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Purpose: The aim of the research was to implement innovative technologies and management practices that would allow the chemical company to reduce the amount and harmfulness of toxic waste generated. Design/methodology/approach: The research took a quantitative approach, based on a detailed analysis of production processes and waste management. Techniques were used to monitor waste at each stage of the production cycle, allowing a precise assessment of the effectiveness of the solutions implemented. A production process analysis and waste balancing method were used. The study included detailed measurements of the consumption of toxic substances and the identification of opportunities to return them to the production cycle, with both economic and environmental benefits. Findings: The implementation of new technologies and the optimization of resource management enabled the company to significantly reduce emissions of hazardous substances and reduce costs associated with waste management and potential environmental penalties. Practical implications: The results of the project indicate that other companies in the chemical industry can use similar solutions to achieve a reduction in toxic waste and increase the efficiency of production processes, while reducing operating costs. Social implications: The project to reduce the production of hazardous substances emitted into the environment described in the article has produced the desired environmental results. The environmental impact of the company has been reduced thanks to the solutions applied. Originality/value: An important aspect of the article was to describe the innovations introduced in an operational innovative line with a closed production cycle. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have led to hydrocarbon spills, and while traditional bioremediation methods are costly and time-consuming, recent research has focused on engineered enzymes for managing pollutant. The potential of enzymes for resolving wax flow problems in the petroleum industry remains unexplored. This paper offers a comprehensive review of the current state of research activities related to the bioremediation of petroleum-polluted sites and the biodegradation of specific petroleum hydrocarbons. The assayed enzymes that took part in the degradation were discussed in detail. Lipase, laccase, alkane hydroxylase, alcohol dehydrogenase, esterase, AlkB homologs and cytochrome P450 monooxygenase are among the enzymes responsible for the degradation of more than 50% of the hydrocarbons in contaminated soil and wastewater and found to be active on carbon C8 to C40. The possible biodegradation mechanism of petroleum hydrocarbons was also elucidated. The enzymes' primary metabolic pathways include terminal, subterminal, and ω-oxidation. Next, given the successful evidence of the hydrocarbon treatment efficiency, the authors analyzed the opportunity for the enzymatic degradation approach if it were to be applied to a different scenario: managing wax deposition in petroleum-production lines. With properties such as high transformation efficiency and high specificity, enzymes can be utilized for the treatment of viscous heavy oil for transportability, evidenced by the 20 to 99% removal of hydrocarbons. The challenges associated with the new approach are also discussed. The production cost of enzymes, the characteristics of hydrocarbons and the operating conditions of the production line may affect the biocatalysis reaction to some extent. However, the challenges can be overcome by the usage of extremophilic enzymes. The combination of technological advancement and deployment strategies such as the immobilization of a consortium of highly thermophilic and halotolerant enzymes is suggested. Recovering and reusing enzymes offers an excellent strategy to improve the economics of the technology. This paper provides insights into the opportunity for the enzymatic degradation approach to be expanded for wax deposition problems in pipelines. [ABSTRACT FROM AUTHOR]

While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. [ABSTRACT FROM AUTHOR]

In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynamic analysis remains unexplored compared with the well-established theoretical system of steady-state analysis. Therefore, in this study, we propose a fast algorithm for predicting the production process performance in the delayed differentiation-based flexible production line under operation control. We first formulate practical problems existing in the auto, food, and furniture industries into a mathematical formation. Then, we offer closed-form formulae for predicting the production process performance using the built stochastic model in the production line with three machines. We also propose an algorithm to predict the performance of a production line having more than three machines. The proposed methods were verified to be highly accurate through comparison experiments. In terms of theoretical contributions, this study offers a research foundation for other transient-based studies. From a practical perspective, the proposed algorithms can be employed to predict the production process performance of processing lines under production control in advance. [ABSTRACT FROM AUTHOR]

The production industry is challenged to become more flexible and efficient while coping with a variety of disruptive events, such as natural disasters, infrastructure blockages, or economic crises. From the individual station on a production line to the global supply chain, everything is connected, making regulation and control a complex task. Biological molecular processes, such as the metabolism of living organisms or the cell cycle, are also extremely complex processes that can be compared to industrial production processes, both of which involve a series of intermediate steps and products. Thanks to (self‐)regulatory mechanisms that have evolved over time, these biological mechanisms are very efficient and robust in the face of perturbations. This article proposes an explanatory representation of these complex processes, considering both biological and technical aspects. The aim is to facilitate biomimetic transfer of biological regulation mechanisms into the technical domain. It presents concepts for biomimetic regulation of production lines and sourcing strategies and introduces a workflow for generating digital twins. This workflow is inspired by the cell cycle checkpoints, which ensure that only perfect copies of DNA are passed on during cell replication. By leveraging this understanding, the production industry can potentially improve its own processes and efficiency. [ABSTRACT FROM AUTHOR]

In the context of Industrial Internet of Things (IIoT) flexible production lines, accurately predicting production bottlenecks is crucial for optimizing efficiency and resource allocation. However, the dynamic and uncertain nature of these processes poses significant challenges. This study introduces a novel bottleneck prediction method by integrating the Drift Index (DI) with a Stacked Regression Model (SRM). This study marks the first application of stacked ensemble learning techniques in predicting bottlenecks within IIoT-enabled flexible production lines, leading to notable improvements in prediction accuracy and model robustness. The proposed method utilizes both real-time and historical data collected from IoT devices, encompassing three core steps: bottleneck data analysis, quantification of the drift index, and construction of the stacked regression model. By incorporating multiple production parameters such as equipment utilization and queue length, the method employs advanced time series analysis to forecast potential bottleneck drifts. Experimental results confirm that the DI-SRM model achieves high prediction accuracy and real-time responsiveness, effectively addressing the challenges of dynamic production environments. This approach provides reliable decision support for production scheduling and resource allocation, thereby optimizing production efficiency and enhancing market competitiveness.

In a globalizing world, the borders on the map have started to disappear economically and customers in the world economy expect high levels of quality from their suppliers. Only companies that can respond to customer expectations in the most accurate way will be able to survive in the future. One of the main goals of today's companies is to offer the best quality product at the most affordable price in line with the needs of customers. At this point, the Six Sigma approach is one of the most effective methods that companies can try. In this study, by applying the Six Sigma methodology, the root causes of PE (Polyethylene) film breaks in plastic bags were investigated by taking plastic bag production machines, processes and control steps as an example, necessary measurements were made by considering the production lines in a sample company and improvements were made in the production process by analyzing with Minitab program. At the end of the study, 24.012 TL/year savings were achieved by reducing downtime due to film breaks in the PE Film line, and 13.938 TL/year savings were achieved financially by reducing the amount of waste. In this way, the plastic bag industry benefited. In six sigma projects, it has been determined once again that the only element that is as important as making the change is that all the personnel of the company should keep up with and support this change. [ABSTRACT FROM AUTHOR]

The standard time gives a measure of how long it takes a process or workpiece to pass through each operation. It is mainly established by work measurement methodology giving the time allowed to an operator to carry out a specified task under specified conditions and defined level of performance. This definition may be with respect to a task (a job element or an activity) or a product (consisting of many tasks). In the latter case, which is the main thrust of this work, as one job element passes through different resource (work) centres, another job element of subsequent ones will immediately follow and this will continue until the last job element is completed as a whole, this means value added at each work centre. This scenario will affect the level of inventory in the system and the level of inventory in the system depends on the type of production system and configuration (series, parallel or a combination of both) adopted. This too will affect the standard time for a single product (one-off), single product (mass produced), mixed or different products which may utilize assembly line philosophy and products in batches even with backtracking. Different examples have been used to highlight this scenario with different approaches developed which have gone a long way to improve on the solution techniques of the existing models. [ABSTRACT FROM AUTHOR]

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 production industry is challenged to become more flexible and efficient while coping with a variety of disruptive events, such as natural disasters, infrastructure blockages, or economic crises. From the individual station on a production line to the global supply chain, everything is connected, making regulation and control a complex task. Biological molecular processes, such as the metabolism of living organisms or the cell cycle, are also extremely complex processes that can be compared to industrial production processes, both of which involve a series of intermediate steps and products. Thanks to (self‐)regulatory mechanisms that have evolved over time, these biological mechanisms are very efficient and robust in the face of perturbations. This article proposes an explanatory representation of these complex processes, considering both biological and technical aspects. The aim is to facilitate biomimetic transfer of biological regulation mechanisms into the technical domain. It presents concepts for biomimetic regulation of production lines and sourcing strategies and introduces a workflow for generating digital twins. This workflow is inspired by the cell cycle checkpoints, which ensure that only perfect copies of DNA are passed on during cell replication. By leveraging this understanding, the production industry can potentially improve its own processes and efficiency.

BackgroundMicrobiota play important roles in the gastrointestinal tract (GIT) of dairy cattle as the communities are responsible for host health, growth, and production performance. However, a systematic characterization and comparison of microbial communities in the GIT of cattle housed in different management units on a modern dairy farm are still lacking. We used 16S rRNA gene sequencing to evaluate the fecal bacterial communities of 90 dairy cattle housed in 12 distinctly defined management units on a modern dairy farm.ResultsWe found that cattle from management units 5, 6, 8, and 9 had similar bacterial communities while the other units showed varying levels of differences. Hutch calves had a dramatically different bacterial community than adult cattle, with at least 10 genera exclusively detected in their samples but not in non-neonatal cattle. Moreover, we compared fecal bacteria of cattle from every pair of the management units and detailed the number and relative abundance of the significantly differential genera. Lastly, we identified 181 pairs of strongly correlated taxa in the community, showing possible synergistic or antagonistic relationships.ConclusionsThis study assesses the fecal microbiota of cattle from 12 distinctly defined management units along the production line on a California dairy farm. The results highlight the similarities and differences of fecal microbiota between cattle from each pair of the management units. Especially, the data indicate that the newborn calves host very different gut bacterial communities than non-neonatal cattle, while non-neonatal cattle adopt one of the two distinct types of gut bacterial communities with subtle differences among the management units. The gut microbial communities of dairy cattle change dramatically in bacterial abundances at different taxonomic levels along the production line. The findings provide a reference for research and practice in modern dairy farm management.

Along with the development of new-generation information technology, digital twins (DTs) have become the most promising enabling technology for smart manufacturing. This article presents a statistical analysis of the literature related to the applications of DTs for discrete manufacturing lines, researches their development status in the areas of the design and improvement of manufacturing lines, the scheduling and control of manufacturing line, and predicting faults in critical equipment. The deployment frameworks of DTs in different applications are summarized. In addition, this article discusses the three key technologies of high-fidelity modeling, real-time information interaction methods, and iterative optimization algorithms. The current issues, such as fine-grained sculpting of twin models, the adaptivity of the models, delay issues, and the development of efficient modeling tools are raised. This study provides a reference for the design, modification, and optimization of discrete manufacturing lines. [ABSTRACT FROM AUTHOR]

Nowadays, there is a worldwide demand to create new, simpler, and more intuitive methods for the manual programming of industrial robots. Gestures can allow the operator to interact with the robot more simply and naturally, as gestures are used in everyday life. The authors have developed and tested a gesture-based robot programming approach for part-handling applications. Compared to classic manual programming methods using jogging and lead-through, the gesture control method reduced wasted time by up to 70% and reduced the probability of operator error. In addition, the proposed method compares favorably with similar works in that the proposed approach allows one to write programs in the native programming language of the robot's controller and allows the operator to control the gripper of an industrial robot. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics 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.)

Businesses are constantly trying to improve their production by looking for bottlenecks to improve their market position. The introduction and innovation of automated production lines is necessary for both labor shortages and productivity and quality reasons. A combination of precision, fluidity, and speed, that is the basic definition of a production line. With the advent of new technologies, production lines have also begun to continuously speed up and innovate. Innovation is the subject of this paper, where the problem of designing a completely new layout for a new production line in the food industry has been addressed. The aim of this paper was to create a design for the optimal layout of the production line in preselected production areas. Optimal use of the space allocated for production is very important for every company today. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).1 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data2 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football ,.3 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest 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.)

With the rapid development of novel energy vehicles, power generation, photovoltaics, and other industries, power electronic devices have gained considerable attention. Insulated-gate bipolar transistors (IGBTs) have been widely used in those fields. With the emergence of intelligent manufacturing concepts such as Germany's "Industry 4.0" and China's "Made in China 2025", conventional manufacturing which needs to be upgraded with higher efficiency and yield is rapidly pivoting toward digitalization and intelligence. The digital twin methodology has been extensively used in various industries for constructing virtual models of physical entities, facilitating real-time data interconnection to reduce costs and improve efficiency. This study proposes a modular intelligent IGBT production line based on the digital twin. Real-time data are transmitted from a physical line to a digital line for storage and analysis. The digital line is visualized, and an intelligent management platform containing multiple functions is developed. Additionally, a process simulation database is established to obtain the optimal process parameters. Numerous quality issues that can arise during each process of IGBT packaging are addressed using a problem-solving approach based on the digital twin methodology. Consequently, this digital-twin-based IGBT intelligent production line effectively enhances yield rates and efficiency. IGBT modules with various packaging forms such as ACF, ACE, and ACD are manufactured. [ABSTRACT FROM AUTHOR]

Bamboo is easy to cultivate, grows quickly, and can replace wood or other products after processing. However, the previous layout was mainly based on manual experience. There were issues such as long material transportation distances and frequent interference in transportation paths, resulting in high construction and maintenance costs for production lines. In order to solve the problems, in this work, a new methodology and mathematical formulation was proposed to minimize the total material handling cost and workflow interference. The new method combined a NSGA- II and the A* algorithm in order to minimize the above two objectives. Compared to the Manhattan distance used in traditional algorithms, this method used the A* algorithm to calculate the optimization objective, which can achieve lower material transportation costs and has the ability to handle transportation obstacles and path interference issues. The results showed that after optimization, the average material transportation cost was reduced by 18.1%, with a maximum reduction of 10.5% and a maximum reduction of 97.5% in cross interference points. This indicated that the method proposed in this article was effective in the layout of the original bamboo primary processing production line. [ABSTRACT FROM AUTHOR]

The increasing complexity of products and manufacturing processes, combined with the constantly advancing technological integration of the manufacturing sector, raised new challenges for world-class industries to optimize time-to-market, resources, and cost. Simulation, as an essential Industry 4.0 enabling technology, allows one to emulate the steps of a manufacturing process, thereby achieving significant improvements in all the product and process development phases. A simulation process can be implemented and improved by creating the Digital Twin of the manufacturing system, which can be realized on a single-line scale or extended to the whole factory. The Digital Twin merges physics-based system modeling and real-time process data to generate a virtual copy of an observable object to reduce and optimize the extensive time and cost of physical design, prototyping, commissioning, reconfiguration, and maintenance. This study aims to investigate how the implementation of digital twin technology can help optimize the balance between power consumption and productivity, taking into account existing barriers and limitations. By following this outline, this study shows the design and development of a digital twin for a floor-ball manufacturing line present in the Smart Factory of Ostschweizer Fachhochschule (Switzerland). The entire production process is reproduced with Siemens Technomatix Plant Simulation software 2201, and data connection and processing are handled by a tailored toolchain consisting of an agent, a database, Python packages, and the COM interface from Tecnomatix. This toolchain feeds the digital twin with data from the physical operating environment. In particular, this study compares direct power measurements with the ones expected by the digital twin to assess digital model accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This paper analyses the feasibility of tracking data originality for pharmaceutical manufacturing in a tamper-proof manner using a geographically distributed system. The main research question is whether it is possible to ensure the traceability of drug manufacturing through the use of smart contracts and a private blockchain network. Methods: This work employs a private Ethereum network with a proof-of-authority consensus algorithm to allow participating nodes to commit the medicament manufacturing originality as transactions in blocks. We use smart contracts to assess the "Original" principle of the ALCOA+ data integrity principles for full sensor-enabled production lines within pharmaceutical manufacturing plants. We have evaluated our data originality assessment approach employing a temporal series of 1300 reports generated based on real datasets from pharma production lines. Out of these reports, 300 reports have been randomly tampered with to make them "unoriginal" (i.e., falsified). Results: Evaluation consistently shows that the proposed approach systematically detects all the manufacturing records whether original or not, together with any source of falsification. By randomly injecting four common data falsification types, their approach effectively detects tampering and ensures the authenticity of the data originality acquired by sensors within manufacturing lines. Conclusion: The approach of using a private blockchain network with a proof-of-authority consensus algorithm and smart contracts is a feasible method to track data originality for pharmaceutical manufacturing in a tamper-proof manner. In addition, this approach effectively detects tampering and ensures the authenticity of the data originality acquired by sensors within manufacturing lines. [ABSTRACT FROM AUTHOR]

To modernize the existing manual control copper powder workshop anode production line and achieve superior technological outcomes, researchers designed an operational algorithm for an automated complex within the copper powder project workshop. The team selected equipment for an effective anode production line automation system. They wrote the software code in TIA Portal 16v and implemented a system to visualize the technological process at each stage of the line's operation. The developed automation system simplifies the control process of the anode production line and enhances its operational reliability. [ABSTRACT FROM AUTHOR]

The article discusses the implementation of a digital twin in the system of adaptive control of robotic manipulators. The use of digital twins to plan, control and maintain the life cycle of production equipment forms the sustainable introduction of robotic systems into the technological processes of high-tech enterprises. The use of a digital twin in the collaboration of a robot system allows you to design more versatile workplaces and assembly lines, and optimize their workload. A new approach is proposed that allows solving problems of direct and inverse kinematics for robotic manipulators. The approach is to represent the kinematic diagram of a robotic arm with six rotational joints as a vector model. Due to the low computational load, the use of the proposed approach in control systems makes it possible to reduce delays when working in real time, with the ability to increase the number of robotic arms and control with higher accuracy.

The aim of the paper is to present the concept of a digital twin (DT) as part of the Industry 4.0 strategy. In the form of a case study, a digital twin of a production line for the processing of engine blocks is presented, which will serve as a starting point for further research in the field of digitization of production processes. The research part describes the simulation model of the production line with the representation of the material flow as a basis for the creation of a digital twin. The simulation model was used to optimize the production processes of the engine block and to verify the increase in its productivity. A case study implemented through a digital twin enables testing and analysis of changes before they are introduced into real production.

The interdependence between the scheduling method and the production efficiency of a micro-hole drilling production line for printed circuit boards (PCBs) holds significant importance, necessitating the optimization of such a production line's scheduling. Consequently, this research paper presents a scheduling optimization approach for the micro-hole drilling production line of a PCB, utilizing complex events as its foundation. Initially, a complex event model was constructed to establish correlations among extensive production line data. Subsequently, the typical complex events associated with the micro-hole drilling production line of a PCB were defined, thereby enabling the all-around monitoring of the operation state of such a production line. Furthermore, this study presents the establishment of a production scheduling model for PCB micro-hole drilling. With the goal of minimizing the maximum completion time, the catastrophe genetic algorithm was used to solve the initial scheduling scheme of the printed circuit board micro-hole drilling production line. The reliability and effectiveness of the catastrophe genetic algorithm in solving the hybrid-driven production scheduling problem of complex events were verified. Dynamic scheduling was performed when three complex events occurred in the production line: emergency order insertion, abnormal equipment operation, and tool failure. The scheduling optimization rate after identifying the emergency insertion event could reach 25.1%. The scheduling optimization rate of the production equipment operation event was related to the specific failure time of the equipment. The scheduling optimization rate after identifying the tool failure event could reach 25%. Rescheduling immediately after identifying the tool failure event could exert no effect on the initial scheduling process. It was proven that the identification and rescheduling of complex events can improve the production efficiency of a PCB micro-hole drilling production line. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering 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.)

Modeling systems in present day life give opportunity to find optimum ways of enterprise functioning and changes in set parameters without interference in production processes or before starting the planned production. The article studies imitation modeling use in agricultural machine-building. Brief description of FlexSim software is provided. Functional features of the system of imitation modeling allow users to identify bottlenecks, analyze statistic data, plot diagrams, etc. Imitation models of production processes on assembly line for seed-loaders are described, obtained results are analyzed and bottlenecks of the production line are identified. The authors study possibilities of optimization, which can raise productivity, for example, cutting the number of operators and introducing coordinators. The present research shows the importance of modeling and analyzing production processes in virtual environment in order to plan changes in production. Apart from modeling production processes, FlexSim program can be used for analyzing material, finance, information, staff resources, which affects managerial decision-making and strategy of industrial enterprise development.

Along with the development of new-generation information technology, digital twins (DTs) have become the most promising enabling technology for smart manufacturing. This article presents a statistical analysis of the literature related to the applications of DTs for discrete manufacturing lines, researches their development status in the areas of the design and improvement of manufacturing lines, the scheduling and control of manufacturing line, and predicting faults in critical equipment. The deployment frameworks of DTs in different applications are summarized. In addition, this article discusses the three key technologies of high-fidelity modeling, real-time information interaction methods, and iterative optimization algorithms. The current issues, such as fine-grained sculpting of twin models, the adaptivity of the models, delay issues, and the development of efficient modeling tools are raised. This study provides a reference for the design, modification, and optimization of discrete manufacturing lines.

Nowadays, there is a worldwide demand to create new, simpler, and more intuitive methods for the manual programming of industrial robots. Gestures can allow the operator to interact with the robot more simply and naturally, as gestures are used in everyday life. The authors have developed and tested a gesture-based robot programming approach for part-handling applications. Compared to classic manual programming methods using jogging and lead-through, the gesture control method reduced wasted time by up to 70% and reduced the probability of operator error. In addition, the proposed method compares favorably with similar works in that the proposed approach allows one to write programs in the native programming language of the robot’s controller and allows the operator to control the gripper of an industrial robot.

Digital twin (DT) is a virtual replica of a physical world that has become one of the most important ideas in the manufacturing industry's digital revolution. DT modeling is a vital issue in building a DT of a production line. In this paper, a method is proposed to address the difficulties of complicated production line business and data heterogeneity. The method focuses on essential data in the production line and creates conceptual and information models based on the ArtiFlow model and AutomationML (AML). Conceptual models are mainly used to describe and analyze the business activities of the production line, and information models describe real production lines in the form of XML files. The proposed modeling approach has been applied to a real-world clothing production line to demonstrate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

The global production tends towards more sustainable manufacturing, which forces manufacturers to constantly change and adapt. In our case, we are considering the FESTO CP LAB 400 production line, primarily designed for the training of personnel in the field of automation, which is essential for maintaining competitiveness in today's world. The production line consists of seven fully automated workstations and one manual assembly workstation, which represents the bottleneck of the production system. The paper presents a comparative study of the lines (human assembly vs. collaborative robot assembly) with an emphasis on economic and environmental aspects. The input parameters of the production line were obtained based on real-world measurements, while the assembly time of the collaborative robot was determined through simulation studies. The results of the study are presented graphically and numerically and confirm the contribution of the introduction of a collaborative robot to the assembly workstation in both financial and environmental terms. [ABSTRACT FROM AUTHOR]

The global production tends towards more sustainable manufacturing, which forces manufacturers to constantly change and adapt. In our case, we are considering the FESTO CP LAB 400 production line, primarily designed for the training of personnel in the field of automation, which is essential for maintaining competitiveness in today's world. The production line consists of seven fully automated workstations and one manual assembly workstation, which represents the bottleneck of the production system. The paper presents a comparative study of the lines (human assembly vs. collaborative robot assembly) with an emphasis on economic and environmental aspects. The input parameters of the production line were obtained based on real-world measurements, while the assembly time of the collaborative robot was determined through simulation studies. The results of the study are presented graphically and numerically and confirm the contribution of the introduction of a collaborative robot to the assembly workstation in both financial and environmental terms.

The production line is an important part of most manufacturing industries. However, the services in the devices may experience some issues that disrupt the production line, requiring the assistance of an IT technician to resolve the problem. Therefore, this study seeks to bridge the gap through the design and development of a PowerShell Script to allow the user to remote control the services in a device such as computers and servers. Using this program, the production operator can solve the issue immediately rather than wait for an IT technician, thus increasing productivity and performance. The program design and development followed the Rational Unified Process (RUP) methodology. First, the functional requirements were collected through interviews and content analysis. Later, a prototype called Remote Restart Services System (RRS) is developed using PowerShell programming language on the Windows PowerShell ISE platform. An evaluation was later conducted to assess its usability. The findings suggested that RRS is practical and easy-to-use. The respondents were also pleased with its functions. The field study helps developers to refine the framework by providing direct input from the target audience. It can be a guide to identify areas for improvement and eventually help realise the goals.

As social uncertainty increases, there are changes in values such as safety, sustainability, and response to changes. The manufacturing industry is also required to improve its resilience by dynamically recombining production resources to respond to change. In the operation of a production line, if demand fluctuates due to changes in the external environment or customer needs, or if sudden production fluctuation such as equipment failure occurs, the production line will be stopped due to reconfiguration or equipment restoration. Therefore, the challenge is to continue production and improve productivity even when fluctuation occurs. In order to respond to the above challenges, this research aims to dynamically and quickly re-plan the production line using the existing equipment according to current equipment and production status. In this paper, failures of the production equipment, such as robots and tools, are regarded as production fluctuation factors. In order to continue production with the remaining resources in the event of equipment failure, it is necessary to change the process plan. However, since these are engineering tasks in the production preparation stage, there was the problem of not being able to flexibly change them at the manufacturing site during the production execution stage. In this development, an alternative plan pre-generation and selection method was developed to maintain production by re-allocating tasks during equipment failure. In this approach, first, multiple alternative plans for task allocation for failures in each piece of equipment are planned in advance using a process plan optimization technique. Next, production fluctuations due to equipment failures are detected. Finally, dynamic changes are possible by selecting alternative plans that maximize the throughput of the entire production system. Initial verification results are shown by comparison with the conventional method for equipment failure using simulation on a small-scale robot assembly line for automotive inverter. [ABSTRACT FROM AUTHOR]

Thermally induced error has proven to be the major source of machining error for the machine tool working in a non-temperature-controlled workshop. Current research on thermal error modeling and compensation is implemented based on the measured thermal deformation of machine tool, and the data is obtained referring to spindle idling rather than metal cutting condition, resulting in the disadvantages of the established model with low adaptability to varying conditions. In this paper, a modeling and compensation method based on the dimensional error of the machined parts is proposed to address the issue and verified through machine tools in an automatic production line. Compared with the modeling based on thermal deformation, the method formulates a more direct relations between temperature rise and machining error. The thermal error modeling is carried out by measuring the dimension deviation of the inner hole diameter of the motor end cover and the screened representative temperature variables. Meantime, considering the temperature coefficient in model is difficult to converge when modeling with the single-day data, the unified modeling with the multi-day data is realized by improving the conventional multiple linear regression model. Finally, the generalized model used for thermal error compensation in x direction of the machine tool is obtained. The real-time compensation of the thermal error based on the established model is realized with the machine tool machining in mass production. The verification results show that this error modeling and compensation method can reduce the machining error of the end cover by more than 52%, irrespective of experiencing various complicated working conditions. Stability and robustness of the modeling are also validated through application on the other machine tool with the same configuration and real machining over seven days. • Dimensional error of machined parts represents thermal effect of machine tool more directly. • Thermal error modeling with real cutting condition guarantees strong robustness. • A generalized error model arises from processing multiday data obtained on production line. • Thermal error model is duplicable to other machine tool and has stability of prediction accuracy. [ABSTRACT FROM AUTHOR]

Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product's physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline's output. An innovative framework for the early detection of a product's physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice. [ABSTRACT FROM AUTHOR]