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For electronic components, micro parts are produced in large quantities. Hence, in modern production lines constantly increasing stroke rates are targeted to reach high productivity. The used foil material is usually fed by roll feeding systems, which limit the feeding rate and potentially cause damage to the foil’s surface. A promising alternative is a feeding concept based on the operating principle of a linear induction machine in which the feeding force is applied in the form of Lorentz Force.

Konkurenca na trgu ter želja po odzivnosti in izpolnjevanju naročil kupca v vedno krajšem času prisilita podjetja k stalnem izboljševanju proizvodnih procesov in zniževanju proizvodnih stroškov. V magistrskem delu je prikazana uporaba metod in orodij vitke proizvodnje z namenom odkrivanja in odpravljanja zapravljanj v proizvodnji. Analizirali smo proizvodno linijo, ki obdela, opere, preizkusi in premeri aluminijast ulitek. Ugotovljeno je bilo, da šibko točko predstavljajo zastoji, zato smo s pomočjo preprostega modela prišli do predlogov izboljšav za nekaj ključnih vzrokov za nastajanje zastojev. Za oceno učinka izboljšav smo definirali ključne kazalnike ter izvedli simulacijo. Predlagane izboljšave prinašajo željen dvig učinkovitosti in produktivnosti. Predstavljene so bile tudi možnosti za nadaljnje izboljševanje proizvodnega sistema. Competition in the market and the desire to respond and fulfill customer orders in an ever shorter time force companies to improve production processes and reduce production costs constantly. In the master's thesis, the use of methods and tools of lean production is presented with the purpose of the detection and elimination of waste in production. We analyzed a production line that processes, washes, tests, and measures an aluminum casting. Availability was found to be a weak point. Therefore, we came up with suggestions for improvements to some of the key downtime causes using a simple model. To assess the impact of improvements, we defined key indicators and performed a simulation. The proposed improvements enable the desired increase in efficiency and productivity. Possibilities for further improvement of the production system were also presented.

The Industrial Internet of Things (IIoT) aims to greatly improve the existing production procedures, enhancing customer experiences, reducing costs and increasing efficiency. IIoT will make a significant impact on existing business models in several areas. However, IIoT has several issues related to the security of the information and the excessive centralization which creates single point of failures, poor performance and scalability. Therefore, we introduce an Industry 4.0 case scenario, and, on top of it we propose a multi-layer distributed ledger architecture for IIoT that combines various types of Distributed Ledger Technologies (DLTs) in order to deliver a lightweight and efficient solution for Industry 4.0. However, in this paper we only focus on the design of the first layer of the architecture. We design a lightweight Directed Acyclic Graph (DAG) DLT architecture and propose several improvements regarding the lightweight devices participation, data storage, cryptography and consensus.

The Industrial Internet of Things (IIoT) combined with Cyber Physical Systems, Flexible Manufacturing and intelligent manufacturing has led to a new wave of industrial development. The new industrial situation requires factories to efficiently produce customized products for customers based on the existing configuration of the factory. In response to this problem, this paper proposes a production line virtual reorganization framework and a manufacturing resource semantic modeling method. Within this framework, according to the processing technology enterprises can automatically arrange virtual production lines to improve the factory's manufacturing response level. Semantic modeling of a wide range of massive manufacturing resources, forms a digital manufacturing resource model that can be understood by computers. This model can be used for data transmission and storage, as well as for big data analysis and intelligent optimization based on massive data.

A Basic Oxygen Furnace (BOF) for steel making is a complex industrial process that is difficult to monitor due to the harsh environment, so the collected production data is very limited given the process complexity. Also, such production data has a low degree of variability. An accurate machine learning (ML) model for predicting production outcome requires both large and varied data, so utilising data from multiple BOFs will allow for more capable ML models, since both the amount and variability of data increases. Data collection setups for different BOFs are different, such that data sets are not compatible to directly join for ML training. Our approach is to let a neural network benefit from these collection differences in a joint training model.

Production equipment is in general collaboratively located in different areas to perform the entire manufacturing process. In order to develop a flexible production line, modern factories are employing industrial robots with 6 degree-of-freedoms (DoFs) to facilitate the task of continuous loading and unloading in the manufacturing process. However, the sizes and spatial position of workpiece are significantly different, and traditional solutions are facing limitations in terms of planar motion and workshop floor cleaning. In this paper, we propose a hybrid robot platform with one mobile robot and one cooperative manipulator (6 DoFs) to be utilized in this industrial operations, especially in the multiple stations operation. Through this platform, we also integrate simultaneous localization and mapping (SLAM) and 3D robot vision technologies, which enables the mobile robot to move to the target position with high localization precision, path planning, autonomous navigation, and obstacle avoidance. Experimental results verify that the proposed framework in discrete production line for the continuous loading and unloading task. The proposed approach can be utilized in flexible manufacture potentially, such as automotive mount and electronic products assembly.

Around the problem of workpiece machining accuracy, a model of autonomous behavior of equipment and the method of establishing patterns in knowledge base have been developed in this paper. Two self-learning models based on pattern comparison: deterministic knowledge and uncertain knowledge self-learning model are presented, while a scene understanding method for intelligent autonomous behavior of equipment is proposed. The research scheme of intelligent autonomous behavior optimization of equipment based on scene understanding and the specific research framework of workpiece machining precision are produced. Finally, focusing on the actual production process of bearing end cover, the flexible automatic production line of bearing end cover is established, the workpiece machining accuracy research based on independent behavior of equipment for CMM and CNC machine tools is preliminarily realized with the help of automatic line control end, it lays a solid foundation for the overall autonomous and intelligent cooperation between the testing area and the processing area.

With the rapid development of China’s new energy industry, the use of lithium-ion batteries has increased sharply, and the demand for battery cathode metals such as nickel, cobalt, and manganese has also increased rapidly. Scrapped ceramic saggars that are used to produce the cathode materials of lithium-ion batteries contain large amounts of nickel, cobalt, and manganese compounds, thus, recycling these saggars has high economic value and ecological significance. In this paper, the emergy method is used to analyze the ecological benefits of the typical Ni–Co-containing saggar recycling process in China. This paper constructs an ecoefficiency evaluation index for industrial systems based on emergy analysis to analyze the recycling of nickel and cobalt saggars. The ecological benefits are analyzed, and the following conclusions are drawn. (1) The Ni–Co-containing saggar recycling production line has good economic and ecological benefits. (2) The process has room for improvement in the energy use efficiency and clean energy use of the crystallization process and the efficiency of chemical use in the cascade separation and purification process. This study also establishes a set of emergy analysis methods and indicator system for the evaluation of the ecological benefit of the recycling industry, which can provide a reference for the evaluation of the eco-economic benefit of similar recycling industry processes.

Automatic crankshaft production lines require high reliability and accuracy stability for the oscillating grinding machine. Crankshaft contour error represent the most intuitive data in production field selective inspection. If the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine can be found, it would be great significance for the reliability maintenance of the oscillating grinding machine. Firstly, a contour error decomposition method based on ensemble empirical mode decomposition (EEMD) is proposed. Secondly, according to the contour generating principle of the pin journal by oscillating grinding, a calculation method to obtain the effect of the axis position control error of the oscillating grinder on the contour error of the pin journal is proposed. Finally, through the grinding experiments, the error data are acquired and measured to calculate and decompose the contour error by using the proposed methods for obtaining the mapping relation between the crankshaft pin journal contour error and the axis position control error. The conclusions show that the proposed calculation and decomposition methods can obtain the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine, which can be used to predict the key functional component performance of the machine tool from the oscillating grinding workpiece contour error.

The study specifies the value of the dust resistance coefficient in the process of wood dust filtration in a pilot-scale test stand. The experiments were carried out for one type of filter material—polyester with a PP film previously used in different production lines. Filter bags from the filtering installation of the processing line for narrow surfaces of furniture panels of the honeycomb structure with a chipboard frame, HDF, natural veneer cladding, and a line of CNC drilling machines, were taken into account. Before the pilot-scale tests, the bags had been in use in industrial installations from zero to nine months. All tests were performed under identical filtration conditions. The values of the dust resistance coefficient depend on the operating time and the conditions in which filtration is carried out in an industrial plant, and increased from 6507 s−1 to 10,208 s−1 for the bags from the filter of the narrow surfaces processing line and to 29,729 s−1 for the bags from the filter of the drilling line. The most important factor influencing the properties of the filter bag in the process of wood dust filtration in an industrial filter is the cleaning pulses frequency.

Published in IFAC-PapersOnLine, 54(11):55-60, 2021; International audience; The problem of reducing vibrations during continuous hot-dip galvanizing process is addressed. Using the finite difference method the concerned part of the steel production line is modeled by a state space version of the axially moving strip equation which takes into account disturbances that may affect its efficient functioning. The synthesis of an appropriate control law for this process aims to reduce the impact of these disturbances and its implementation requires a definition of the position and the number of the sensor/actuator allowing an optimal reduction. Some numerical simulation of the steel strip behavior are presented and discussed for different sources of vibrations with and without a control system.

Demonstration of real industrial equipment, manufacturing processes, and control communication is essential for students of technical universities and colleges to improve their technical competencies and knowledge. To build an industrial control systems (ICS) lab with real hardware and control software usually requires a significant amount of finances. However, the lab equipment is usually accessible only to a limited number of students. A possible alternative is a blended classroom environment that combines real inexpensive devices connected with a configurable simulation environment. In such an environment, groups of students can create their own experiments and observe a near real-life behavior of an ICS system.In this paper we demonstrate how a blended ICS classroom can be built of the Factory I/O 3D software simulator and real UniPi PLC devices equipped with digital and analog inputs. Using such an environment, students may design a set of nontrivial manufacturing scenarios, e.g., a production line, and make experiments with ICS components. The paper presents the topology and equipment of the blended ICS classroom. We also introduce two lab scenarios focused on the security of ICS processes and analysis of Modbus communication in the ICS environment.

To ensure that fuel rods operate in nuclear reactors safely and reliably, UO2 pellets with different enrichment levels of 235U in the same production line are manufactured in batches and divisionally managed to avoid confusion or the potential misloading of UO2 pellets with different enrichment levels. At the same time, nondestructive tests for their enrichment levels and loading uniformity and all UO2 pellets must be nondestructively tested during production. By studying the enrichment detection mechanism of the UO2 pellets of 235U, the design of an integral standard rod was carried out, and a single integral standard rod was used to achieve the calibration of the enrichment measurement curve, as well as the detection and calibration of abnormal pellets. This study undertook a comparison test of 235U enrichment between the neutron activation method and the array multi-probe passive method. The test results showed that the array multi-probe passive method had higher detection efficiency and equal accuracy.

The lids of glass containers which have a ‘twist-off’ mechanism are manufactured from tinplate through a process of cutting and drawing. Previously, the tinplate was protected with a double layer of a certain epoxy-phenolic varnish. During cutting, the detachment of threads of varnish is produced, and these may reach more than 150 microns in diameter. These threads stick to the equipment, thus hindering the shaping process. After manufacturing thousands of lids, stops must inevitably be made in production in order to clean machinery. Through the application of a fractioned design of experiment (DoE) application, carried out on an industrial scale, the effect of a number of factors on the detachment of threads of varnish was studied. Some to these factors refer to coating, others to the substratum and others to the process of cutting and drawing. It is concluded that the detachment is greater in the disk areas which are parallel to the forward direction of the production line. This problem could be substantially reduced, and even eliminated, if the direction of the rolling of the sheet metal were perpendicular to that of the forward direction of the production line, if the blank-holder is situated at 4 bar, if the time between the curing process and cutting is no more than 3threedays, if the clearance in the cutting is situated at 0.06 mm, and if the grammage of the varnish and the grammage of the layer of tin are increased.

The problem of developing mathematical methods for improving the efficiency of the organization of repair and maintenance work requires the formulation of general principles for determining their actual state in the process of operation. In order to increase the efficiency of the use of machines and electrical equipment of technological systems, the optimization of restoration works based on the results of diagnostics is necessary, which will ensure their high trouble-free operation and durability of all elements of the system. The main task is to determine the residual resource, which makes it possible to reduce the complexity and cost of the work that is carried out during ongoing repairs, installed only when necessary; set the timing (periodicity) of diagnosis. To optimize the characteristics of technological systems and diagnostic tools used the technical and economic criterion of the minimum unit costs of the technological system as a whole. In this case, take into account the features of the elements of the system and the diagnostic method, which affects the total unit costs. Regarding diagnosis, the total costs associated with the use of diagnostic tools, the costs of their production and expenses caused by the error of diagnosis. Management of the technical condition of technological systems based on determining the optimal residual life of each element allows you to plan repair and restoration work using the principles of system analysis. At the same time, it is advisable to simultaneously repair the elements of the production line, and therefore the costs will be distributed between them and thereby the cost of repair of each unit will be reduced. Compatible repair of technological installations, the terms and volumes of which are determined by comparing the intervals of the residual life of all components of the technological line, will be more cost-effective than individual repairs of individual machines. The estimates obtained with this method, in particular, can be used in the planning of repair and maintenance work of technological systems. Key words: technological line, failure, diagnosis, residual resource, technical and economic criterion, target function

This paper proposes an integrated multi-product production planning and distribution allocation in the supply chain network, including the detailed production sequences in each production line for a dairy manufacturing. The models are designed for manufacturers that produce multi-products. One of the concerns of the multi-products manufacturers is the sequence-dependent changeover times and costs that occur when switching from one type of product to another. The models are developed into two-stages due to different levels of decisions. The first stage models the supply chain network that determines the optimum production planning for each product type on each production line, delivery schedule, and inventory planning for the entire supply chain simultaneously to maximize the total profit. Sequentially, based on the optimal production planning, detailed production schedules are then optimized in the second stage of the model with minimizing total setup costs as the objective. The result shows that the proposed models significantly reduce the total cost and consequently increase the annual gross profit.

Manufacturing flexibility improves a firm’s ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License.

The improvement of operational planning in the field of oil refinery management is becoming increasingly essential and valid. The influential primary factor, among others, is the ever-changing economic climate. The industry must continually assess the potential impacts of variations in the final product demand, price fluctuations, crude oil compositions and even seek out immediate opportunities within the market. The Master Production Schedule (MPS) is a planned process within the Production Management System that provides a mechanism for active collaboration between the marketing and manufacturing processes. However, the problem of MPS is a predictable non-deterministic, polynomial-time and NP-hard combination optimisation issue. The global search for the best solution to the MPS problem involves determination and funds that many industries are reluctant to provide. Hence, the alternative approach using meta-heuristics could provide desirable and workable answers in a realistic computing period. In this paper, a unique hybrid Multi-Objective Evolutionary Imperialist Competitive Algorithm (MOEICA) is proposed. The algorithm combines the advantages of an Imperialist Competitive Algorithm (ICA) and a Genetic Algorithm (GA) to optimise a multi-objective master production schedule (MOMPS). The primary objective is to integrate the ICA with GA operators. The paper will also apply the optimised MOMPS to the Kalak Refinery System (KRS) operations using the proposed algorithm. The application involves determining the available capacity of each production line by estimating the parametric values for all failures. In addition, the gross requirements using demand forecasting and neural networks are defined. The proposed algorithm proved efficient in resolving the issues of the MOMPS model within KRS compared to the NSGAII and MOPSO algorithms. The results reflect that the novel MOEICA algorithm outperformed NSGAII and MOPSO in almost all measurements.

This research and resulting contribution are results of Assumption University of Thailand. The university partially supports financially the publication., Purpose: The paper copes with the queueing theory for evaluating a muti-stage production line process with concurrent goods. The intention of this article is to evaluate the efficiency of products assembly in the production line. Design/Methodology/Approach: To elevate the efficiency of the assembly line it is required to control the performance of individual stations. The arrival process of concurrent products is piled up before flowing to each station. All experiments are based on queueing network analysis. Findings: The performance analysis for unstable concurrent sub-items in the production line is discussed. The proposed analysis is based on the improvement of the total sub-production time by lessening the queue time in each station. Practical implications: The collected data are number of workers, incoming and outgoing sub-products, throughput rate, and individual station processing time. The front loading place unpacks product items into concurrent sub-items by an operator and automatically sorts them by RFID tag or bar code identifiers. Experiments of the work based on simulation are compared and validated with results from real approximation. Originality/Value: It is an alternative improvement to increase the efficiency of the operation in each station with minimum costs., peer-reviewed

Applying the wireless sensor network of the Industrial Internet of Things and the radio frequency identification technology to the production workshop of the discrete manufacturing industry, the real-time status of the shop floor can be automatically collected, providing a powerful decision-making basis for the upper-level planning management department. This paper proposes a reference architecture and construction path for smart factories by analyzing industrial IoT technology and its application in manufacturing workshops. Combined with the analysis of the status quo and needs of the discrete manufacturing enterprise workshop, this paper designs the overall architecture and theoretical model of the system. In view of the variety of on-site manufacturing data, large amount of data, variable status, heterogeneity, and strong correlation between data, integrated key technologies such as WSN and RFID, the industrial IoTs solution for manufacturing workshops is given. The multi-thread data real-time collection, storage technology and product tracking monitoring of the workshop are studied. Finally, the performance of the system is analyzed from the perspective of real-time and quality. The results show that the system is effective in the monitoring of production line data.

With the large-scale application of electric vehicles (EV) in the world and also in China, the contradiction between the EV and charging stations has become more and more prominent. People always cannot easily find the charging stations or when they find them finally found they do not work. To connect the vehicle, charging station/pile and end-users for making the charging simple, convenient, efficient and visible is becoming very important. People need a platform to tell them where, when and how to charge for their EV. Matrix Mobility is focusing on realizing this comprehensive charging solution together with OEM, charging point operator (CPO), electric power company and parking lots by using big data analysis. Matrix Mobility installs the charging solution into the car unit before cars go off production line and meanwhile integrates the same function into OEM’s own APP with opening API to help end-users increase their charging experience.

To the best of our knowledge, the general approach of designing alloys with specified mechanical properties does not exist. This is due to the unresolved problem of analysing the set of heterogeneous variables that affect the mechanical properties along its production line from the smelting of the alloy to the manufacture of the final product. Here, we show that in principle this problem can be solved by analysing all the strengthening mechanisms in a common reference frame with reference to the single factor namely, the generalized degree of metallicity and covalence, which characterizes the entire interatomic bonds in all phases of the alloy. Such factors are able to reflect the results of hardening by various mechanisms because of the correlation with the mechanical properties. From the energy view point, these factors correspond to the proportion of the metallic and covalent bonds energy in the total energy of all chemical bonds in the alloy. Based on the approach being developed, we considered a method for predicting new doping systems for dispersively strengthening aluminum alloys according to the criterion of a given strength and have considered the methodology of optimizing chemical composition in steel smelting which is used for mass production of parts according to the criterion of the desired mechanical properties obtained due to solid solution hardening.

Adopting magnetron sputtering continuous automatic production line equipment for industrial production, polyester fabrics as the substrates were coated with nano-Ag/TiO2 composite films prepared by direct current sputtering method and direct current/radio frequency reactive sputtering method, respectively. The microstructure of Ag/TiO2 composite films deposited on the surface of the fabrics was dense and uniform. Structural colors were generated on the surface of the fabrics coated with the composite films and the coloring mechanism was consistent with the single-layer film interference principle. The color fastness, mechanical properties, comfortable properties were not significantly changed and had better antistatic property, anti-ultraviolet property, and antibacterial property compared with the original polyester fabrics. Therefore, the multi-functional and structurally colored fabrics can be prepared by magnetron sputtering technology, and can achieve industrial production, and have wide application prospects in apparel, home textile, and so on.

The reinforcement learning (RL) is being used for scheduling to improve the adaptability and flexibility of an automated production line. However, the existing methods only consider processing time...

Quality analysis of the polarizer of a production line can be performed using image processing technology. The existing method of detecting defective images based on deep learning can ensure accurate classification; however, its detection speed is low, the model requires a large amount of memory, and it is difficult to meet the real-time requirements of online detection systems when hardware resources are limited. Therefore, in this study a lightweight polarizer defect detection network, called DDN, was developed based on deep learning. First, a parallel module was designed to build the network. This module has two main advantages. First, it mixes different convolution template sizes, and can fuse the features of different scales and extract more defect features than the traditional convolution layer. Second, depthwise separable convolution is used to replace full convolution in this module, which significantly reduces the number of parameters and the multiply-accumulate operations. Finally, a global average pooling (GAP) layer is used instead of a fully connected layer. The GAP layer has no parameters to optimize, which substantially reduces the number of network parameters. Experimental results show that the proposed method is better than existing methods in terms of classification speed, precision, and memory consumption for polarizer detection, and can satisfy real-time requirements.

A novel mathematical model is introduced that allows solving real-life scheduling problems in complex multi-stage machine environment with (i) non-triangular sequence-dependent setup times and (ii) shifting production bottlenecks, both of which are important aspects appearing in varying manufacturing industries. The primary goal is to minimise the tardiness of customer orders, which may consist of multiple production orders each in turn composed of several batches. A secondary objective is to maximise the production capacity utilization as measured by the makespan. The model is elaborated for general animal-feed plants which have to deal with the particular production scheduling problem on a daily basis. Dispatching rules are introduced to enhance the optimization progress. Numerical experiments show that optimising the model leads to schedules that meet the due dates. Moreover, by reducing the mean idle time of production lines with 35.6%, the optimization leads to a makespan reduction of 6.5% on average compared to real-life applied schedules.

Mixed-model assembly line is known to be a special case of production lines where variety of product models similar to product characteristics are assembled. This article addresses available-to-promise (ATP) in mixed-model assembly line sequencing problems in a Make-To-Order environment in two stages. First, the customers are prioritized based on their corresponding profit values and a decision support system for order acceptance/rejection based on ATP is developed. By implementing this concept and developing a mathematical model, delivery quantity and date in a planning horizon are determined based on the inventories in the stock. The second stage is solving a mixed binary mathematical model to sequence accepted orders suitably according to demands due dates that guarantees the orders are not released too late or too early. The problem simultaneously considers following objectives: minimizing the total tardiness and earliness costs based on the determined priority of orders and minimizing the utility work and idle time of workers in the production line. An algorithm based on Lagrangian relaxation is developed for the problem, and tested in terms of solution quality and computational efficiency. To validate the performance of the proposed algorithm, various test problems in small size are solved using the CPLEX solver, and compared with the Lagrangian relaxation method. Finally, the proposed model is solved in large size problems to analyze the model performance. The drawback of the CPLEX is that it could not solve large problem instances in reasonable time. For the small sized problem, there is approximately 1% duality gap for the Lagrangian relaxation method. The maximum duality gap in the Lagrangian relaxation method for the large sized problem is always kept below 4% while the average computing time is very reasonable. Therefore, according to the results obtained from test problems, the developed Lagrangian relaxation method proved to be the suitable method for this problem.

Industrial automation system and control need to increase the flexibilities and to decrease the complexities in the automation system. Due to the large panels and multitasks in the production lines...

In a study entitled "Analysis of Production Tests with Orifice Plate on the J-97 Well KYU Geothermal Field" will discuss the production test. Geothermal production tests are conducted to determine the potential of the production capacity of a well at different wellhead pressures so that the value of the mass flow rate and the well production curve is obtained. J-97 well is one-phase steam well, so the production test method used is to use the orifice plate. The pressure data read by the orifice plate is then converted to a mass flow rate. Calculating the mass flow rate can use the British Standard 1042 method. In the production test to obtain the final result in the form of a production curve, the gas deliverability equation can be used. The method used in the S-87 well is Flow After Flow Test (Back pressure Test). The final result of the curve shows a decrease in the production line calculated using the British Standard 1042 method..

The volatility and flexibility of a production line in a smart factory demands for high dynamics during the assembly tasks and repetitive movements in an un-ergonomic body posture. Hence, the workers are exposed to the danger of developing work-related musculoskeletal disorders (MSDs). Previous studies have reported that exoskeletons help to reduce fatigue and prevent injuries during lifting, carrying, pushing, pulling, and overheadpositions tasks, improving productivity in manufacturing industry. However, no studies have reported the advantages of digital integration of an exoskeletons network in a smart factory. This paper gives an overview of benefits and challenges regarding the connectivity of exoskeletons within a smart factory. Moreover, it describes the communication architecture of a cyber-physical exoskeleton network. Finally, an experimental test of fog computing structure was conducted based on IoT messaging protocols, allowing real-time data analytics for benefits of the manufacturing process.

Nowadays, robots are an essential part of modern production lines, usually working in a designated area since they can pose a threat to human workers. The so-called soft robots constitute a human-friendly alternative to classic industrial robots, even allowing for human-machine collaboration. This is possible due to their soft and therefore inherent safe structure. In this paper we consider quasi continuum manipulators (QCMs), a special kind of soft robots. Their dynamic behavior is affected by friction as well as their soft materials. Dynamical models are thus hard to identify, suffering from imperfections and uncertainties. To overcome these flaws we propose a disturbance observer (DOB) based controller using an extended Kalman filter (EKF). We show superior performance on a real robot compared to an existing benchmark concept based on a PID-like controller. The generalization of this approach is demonstrated by implementing our method on two QCMs with different kinematics.

In the manufacturing industry, pneumatic powered components provide a safe and reliable opportunity to automate a production line. However, compressed air systems are notoriously expensive to operate as a result of system losses and inefficiencies. Typical systems have an output efficiency of 10–12%. This offers a significant improvement opportunity to meet sustainable targets concerning energy consumption in industry and lower life cycle energy impacts. Amongst various inefficiencies, leakages and excessive pressures are identified as some of the most common sources of waste. The scope of this study was to make use of a compressed air system which was designed in the form an experimental test bed in order to assess the sustainability impact of various compressed air shortcomings. Simulations were carried out under experimental conditions to measure the additional energy consumption and air volume required for different pneumatic scenarios. Some of the results showed that a noise level of 70 dB is attributable to a leakage of 1.5 mm at the industry standard of 6 bar. Such a single leak could incur more than €470 of additional electrical costs and would result in 1.8 tonnes of additional carbon dioxide emissions within one year of operation, highlighting a significant effect on the life cycle impacts of industrial production., peer-reviewed

17th Global Conference on Sustainable Manufacturing, GCSM, Shanghai, Peoples R China, 9 Oct 2019 - 11 Oct 2019; Procedia manufacturing 43, 576-582 (2020). doi:10.1016/j.promfg.2020.02.157 special issue: "Sustainable Manufacturing - Hand in Hand to Sustainability on Globe : Proceedings of the 17th Global Conference on Sustainable Manufacturing / Edited by Günther Seliger, Ömer Şahin Ganiyusufoğlu, Weimin Zhang, Holger Kohl", Published by Elsevier, Amsterdam [u.a.]

Current enhancement of data availability and cloud technology provide a tremendous opportunity and platform for data-driven modeling and analyses in smart manufacturing. This paper proposes a data analysis framework to diagnose the root causes of first-time quality (FTQ), where FTQ is the quality of a part when it is measured the first time after all processes/operations in a production line. Due to the fact that too many inline factors in a production line with multiple operations may impact FTQ directly or indirectly, finding the key factors is essential yet difficult. An automotive crankshaft line is studied as an example. Production real-time inline data are collected and fetched from the data warehouse. After data preprocessing and exploratory analysis, a prediction model for FTQ is built through machine learning algorithms in Python. The data analysis identifies the significant process factors that influence the part quality, consequently improvement solutions and adjustment strategies could be generated based on the analysis. This study indicates the effectiveness and sustainability of data analysis and machine learning, as applied for quality diagnostics and improvement in manufacturing systems.

Mission reliability could systematically characterize the running state of a manufacturing system and the rationality of production task setting in advance. However, few studies can realize the mission reliability modeling, which integrates production scheduling based on fully considering the complexity, dynamics, and multi-state characteristics of a manufacturing system. Thus, a dynamic method for modeling the mission reliability of a multi-state manufacturing system with multiple production lines is proposed in this paper. First, the multi-state characteristic of manufacturing systems operation is analyzed. On this basis, considering the composition of manufacturing systems, the connotation and modeling mechanism of the mission reliability of manufacturing systems are proposed. Second, combining the discretization of machine performance states and dynamic continuity of the performance degradation process, the dynamic modeling method of multi-state machine performance and buffer usage are respectively discussed. The transfer evolution of task demands in the production line is analyzed, and the decomposition method of task scheduling and load mapping in multiple production lines is provided. Third, considering the characteristics of multiple production lines and the buffer of a manufacturing system, taking production tasks as the core and integrating production scheduling, a modeling method for the mission reliability of manufacturing systems is proposed. Finally, the effectiveness of the proposed approach is verified using a case study on the modeling of the mission reliability of a manufacturing system of aerospace parts.

Induction motors (IM) are susceptible to mechanical failures with severe consequences for production lines; hence, detection and classification of IM faults have been of great interest for researchers in last years. Broken rotor bars (BRB) are one of the most difficult faults to detect, since this fault does not give any indication of deterioration increasing significantly the production costs; hence, it is quite important to detect them in early states. Several methodologies have been proposed to extract information about the motor condition relying on motor-current-signature analysis (MCSA); however, they usually require high-computational-complexity algorithms to reach trustworthy result. In this work, a novel methodology for early detection and classification of BRB faults in IM is proposed. This methodology consists of obtaining two spectrograms using fixed-width windows, which are segmented through Otsu algorithm to visualize the time evolution of fault frequencies. The fault severity classification is performed through Kurtosis computation from non-stationary components. Obtained results from real experimentation validate the proposed-method high efficiency, reaching an overall 100% accuracy on detecting and classifying half, one, two BRBs, and healthy condition.

The methods of multi-criteria decision analysis are widely used for problems of selecting the best solution or ordering their sets. In the vast majority of cases, solving multi-criteria decision-making problems focuses on finding solutions for a given set of alternatives. When, for one or more decisional variants, the values of their attributes change, or a new alternative for assessment appears, the problem occurs. Therefore, in this paper, we focus on identifying the full decision-making model in the issue related to the study case of production line optimization. In this work, the Characteristic Objects METhod (COMET) has been applied. In effect, it identifies the full decision-making model. The decision problem has been identified, taking into account six decision criteria, and its operation has been used in organizing four decision alternatives.

The steering knuckle is the important safety part of commercial vehicle, the structure is more complex as the performance improved. In this paper, a kind of disc-type steering knuckle with complexity up to level S4 is studied, and a precision forging process is proposed to change the traditional horizontal open pre-forging and large flash final forging into semi-closed flat thin flash pre-forging and semi-closed flat thin flash final forging. Based on the analysis of mechanism and effect of the classical forming theory on the flat thin flash, the equivalent stress, equivalent strain and temperature distribution field as well as the force formation curve and deformation law are obtained by means of thermal-mechanical coupled finite element simulation analysis, and the correctness of the process scheme is verified. The precision forging of steering knuckle has been successfully developed on the program controlled hammer and CNC electric screw press, the results are consistent with the simulation results. The material utilization rate of the new process is 88%, which is 16% higher than that of the traditional process, with better quality and stronger bearing capacity. In recent years, the company has built four automatic production lines with CNC electric screw press as main equipment, with robot operation and online detection of hot forging, it has realized mass production of flat thin flash precision forging of A161 disc-type knuckle series product.

Automated guided vehicles (AGVs) are widely used for material handling in warehouses and automated production lines due to their high efficiency and low cost. However, AGVs usually interact with each other because of the restricted capacity of the layout. Although many algorithms have been proposed to address the problem, most of them are inefficient for collision and deadlock avoidance in dynamic environments. This paper proposes a dynamic resource reservation (DRR) based method supporting time-efficient scheduling and collision avoidance of multiple AGVs. In this method, the layout is divided into square blocks with the same size that are abstracted as points in the undirected graph. In order to solve the collision and deadlock problem dynamically, the shared resource points of each vehicle are extracted from their guide paths in real time. Unlike the traditional approaches most of which adopt a static point occupation policy, DRR exploits dynamical reservations of shared resource points to change AGV movement states for avoiding collisions and deadlocks, resulting in better time efficiency. We jointly implement the algorithm on both central and local controllers. Extensive simulation results demonstrate the feasibility and efficiency of the proposed collision and deadlock prevention method.

The Fourth Industrial Revolution has made efficient and adaptive manufacturing a prominent research topic. The necessity for personalized products leads to complex production lines. Due to the increased complexity, planning and scheduling of manufacturing processes seeks to identify near-optimum solutions to ensure fast and precise decision making. This research paper aims to contribute to the field of adaptive scheduling by proposing an algorithm that enables near real-time cooperation among machines, workforce and the production manager. A framework for self-adaptive scheduling in a real-world manufacturing case deriving from an SME that produces solar panels will be presented.

Surface quality is the most important index to improve the overall quality of strip steel. In order to implement the fault location on the hot-rolling line with surface defects of strip steel, a fault tracing model based on information fusion of historical production cases and process data is proposed. For historical cases, the model determines the defect cause labels through text similarity calculation, and fuzzy semantic inference is used to obtain the probability distribution of defect causes on this basis; for the process data, the model uses L1 regularization method for feature selection, and XGBoost integration method is used to train the correlation model between process data and defects to determine the contribution of each feature in the data source. Finally, based on the D-S evidence theory, different rules are set to merge the two judgments to determine the probability of each source of failure on the hot-rolling production line. The model is applied to the real production environment of iron and steel enterprises, and it is verified that the proposed method can effectively assist experts in decision-making, which greatly improves the efficiency of tracing the source of faults on the hot-rolling production line.

This article aims to improve deep-learning-based surface defect recognition. Owing to the insufficiency of the defect images in practical production lines and the high cost of labeling, it is difficult to obtain a sufficient defect data set in terms of diversity and quantity. A new generation method called surface defect-generation adversarial network (SDGAN), which employs generative adversarial networks (GANs), is proposed to generate defect images using a large number of defect-free images from industrial sites. Experiments show that the defect images generated by the SDGAN have better image quality and diversity than those generated by the state-of-the-art methods. The SDGAN is applied to expand the commutator cylinder surface defect image data sets with and without labels (referred to as the CCSD-L and CCSD-NL data sets, respectively). Regarding anomaly recognition, a 1.77% error rate and a 49.43% relative improvement (IMP) for the CCSD-NL defect data set are obtained. Regarding defect classification, a 0.74% error rate and a 57.47% IMP for the CCSD-L defect data set are achieved. Moreover, defect classification trained on the images augmented by the SDGAN is robust to uneven and poor lighting conditions. Note to Practitioners —This article proposes a method of defect image generation to address the lack of industrial defect images. Traditional defect recognition methods have two disadvantages: different types of defects require different algorithms and handcrafted features are deficient. Defect recognition using deep learning can solve the above problems. However, deep learning requires a plethora of images, and the number of industrial defect images cannot meet this requirement. We propose a new defect image-generation method called SDGAN to generate a defect image data set that balances diversity and authenticity. In practice, we employ a large number of defect-free images to generate a large number of defect images using our method to expand the industry defect-free image data set. Then, the augmented defect data set is used to build a deep-learning defect recognition model. Experiments show that the accuracy of defect recognition can be significantly improved by building a deep-learning defect recognition model using the augmented data set. Therefore, deep learning can achieve excellent performance in defect recognition with a limited number of defect images.