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Copyright of Electroplating & Finishing is the property of Electroplating & Finishing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, reducing standby electric power consumption by individual production facilities has been studied to reduce their energy consumption, particularly regarding machine tools, industrial robots, etc., which are principal types of facilities in production systems. Energy-saving idle state capable production facilities (energy-saving idle facilities) have been developed and have begun to be commercialized. They can be in the normal idle state, permitting the quick startup of production from their idle state to reduce energy. Specifically, auxiliary machines unused during idling are shut down, thus minimizing electric power consumption. Such an idle state is defined as the energy-saving idle state (eco-idle state). The research and development of energy-saving idle facilities is advancing, but a lack of effective methods to operate production lines after energy-saving idle facilities have been introduced on these lines is hindering their introduction. Past research has proposed methods for operating energy-saving idle facilities on production lines. However, breakdowns occurring in production facilities have not been considered. This paper proposes a method for operating energy-saving idle facilities on production lines considering breakdowns. The method consists of an idle-time prediction algorithm and an idle-state selection algorithm. Case studies are presented to confirm the effectiveness of the proposed method.

Purpose: The article presents an analysis of the application of selected quality management methods and tools in order to identify factors affecting downtimes in the production line, illustrated with an example of the automotive industry. Methodology/Approach: The paper contains an analysis of downtimes in the production process using selected methods and tools of quality management. The authors used a combination of tools and methods to carry out the analysis. In this concept, first, the 5WHY method and the Ishikawa diagram were used. Findings: The research results presented in the paper suggest that better results can be achieved by using a set of quality tools instead of one particular tool. The authors found that using a wide range of quality tools can be useful to reduce downtimes on the production line. Research limitation/implication: The major limitation of the paper is that it is based on one case of an organisation from the automotive industry. In the future, it will be necessary to conduct studies in more organisations so as to find out if the same result can be achieved. Originality/Value of paper: The article is concerned with the use of quality management methods and tools to analyse production line breakdowns. Until present, in subject literature, the causes of downtimes have been analysed without differentiating between planned and unplanned downitmes of the production line.

Visual inspection is an important task in manufacturing industries in order to evaluate the completeness and quality of manufactured products. An autonomous robot-guided inspection system was recently developed based on an offline programming (OLP) and RGB-D model system. This system allows a non-expert automatic optical inspection (AOI) engineer to easily perform inspections using scanned data. However, if there is a positioning error due to displacement or rotation of the object, this system cannot be used on a production line. In this study, we developed an automated position correction module to locate an object’s position and correct the robot’s pose and position based on the detected error values in terms of displacement or rotation. The proposed module comprised an automatic hand–eye calibration and the PnP algorithm. The automatic hand–eye calibration was performed using a calibration board to reduce manual error. After calibration, the PnP algorithm calculates the object position error using artificial marker images and compensates for the error to a new object on the production line. The position correction module then automatically maps the defined AOI target positions onto a new object, unless the target position changes. We performed experiments that showed that the robot-guided inspection system with the position correction module effectively performed the desired task. This smart innovative system provides a novel advancement by automating the AOI process on a production line to increase productivity.

In order to meet the urgent needs of the current clothing market for "multi-variety, small batch, short cycle", the rules for the division of clothing modules were set by modularized division method based on rules, and the process were modularized. Based on the method of the large-scale clothing production, the principle of process optimization in the module was summarized. Through the optimization of each process in the module, the rapid and efficient preparation of the processes in each module was realized. Taking the sewing production line of women's leisure trousers as an example, the results show that the efficiency of component module 1, 2 and connection module is 94.1%, 98.3% and 95.5%, respectively, the equilibrium indices are 4.58, 3.00, and 2.83, respectively, which meets the requirements of lean production of garment enterprises. [ABSTRACT FROM AUTHOR]

Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Scientific Bulletin of National Mining University is the property of National Mining University, State Higher Educational Institution and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is well established, that certain bacteria within the Brochothrix , Carnobacterium , Lactobacillus, Lactococcus , and Leuconostoc genera have an important role in the spoilage of chill stored poultry meat packaged in modified atmosphere. However, little is known about the role of microorganisms that are difficult to culture and the microbiota during poultry spoilage. We combined traditional cultivation and culture-independent 16S rRNA amplicon sequencing to investigate the microbiota encompassing putative bacteria of whole broiler meat, packaged in modified atmosphere, during and exceeding shelf-life. Samples were taken from 6 flocks during independent slaughter days. Additional samples were analysed from the production line. There was a significant difference in the microbial community structure of 80%O 2 /20%CO 2 retail packaged broiler meat during different times of shelf-life, mainly due to an increase of species within the Brochothrix , Carnobacterium , Vagococcus , and Janthinobacterium genera. These genera were already detected four to eight days after slaughter. However, no significant difference between flocks with respect to the microbiota encompassing putative spoilage bacteria was observed when examined in retail packaged broilers, slaughtered at the same abattoir on different days. Our study also showed that lactic acid bacteria within the Vagococcus genus can constitute a dominating part of the later shelf-life microbiota in fresh whole broiler meat packaged in 80%O 2 /20%CO 2 modified atmosphere. A single operational taxonomic unit (OTU) assigned as Janthinobacterium lividum , an occasional spoiler of meat products, was identified as a major part of the microbiota in late shelf life broiler meat and swab samples in the cooling facility at the slaughter house production line. The combination of traditional cultivation and culture-independent methods provided a great insight into the microbiota of broiler meat during shelf-life and identified a potential point of contamination in the production line for cold tolerant Janthinobacterium. • The microbiota of broiler carcasses was dominant already four to eight days after slaughter • The acceptable shelf-life of whole broiler meat in 80%O 2 /20%CO 2 MAP was around 8–12 days • The spoilage microbiota was quite similar between flocks • The core microbiota consisted of Brochothrix , Carnobacterium and Vagococcus [ABSTRACT FROM AUTHOR]

Purpose: The article presents an analysis of the application of selected quality management methods and tools in order to identify factors affecting downtimes in the production line, illustrated with an example of the automotive industry. Methodology/Approach: The paper contains an analysis of downtimes in the production process using selected methods and tools of quality management. The authors used a combination of tools and methods to carry out the analysis. In this concept, first, the 5WHY method and the Ishikawa diagram were used. Findings: The research results presented in the paper suggest that better results can be achieved by using a set of quality tools instead of one particular tool. The authors found that using a wide range of quality tools can be useful to reduce downtimes on the production line. Research limitation/implication: The major limitation of the paper is that it is based on one case of an organisation from the automotive industry. In the future, it will be necessary to conduct studies in more organisations so as to find out if the same result can be achieved. Originality/Value of paper: The article is concerned with the use of quality management methods and tools to analyse production line breakdowns. Until present, in subject literature, the causes of downtimes have been analysed without differentiating between planned and unplanned downitmes of the production line. [ABSTRACT FROM AUTHOR]

The implementation of RFID systems allows automation of information flow along the production chains. This is a desirable condition for increasing efficiency, productivity and management, and it simplifies other activities such as traceability records along these supply and production chains, especially in the automobile assembly supply chain as in the case of Renault-SOFASA-Colombia, where the traceability of automotive parts is realized manually by means of mobile terminals, which is time consuming and laborious, with low automotive parts rate and high error rate. In this work, a RFID solution for the traceability of automotive parts in real time in Renault-SOFASA-Colombia is implemented, where a middleware under client-multiserver architecture for Siemens readers is developed. The terminal, where the middleware is located, behaves like a client and links to several RFID readers which behave like servers, forming a star type network topology. This architecture allows a cost reduction in the implementation and operation of RFID solutions by reducing hardware acquisition and energy consumption, being an alternative that can compete with the solution provided by a manufacturer as Siemens without neglecting the robustness and the proper functioning of the system. Moreover, the proposed solution concentrates all the maintenance efforts of the system at a single point, which is due to the fact that a terminal attends all RFID portals. The tests performed, where the transport carts with the automotive parts and their respective RFID tags are passed through the RFID portals of the assembly plant, are in agreement with the results reported by the developed middleware. [ABSTRACT FROM AUTHOR]

Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on‐line, real‐time, stand‐off cake quality monitoring. [ABSTRACT FROM AUTHOR]

A model of a single-machine production system with finite magazine capacity is investigated. The input ow of jobs is organized according to geometric distribution of interarrival times, while processing times are assumed to be generally distributed. The closed-form formula for the generating function of the time to the first buffer overow distribution conditioned by the initial buffer state is found. The analytical approach based on the idea of embedded Markov chain, the formula of total probability and linear algebra is applied. The corresponding result for next buffer overows is also given. Numerical examples are attached as well. [ABSTRACT FROM AUTHOR]

Because of the large quantity of three-dimensional (3D) models and the manual operation process used for their virtual assembly, production line assembly design is time consuming and cannot readily meet the requirements of normalization. To address these problems, we propose a rapid virtual assembly approach based on the smart recognition of assembly features and present a system based on this concept. A 3D-two-dimensional (2D)-3D assembly feature recognition mode is proposed. The 3D models are first standardized using a standardization algorithm. The standardized 3D models are subsequently divided into six half parts, and each half part is projected into the coordinate plane to obtain its 2D projection drawing. The contours of the 2D projection drawings are obtained via a contour-recognition algorithm, and segments in contours are classified into different groups according to collinearity. A lightweight 3D model of the original 3D model is created through a series of Boolean operations. Assembly features can be obtained by matching the lightweight 3D model with the original 3D model. The recognized assembly features are used in the rapid assembly system to perform the assembly, and the constraints among these features are automatically added when two models are in proximity. Three comparison tests are conducted, and the results show that the system simplifies the assembly process, greatly increases the assembly design efficiency of the production line, and simultaneously reduces the workload and operational complexity. [ABSTRACT FROM AUTHOR]

The process preparation time for complex parts accounts for more than 20% of the parts manufacturing cycle, which is the main factor restricting the improvement of production efficiency. To shorten the process preparation time, we propose an approach for dynamic clamping and positioning of flexible tooling system based on the digital twin technology. Three core technologies embodied in the proposed method are illustrated in details as follows: (1) real-time acquisition, organization, and management of the geometry data of the machining parts; (2) digital twin data driven decision of the clamping forces and positioning position; (3) construction of the digital twin-based flexible tooling system. To elaborate how to apply the proposed approach to the reality, we present detailed implementation process of the proposed approach in the diesel engine cylinder head production line. Meanwhile, the future work to completely fulfill digital twin-based smart flexible tooling system is discussed. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

Industry 4.0 collects, exchanges, and analyzes data during the production process to increase production efficiency. Internet of Things (IoT) devices are among the basic technologies used for this purpose. However, the integration of IoT technology into the industrial environment faces new security challenges that need to be addressed. This is also true for a production line. The production line is a basic element of industrial production and integrating IoT equipment allows one to streamline the production process and thus reduce costs. On the other hand, IoT integration opens the way for network cyberattacks. One possible cyberattack is the increasingly widely used distributed denial-of-service attack. This article presents a case study that demonstrates the devastating effects of a DDOS attack on a real IoT-based production line and the entire production process. The emphasis was mainly on the integration of IoT devices, which could potentially be misused to run DDoS. Next, the verification of the proposed solution is described, which proves that it is possible to use the sampled flow (sFlow) stream to detect and protect against DDoS attacks on the running production line during the production process.

The paper presents the results of simulation research on buffer space allocated in a flow line and operation times influence on the throughput of a manufacturing system. The production line in the study consists of four stages and is based on a real machining manufacturing system of a small production enterprise. Using Tecnomatix Plant Simulation software, a simulation model of the system was created and set of experiments was planned. Simulation experiments were prepared for different capacities of intermediate buffers located between manufacturing resources and operation times as input parameters, and the throughput per hour and average life span of products as the output.

The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.

Currently, challenges in quality improvement have driven various enterprises to create quality management systems in smart factories. The development of quality management systems enables quality control for reviewing product quality, identification, and eliminating product failures. However, process adjustment in quality control decisions may be hard to determine when failures are detected. To overcome this problem, an expert system (ES) that applies the failure mode and effects analysis (FMEA) method for developing quality control systems in brake disc production lines is considered. This quality control system concentrates on analyzing product defects that occur frequently in the production line and will lead to an improved performance of the braking system; the selected product defects are disc thickness variation (DTV), runout, and parallelism. This quality control system developed two modules, the designed FMEA (DFMEA) and component FMEA, which apply a rule-based algorithm for selecting actions. We propose the rules of configuration into the expert system code. The results indicate that the operator can carry out a quality control system with decision-making that can be supported by intelligent searching and reasoning in an expert system.

With the rapid growth of the industries and commercial space being more competitive attention to quality control and most importantly to produce zero defect products has become more important than ever. Retaining customers, attracting new customers, and making them loyal require many efforts among which producing competitive, high quality, and zero defects products enjoys the highest weight. Recognition of errors and defects in production lines which cause reduction in product quality is an effective lever to eliminate errors and improve the existing situation. In this paper, failure modes and effects analysis (FMEA) technique has been utilized to find errors and their causes in the production line of the Iranian Tobacco Company. With the completion of FMEA table and calculating Risk Priority Number (RPN) values, all errors were zoned and prioritized. The prioritization of the discovered errors based on FMEA technique indicated human error as the most important factor in the emergence of errors.

In Japan, the Law Concerning the Rational Use of Energy (Energy Saving Law) has been revised to regulate the annual average of specific energy consumption at 1%. In December 2015, the United Nations Climate Change Conference (COP21) culminated in the Paris Agreement, which demanded global efforts to reduce greenhouse gas emissions. To solve these problems, industries have been becoming more important to evaluate and reduce energy consumption when designing, operating, and improving manufacturing systems. Therefore production management methods concurrently considering productivity and energy consumption are needed. Recently, production facilities which have an energy-saving idle state such as machining’s and industrial robots have been proposed and begin to be introduced. These facilities have two types of idle state such as an energy-saving idle state and a normal idle state. In the normal idle state, the facilities are possible to immediately start up to produce. In the energy-saving idle state, the facilities need a period to start to produce. Within keeping the energy-saving idle state, the amount of the energy consumption becomes a low level because auxiliary machines that support the facility behaviors are temporally stopped. However production management methods in a production line such as a decision support in each facility to operate the normal state to the energy-saving idle state does not propose. In this research, we propose a decision support method to operate production facility with the energy-saving idle state in production lines. We carried out a case study that is a production line which consists of three facilities.