstacles including human operators or temporary structures in the indoor factory, which cannot be updated in the 2D map. Simulation results in a virtual factory environment show that our proposed framework can be reasonable solutions for indoor mobile robots.

Objective: Nnewi is a growing industrial town in Nigeria and the industries are sited very close to human habitation. Exposure to industrial effluents come with deleterious consequences. The purpose of this study was to investigate the liver function status of chickens exposed to these factory sites in Nnewi Nigeria. Materials and Methods: A total of twenty-nine (29) chicks were allowed to feed from the surrounding homeland of the factory until they aged between four (4) and five (5) months as adult birds (chickens) for the study. The chickens were sacrificed to obtain the blood for analyses. Five (5) ml of blood sample was collected from the heart into plain containers and the serum obtained was used for the estimation of biochemical parameters. Total protein (TP), albumin (ALB), total bilirubin (TB), direct bilirubin (DB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) were assayed using standard laboratory procedures. The data obtained was subjected to statistical analysis using Students t-test using Statistical Package for Social Sciences (SPSS) (version 16) software. Statistical significance was tested at p Results: The mean serum TP, ALB, TB and DB levels as well as ALP activity in chickens reared around factory sites were not significantly different when compared with the control chickens (p>0.05), however, the mean serum activities of AST and ALT were significantly elevated in the factory exposed chickens than those of the control chicken (p Conclusion: This study found a possible compromise between the liver enzymes and the factory environment which could result in poor growth of the chickens. It is recommended that factories be sited away from the reach of the chickens. Keywords: Industrialization, Factory site, Liver, Protein, Bilirubin, Aspartate aminotransferase, Alanine aminotransferase, Alkaline phosphatase, Chickens (Gallus gallus domestica), Nnewi.

This position paper by the DEIS HVDC Cable Systems Technical Committee provides a review of existing diagnostic electrical and dielectric techniques for testing the insulation of polymeric extruded HVDC cable joints in the present Part 1. Here, the state of the art on the insulation of HVDC extruded cable system joints is covered with reference to types, design and testing techniques. This helps to identify routine tests as the first target for the onset of new testing procedures, AC-PD measurements as the readily-available measurement from manufacturers' practices for quality control of the insulation of accessories during routine tests and VHF/UHF wireless sensors as the best tool for performing such measurements on joints in the noisy factory environment. Thereby, a novel protocol for the measurement of partial discharges using AC voltages and VHF/UHF sensors, for quality control during routine tests on such joints, is derived in the next Part 2. This protocol is the main novelty of this investigation. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Industry 4.0 demonstration factories show live demonstrations of use cases in a realistic factory environment. Usually, the use cases displayed in such an environment are uncoordinated in terms of integrity and variance of addressed research topics. This paper presents a classification approach for use cases to contribute to a more structured planning and coordination of demonstration factories. When applying this developed approach to individual demonstration factories, the analytical steps lead to an accumulated heat map, which supports practitioners in identifying both strengths and weaknesses of the individual demonstration factory for further planning. An exemplary application of the approach was conducted by analyzing the demonstration factory of the nationally funded project Future Work Lab.

Due to climate change and the resulting introduction of sustainability goals by the UN and federal governments, there is growing pressure on manufacturers to increase the sustainability of production systems. In this paper a new, sustainable production scheduling model for job-shop scheduling is developed. The model is optimized using an adjusted genetic algorithm (GA) to minimize energy-related cost (ERC). The proposed model includes multiple energy sources and incorporates a time-of-use (TOU) demand response (DR) scheme for all energy sources. Furthermore, it considers five machine operating modes to reflect different energy states of machines. This means that underutilized machines can be powered down to use less energy, thus reducing ERC. The model and algorithm are evaluated within the Energy-Technology and Application (ETA) research factory environment using a Python application that interfaces with other components to get information about the production system.

Implementation of the digitization and growth of the manufacturing processes intelligence are increasingly becoming necessity in today´s factories. Digitization and globalization are opening new market opportunities, in particular for small factories. The realization of the Digital Factory is the strategic goal of many factories for the next years. Previous researches been limited to a lack of information from small and medium-sized factories. Small and medium-sized factories play an important role in our and Europe´s economy. This paper investigates how to solve the digitization and the manufacturing and auxiliary systems designing in the factory with regard to the digitization and the dynamic market. This is very important step for creating of intelligent and autonomous transport systems. Described model is one of the most rapid ways to transform the classical (conventional) factory to modern Digital Factory. Propose model has many beneficial applications in real life of the factory environment. Achieved results offer vital evidence for possibility of the digitization in small and medium-sized factories. This approach has the real potential for increasing of the factories competitiveness.

Off-site construction aims to shift construction work to factory environment wherein enabling modular paneling to be automated. Whereas this approach has proven to be advantageous for the Canadian industry, the changes in the manufacturing processes of panelized walls bring new challenges and opportunities to the construction industry. Within such a controlled environment, inspection for safety and product quality can be automatized. A visual sensor can perform such duties for frame assemblies. This paper proposes a vision-based framework for automatic supervision of the light-gauge steel frame pre-manufacturing stage. The proposed framework is implemented as a Python-based program on the machine environment. The information extracted from an industrial camera placed on top of a steel framing machine prototype is compared with the manufacturing information available from the building information model (BIM) for each frame. The proposed framework is tested on several simulated and real scenarios to validate its accuracy and limitations. The results show this approach successfully identifies, validates and corrects, if needed, the frame assembly on its pre-manufacturing stage.

This paper presents an integrated robotic platform for advanced collaborative robots and demonstrates an application of multiple robots collaboratively transporting an object to different positions in a factory environment. The proposed platform integrates a drone, a mobile manipulator robot, and a dual-arm robot to work autonomously, while also collaborating with a human worker.

In this paper, we propose a real-time and low-drift localization method for lidar-equipped robot in indoor environments. State-of-the-art lidar localization research mostly uses a scan-to-scan method, which produces high drifts during the localization of the robot. It is not suitable for robots to operate indoors (such as factory environment) for a long term. Besides, the mapping and localization of this method are susceptible to the dynamic objects (such as pedestrians). To solve above problems, we propose the scan-to-submap matching method for real-time localization. Currently, this method has been used for building maps, and there are few studies to use it for localization, especially for real-time localization. In our research, we build the hardware and software platform for the scan-to-submap matching method. We extensively evaluate our approach with simulations and real-world tests. Compared with the scan-to-scan method, the results demonstrate that our approach can cope with the mapping and localization problem with high localization accuracy and low drift.

The emergence of Cronobacter as an important potential pathogen for newborn children and its occurrence in powdered infant formulae has generated a need to develop new management practices for this food group. This includes reduction of the prevalence of Cronobacter in manufacturing environments which can be a source of Cronobacter. This study was performed to assess the suitability of qualitative and quantitative Enterobacteriaceae and coliforms indicator tests for the presence and prevalence of Cronobacter. Environmental swabs (205) from five milk powder factories were examined. The qualitative indicator tests had good sensitivity but they lacked specificity for reliable routine use. Logistic regression analyses revealed a significant relationship between the quantitative indicator tests and Cronobacter prevalence, where the Enterobacteriaceae count was a slightly stronger predictor for Cronobacter than the coliforms count. The optimum test sensitivity (81%) and specificity (66%) was obtained when the indicator count thresholds were set at ≥1 cfu/cm2. However, since 11% of samples were Cronobacter positive when counts of Enterobacteriaceae and coliforms were less than 1 cfu/cm2, specific testing for Cronobacter is advised in addition to Enterobacteriaceae testing to minimise risk of transfer of Cronobacter from the factory environment into powdered infant formulae products.

The use of Whole genome sequencing (WGS) identified a multi-country outbreak of human listeriosis associated with consumption of frozen sweet corn produced in Hungary. The purpose of this report was to summarise information on the cases occurring in the UK which were part of this outbreak and outline investigations on the presence of Listeria monocytogenes in the affected food chain. Prior to the international recall of this product in 2018, 12 UK cases of listeriosis were identified as infected by the outbreak strain between 2015 and 18. Epidemiological and microbiological investigations confirmed these cases as belonging to the outbreak. A further case occurred in 2019 and a contaminated frozen pack from one of the implicated batches of sweet corn was recovered from the patient's domestic freezer. The outbreak strain was also detected in products from a sandwich manufacturer in 2018 which added frozen sweet corn directly to sandwich fillings. The sandwich manufacturer's sweet corn was supplied by a distributor in England which obtained frozen products from the Hungarian manufacturer implicated in the outbreak. Within the distributor's premises, 208 food and environmental samples were taken: L. monocytogenes was detected in 44% of 70 samples of frozen sweet corn and 5% of 79 other foods. The outbreak strain was detected in the frozen sweet corn, in one other frozen food (mixed vegetables) and in the factory environment. The outbreak strain was also recovered from frozen beans on retail sale in the first four months of 2019. Five other L. monocytogenes strains together with two other Listeria species were detected in samples from the importer's premises. One of the L. monocytogenes strains in the importer's factory, which was distinct from the outbreak strain, was also recovered from sweet corn collected from the sandwich manufacturer, sweet corn tested in England in 2013 and 2016 and the blood of two cases of human listeriosis which occurred in England in 2014. This report shows how analysis by WGS provides evidence to understand complex food chains. This report also highlights risks for transmission of human listeriosis from frozen sweet corn and the potential for misuse of this food as a ready-to-eat product.

Off-site construction aims to shift construction work to a factory environment, thereby enabling automation of the wood framing process. Current automated or semiautomated solutions for woo...

Digital Business-to-Business (B2B) platforms are increasingly playing an influential role in shaping business collaboration. Companies are relying more on such platforms to approach suppliers, customers, and service providers hence improving cross-sectoral collaboration and market reach. Digital platforms are including disruptive technologies such as the Internet of Things (IoT) as well as Blockchain to improve transparency and trust in business processes. However, notable challenges occur in integrating IoT information with product tracking information as well as storing them on Blockchain. This paper provides an implementation of integrating IoT-based product tracking with Blockchain in an open-source multi-sided B2B platform to address integration challenges. It focuses on product-tracking events and sensor data from the factory environment. A Blockchain service keeps cryptographic hash values for this information to avoid manipulation after the data storage.

The chapter presents a framework for establishing human-robot collaborative assembly in industrial environments. To achieve this, the chapter first reviews the subject state of the art and then addresses the challenges facing researchers. The chapter provides two examples of human-robot collaboration. The first is a scenario where a human is remotely connected to an industrial robot, and the second is where a human collaborates locally with a robot on a shop floor. The chapter focuses on the human-robot collaborative assembly of mechanical components, both on-site and remotely. It also addresses sustainability issues from the societal perspective. The main research objective is to develop safe and operator-friendly solutions for human-robot collaborative assembly within a dynamic factory environment. The presented framework is evaluated using defined scenarios of distant and local assembly operations when the experimental results show that the approach is capable of effectively performing human-robot collaborative assembly tasks.

As a consequence of the upcoming fourth Industrial Revolution, also known as Industry 4.0 (Industry 4.0), new disruptive technologies are being considered to be incorporated in the factory environment. One of these solutions are the Blockchain, which aims to integrate heterogeneous systems, manage commercial transactions and foster the assets’ traceability. Thus, this technology contributes to create a whole optimized supply chain that can impact in the global market. This chapter exposes the convergence of Blockchain and Industry 4.0. In this way, academical, commercial, and governmental initiatives are explored to present examples of what is currently being proposed, fostered and developed. In addition, a critical analysis covers the aspects of how these technologies can be synergically and efficiently integrated in a future society and industrial environment.

Compact lidars for aerosol monitoring applications were studied. One is a simple polarization lidar that detects near surface dust phenomena at meteorological stations. Another is a lidar for monitoring indoor particulates in a factory environment.

The automotive component manufacturing sector is experiencing fierce competitions. To enable improvements in production efficiency, the authors introduced single step injection forging as an alternative to conventional multisteps forging processes for manufacturing automobile fasteners, being enabled by a dedicated tool-design to achieve combined material-flows and hence, a complex component-form. To assist in this, a feasibility study was conducted, including comparisons of conventional multisteps forging with injection forging, through FE simulations, experimental validation of the injection forging process, as well as detailed examinations of the quality of the parts formed. The simulations were focused mainly on the forming of a wheel bolt. Axi-symmetric models were developed to analyse forging force and energy requirements, resulting forming-errors and tool stresses for each process. Injection forging tests were carried out in a factory environment with the aim of verifying the FE results and of confirming process and tool-design feasibility. Based on the results from these studies, the feasibility of replacing multisteps forging with injection forging was confirmed. It was established that injection forging may demand higher a forming force in its single step but it would consume less energy. Also, there is less chance of developing flow faults during injection forging, which is critical for the forming of the automotive fasteners. Nevertheless, due to the complex material-flow in injection forging and large die-deflections, a dedicated tool-design for compensating for forming-errors and for enhancing tool-life has to be enabled for the forging production applications.

The asbestos industry originated in the UK in the 1870s. By 1898, asbestos had many applications and was reported to be one of the four leading causes of severe occupational disease. In 1912, the UK government sponsored an experimental study that reported that exposure to asbestos produced no more than a modicum of pulmonary fibrosis in guinea pigs. In the 1930s, the newly established Medical Research Council, with assistance from industry, sponsored a study of the effects of exposing animals to asbestos by injection (intratracheal and subcutaneous) and by inhalation in the factory environment. Government reports, publications, and contemporary records obtained by legal discovery have been reviewed in the context of the stage of scientific development and the history of the times. Experimenters were engaged in a learning process during the 1912-1950 period, and their reports of the effects of asbestos were inconsistent. Pathologists who studied the effects of asbestos experimentally, at whole animal, tissue and cellular levels, advanced experimental methodology and mechanistic knowledge. In the hands of public relations experts, however, research was exploited to preserve an industry and perpetuate preventable diseases, a practice that continues to this day.

The increasing share of renewable energies in modern energy systems requires effective solutions to help harmonizing energy supply and demand. Fluctuating energy prices have been incentivizing customers, including manufacturing companies, to identify and build up technical solutions and abilities to profit financially from fluctuating energy prices. Therefore, energy flexibility has gained major attention in research and among industry practitioners. This paper overviews the topic of energy flexibility and discusses associated opportunities, risks and trends. Besides, an assessment methodology to identify potentials for energy flexibility in the factory environment is proposed. Furthermore, practical and conceptual examples to augment energy flexibility in the factory environment are given. In addition, a new concept for (energy) flexible factory environments is presented.

The unprecedented amount of bandwidth available at the millimeter-wave band brings new opportunities for the next-generation factory automation. By supporting these frequencies, the communication technologies may significantly improve the safety and efficiency of manufacturing processes. This paper presents channel measurement results at 28 GHz in a factory environment. The primary channel properties such as path loss, delay, and angular spread are evaluated. Additionally, the distribution of the cross-polarization ratio is shown. acceptedVersion

This paper presents a production scheduling system that optimizes operations of a modular factory. The proposed system consists of a database, a scheduling optimizer and an interface connecting two other components. A scheduling model for optimal schedules plays a key role in the scheduling system. In this research, we aim to develop the scheduling model using constraint programming, and design a data mart storing the related operational data. In particular, we set the modular factory environment to the hybrid flow shop and apply the constraint programming for scheduling. We provide a case study to test the performance of the proposed scheduling model with the synthesized dataset based on a modular factory environment in SmartFactoryKL.

This paper proposes a temperature and humidity monitoring system. The system was designed for monitoring and investigating the manufacturing environment in a tea factory based on the request of the factory’s owner. The system had been installed in the factory and the monitoring has already been done. We show distribution of temperatures and humidities in the factory collected by the system.

Health and safety regulations in many countries require workers at risk to wear safety shoes in a factory environment. These shoes are often heavy, rigid, and uncomfortable. Wearing safety shoes daily leads to foot problems, discomfort and fatigue, resulting also in the loss of numerous working days. Currently, knowledge of the biomechanical effects of insoles in safety shoes, during working activities, is very limited. Seventeen workers from a metalworking factory were selected and clinically examined for any foot conditions. Workers feet were 3D scanned, with regards to their plantar view, and the images used to design 34 custom-insoles, based on foot and safety shoe models. Three insoles were blind-tested by each worker: custom (CUS); prefabricated with the safety-shoe (PSS), and off-the-shelf (OTS). Foot-to-insole pressure distribution was measured in seven motor tasks replicating typical working activities: single and double-leg standing; weight lifting; stair ascending and descending; normal and fast walking. Wearing CUS within safety shoes resulted in a greater uniform pressure distribution across plantar regions for most of the working activities. Peak pressure at the forefoot during normal walking was the lowest in the custom insole (CUS 275.9±55.3kPa; OTS 332.7±75.5kPa; PSS 304.5±54.2kPa). Normal and fast walking were found to be the most demanding activities in terms of peak pressure. Wearing safety shoes results in high pedobarographic parameters in several foot regions. The use of custom insoles designed on the foot morphology helps decrease peak pressure and pressure-time integral compared to prefabricated featureless insoles.

Forging is one of the material deformation processing techniques. While the operation cycle is short and efficient, the machine setup process is generally long and time consuming. One of the difficulties in operation is position alignment procedure of the equipment during the die changing process. Due to the size and shape complexity of the machine and equipment, the operators are not able to clearly see the important parts – e.g. guided pins, holes, and sub-plate which need to be moved to finely adjust the alignment. Augmented Reality (henceforth AR) is used to solve this problem by rendering virtual objects and guides associated to those parts over the real equipment. Thus, the operators can monitor the position of the parts during the alignment process through the virtual objects. This AR system is developed based on ARcore SDK together with Unity on the Android platform. The experiment was conducted in real factory environment.

Based on the Internet of Things (IoT), the smart factory, aims to strengthen producibility and provide a system for manufacturing information management. The implementation of the smart factory usually raises worries regarding network reliability and cost. In this paper, a cost friendly and easily implemented smart factory solution is proposed and verified in a real factory environment, with China Mobile's NB-IoT network and OneNet PaaS platform. The measurement results proved its efficiency in typical smart factory use cases, and it is economical in both implementation and operation.

This paper presents a novel application of a perception sensor Leap Motion to a mobile tool cart (MTC). Daily-used instructions are exercised as commands to control the MTC in the factory environment by using finger/hand position detection of the Leap Motion. A type of differential-driven mobile cart with a tool basket which is mounted on the top of the mobile cart comprise the mobile tool cart. The commercialized Leap Motion sensor is employed to detect hand/finger gestures of the worker. In the same WiFi environment, the worker uses Leap Motion sensor which is equipped on the operation platform to give different remote commands, for example, Go Forward, Draw Back, Turn Left, Turn Right, Start/Stop, Acceleration and Deceleration to the MTC. We define a set of trajectory determined by the hand/finger commend of the worker to do the reflexes and the experiment result shows the validity of proposed method.

In assembly lines, deformation of the objects makes many efforts on automating the corresponding processes difficult. Mating of a wire harness onto a car body is among the representative tasks. Academically, this kind of problems are known as automatic manipulation of deformable linear objects. Previous technical approaches for solving the problem is characterized by employment of multiple contactless sensors and complex sensor based algorithms. These attempts are verified to be feasible only for simplified environments. The dependence of the approaches on real-time measurement to the state of the deformable targets limits their application in practical factory environment. In this paper, it is proposed to utilize wire tracing operation in recognizing the wire harness for the purpose of automatic mating. This method is inspired from the behaviors of humans demonstrated when they deal with similar problems. The proposed method is implemented and verified in a robot system. The feasibility and effectiveness demonstrated in the preliminary experiments show the potential of the proposed method for substantially simplifying the wire recognition problems which is thought to be crucial for practices in factory environments. HighlightsA new method for realizing automatic wire harness mating is proposed. In this method, recognition to the wire harness is proposed to be achieved by using a gripping which can move along the wire harness.The proposed solution does not necessitate complex sensing means and algorithms thus its extension has the potential to be applied in factory environment.The proposed approach has been implemented using three arms robot manipulator.Preliminary experiments are verifying the feasibility of the proposed approach.

Remanufacturing has attracted a lot of attention as the best form of resource utilisation for used products and the important way of energy saving and emission reduction. In this article, a version of production planning problems with remanufacturing and back-ordering is discussed, in which there are multiple factories in a cooperative relationship to produce new products, or remanufactured ones, or both. And all products are transported to different demand sites, but back-ordering is allowable. For this planning problem, three different models are proposed. The first model is a basic model, the objective of which is to maximise the total profit of all factories without considering the individual profit of each factory. The second model introduces the individual profit constraints of each factory, so as to maximise the total profit on the basis of achieving the individual profit matching with the production scale of each factory. The third model allows a kind of appropriate compromise form between the total profit and the individual profit. Then, the solution approach for these models is designed based on self-adaptive genetic algorithm with population division SAGA-PD. Finally, a numerical example is suggested to demonstrate the applicability and effectiveness of the proposed models and solution approach, and solution results of three models are compared and analysed.

The aim of this study was to determine the potential spoilage agent in hot smoked dry sausages among lactic acid bacteria (LAB). A total of 87 Vysocina sausages obtained from eight producers in the Czech Republic were analysed. The number of LAB ranged from 2.00 to 7.88 log CFU/g. Weissella viridescens was found in 30 of the 42 samples positive for LAB (71.4%), in 23 cases (53.5%) as a monoculture of LAB. An analysis of the factory environment and raw materials (84 swab samples) for the presence of LAB was conducted in relation to three producers. The species W viridescens was found in just 2.4% of all swabs. As total of four samples designated for reworking contained LAB, with three sample being positive for the presence of W viridescens, we suggest that the reworking process leads to subsequent growth of thermoresistant strains causing spoilage of the sausages.

This paper is about automatically guided vehicle (AGV) system in the automated-storage-and-retrieval-system (ASRS). In ASRS, it usually uses AGV system to transport materials, because it not only efficient but can cost down logistic cost. However, the major problem of the application about AGV is how to find the position of the pallets due to the difficulties to locating the pallet position on a complicated factory environment. In this work, Haar like-based Adaboost scheme with adaptive structure feature of pallets algorithm to detect pallets is presented, and by combining direction weighted overlapping (DWO) ratio, it can avoid those non-optimal candidates in object tracking. The experimental result shows this method can remove most of the non-stationary background and can increase the average pallet detection rate by 95%.

This paper presents a two-objective mixed binary integer linear programming model and a search routine solution method is proposed using a service factory environment with multi-processor workstations and a constant daily workload, for employee scheduling, number of machines per station, and makespan minimisation objectives. The search routine is simple enough to be implemented by managers using readily available spreadsheet programs. Solution of the four-station service factory formulation yielded results for improvement in the makespan of the shop. This search routine can be used by management in streamlining and optimising the service factory production environment as exemplified in the four-station case, and could also be applied to multi-processor flow shops.

The paper presents design principles of multi-agent system “Smart Factory” for real time resource management in manufacturing workshops. This objective is achieved by flexible event-driven reaction on changes in factory environment, resource allocation, scheduling, optimization and controlling in real time. The developed method of adaptive resource scheduling based on multi-agent technology, main functionality and architecture of the system are discussed. The results of application and measured benefits for aircraft jet engines manufacturer are presented.

In this study, we propose a method to extract features for graph SLAM using an upward-looking camera mounted in an automated guided vehicle (AGV) in a factory environment. To this end, a novel feature detector and descriptor GALS (geometrical arrangement based on lamps and SURF) is proposed in this study. This algorithm consists of two parts: 1) geometrical arrangement of SURF features around a lamp that is often observed in a factory environment; and 2) a SURF descriptor. The use of the geometrical arrangement of several SURF features provides a more distinguishable feature than the use of a single SURF feature, and thus it can be robustly used for feature matching. As a result of GALS-based graph SLAM, a graph is created and optimized through a Georgia Tech Smoothing and Mapping Library (GTSAM) tool so that the accumulated error due to the slip is minimized. A series of experiments in indoor factory environments showed that the proposed scheme resulted in reliable navigation.

Bioreactors are essential enabling technologies for the translation of advanced therapies medicinal products from the research field towards a successful clinical application. In order to speed up the translation and the spread of novel tissue engineering products into the clinical routine, tissue engineering bioreactors should evolve from laboratory prototypes towards industrialized products. In this work, we thus challenged the industrialization process of a novel technological platform, based on an established research prototype of perfusion bioreactor, following a GMP-driven approach. We describe how the combination of scientific background, intellectual property, start-up factory environment, wise industrial advice in the biomedical field, design, and regulatory consultancy allowed us to turn a previously validated prototype technology into an industrial product suitable for serial production with improved replicability and user-friendliness. The solutions implemented enhanced aesthetics, ergonomics, handling, and safety of the bioreactor, and they allowed compliance with the fundamental requirements in terms of traceability, reproducibility, efficiency, and safety of the manufacturing process of advanced therapies medicinal products. The result is an automated incubator-compatible device, housing 12 disposable independent perfusion chambers for seeding and culture of any perfusable tissue. We validated the cell seeding process of the industrialized bioreactor by means of the Design of Experiment approach, whilst the effectiveness of perfusion culture was evaluated in the context of bone tissue engineering.

In this paper, a novel wavelet-based approach to the detection of defects in grey-level texture images is proposed. This new approach (system) explores specific properties of the discrete wavelet transform (DWT), evaluates the statistical analysis results associated with well-defined and task-oriented subsets of DWT spectral coefficients, and generates defect detection criteria which, in their turn, evaluate many-sided nature of potential defects in texture images and leave space for controlling the risk, i.e. for controlling the percentage of false positives and/or false negatives in a particular class of texture images. The experimental results demonstrating the use of the proposed system for the visual inspection of ceramic tiles, obtained from the real factory environment, and textile fabric scraps are also presented. DOI: http://dx.doi.org/10.5755/j01.itc.45.2.12654

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM2.5 and PM2.5-10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m(3) for PM2.5 and 331.36, 190.01, and 184.60 μg/m(3) for PM2.5-10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM2.5-10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM2.5-10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.

This paper presents MicroFactory – a simplified version of the Robot@Factory competition. This version of the competition was conceived to be low-cost and easily implementable in a small space, be it a classroom or the school robotics club. The factory scenario size was originally 3.5m by 2.5m. The floor is now an A0 printed sheet and the warehouses and machines dimensions are so that they can be 3D printed or made out of LEGOTM bricks. Both machines and parts had active elements with leds; now they are passive. Robot@Factory is a Portuguese robotic competition whose first edition was held in 2011 in Lisbon. The scenario of the competition simulates a factory which has an Incoming Warehouse, an Outgoing Warehouse, and 8 processing machines. The robots must collect, transport and position the materials, self-localize and navigate while avoiding collisions with walls, obstacles and other robots. There are three rounds of increasing complexity: • In the first round the robot has to take 5 parts directly from the Incoming Warehouse to the Outgoing Warehouse, as fast as possible. • In the second round some of the five parts present in the Incoming Warehouse must be placed in a machine for processing; after the completion of this operation they can be carried into the Outgoing Warehouse. • In the third round, there are two types of machines (type A and type B). Before being taken to the Outgoing Warehouse some of the parts should be processed sequentially, first by a machine of type A, then by a machine of type B; some should be processed by a machine of type B; the other parts can be taken directly. During this round the teams can use two robots simultaneously. Robot@Factory poses challenges like dynamic task scheduling, robot cooperation, trajectory planning, robot navigation with obstacle avoidance, robot self-localization and materials identification and manipulation. Related research contributes to improve AGVs (Automated Guided Vehicle systems) technology. Robot@Factory is now integrated in Festival Nacional de Robótica, a yearly event which attracts lots of public, contributing also to STEM (Science, Technology, Engineering and Mathematics) popularization. MicroFactory’s main contribution is different – enhancing learning and the undergraduate experience in robotics. While Robot@Factory is intended for groups with high skills, MicroFactory is supposed to attract younger and less skilled people. So, the proposed challenges were simplified: • the team can choose were the robot starts every round; • in rounds 2 and 3 the types of parts which are in each place of the Incoming Warehouse are known from the start; • in round 3 there is only one robot… It was conceived an official robot for the MicroFactory competition. It’s a 3D printed robot, based on Arduino and cheap common electronic parts. CAD files for the mechanics (and every bit of the factory scenario), the hardware schematics and most of the software can be made available to the organizers or teachers trying to implement didactic experiences involving robotics. The challenge may then be reduced from developing a robot from scratch to implementing just a small part like programming the localization algorithm. The presented work is part of a wider Open Source project, aiming to develop project-based collaborative didactic experiences involving robotics to foster STEM education, and low-cost 3D printed educational robots based on generic electronics to support those experiences. info:eu-repo/semantics/publishedVersion

The paper describes intelligent system “Smart Factory” which is designed to increase factory productivity and efficiency of resources utilization in real time (including equipment, manpower, materials, etc.). The objectives and key areas of application are considered and it is shown that system provides flexible event-driven reaction on changes in factory environment, resource allocation, scheduling, optimization and controlling in real time. The developed method of adaptive resource scheduling, main functionality of system and examples of screens are discussed. The results of implementation and measured benefits for Izhevsk Axion Holding are presented.

Unwanted vibration produced by production process of chemical enterprises is controlled by the vibration absorber. This design is low-cost, easy installation and monitoring. The control algorithm builds a dynamic absorber model to analyze attributes of damping characteristics, and completes detailed experimental work in simulated factory environment. The investigation results demonstrate the good performance of absorber, and experiment results are able to prove the theory.