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[EN] Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of -0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE., This work was supported in part by the Government of Aragon and the European Social Fund "Construyendo Europa desde Aragon" under Grant T40_20D Research Group, and in part by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018," Spain, under Grant RTI2018-096384-B-I00.

In current global manufacturing scenario, every organization wants to fulfill their customer needs to retain their position in the market. Lead time plays a vital role to improve them in the global competitive market. The aim of this research work is to minimize the overall lead time of the product by finding out bottlenecks and to eliminate them. The important processes in the production line are work in progress, layout optimization and line balancing. The existing manufacturing techniques and the process of the product were studied thoroughly and then time study was followed. Based on the time study results TAKT time was calculated for the demand followed with current state VSM was drawn and the bottleneck stations were identified. After developing current state VSM, assembly line was balanced using flexible line balancing software. Further improvements were made with the lean techniques of fish bone diagram, Pareto chart and layout optimization. After the execution of the above lean techniques the future state VSM was drawn and observed that the lead time and value added time were reduced by about 15 and 16 min respectively.

On the basis of systems thinking, intelligent research methods have been developed for pressing briquette material by constructing a mathematical and computer model of the material compaction process. The apparatus of the installation for studying the process of pressing the material (fine coal fractions) has been developed. The first level of the pressing process deals with the production line for preparing and compacting the material. The second level of the hierarchy includes devices of the technological line, including the device for pressing the material. Starting from the second level of the hierarchy, a material compaction apparatus is considered, in the form of a material compaction plant, where one of the abstract indicators is the material compaction coefficient. The technique of the experiment in the apparatus of the material pressing plant is presented. On the basis of the main parameters of the process of pressing the briquette material revealed during the experiment, a mathematical model was developed in the form of a system of equations. On the basis of the developed mathematical model, the starting characteristic of the material compaction apparatus is studied, which characterizes changes in the relative density of the compressed material. The change in the density of the briquette during the compaction of the material for the installation is equal to 0.25, while the density of the material increases from 600 to 724 kg/m3.

The availability of automated production lines and production data has opened a new opportunity for improving the geometrical quality of assemblies by using a digital twin in the concept of a smart assembly line. In this concept, a digital twin is generated from scanned data of incoming parts for each assembly. The assembly process is then simulated for the digital twin using variation simulation tools. Subsequently, the optimal production parameters are found by utilizing optimization algorithms along with the simulations so that the geometrical qualities of assemblies are maximum. Two effective production parameters that can be optimized in this concept are the combination of parts and adjustments of locators. The techniques to implement optimize these parameters in the production are referred to as selective assembly and individualized locator adjustments, respectively. This paper evaluates the results of optimizing each parameter separately and both parameters together. To attain this goal, the results of applying the optimal parameters on three industrial cases are determined and compared. The results evidence that the potential of individualized locator adjustment in improving geometrical quality is considerably greater than selective assembly.

Performance analysis of flexible manufacturing systems has received substantial research attention. Most of the studies focus on steady-state behavior. The transient performance, which is critical for system operation, control, and improvement, is less investigated. No analytical method is available to evaluate the transients in flexible lines with unreliable machines, finite buffers, and setup times. In this article, an analytical model is introduced to study the transient performance of multiproduct serial lines with Bernoulli reliability machines and nonnegligible setups during changeovers. To reduce the state-space dimension, an approximation method is introduced to evaluate the performance in two-machine lines, where the sequence of multiple part types in the inventory is approximated by the ratio of product mix. Such an approximation method leads to high precision of production rate estimation. Then, for longer lines, the analysis approach in two-machine lines is used as a building block in an iterative aggregation procedure. Numerical experiments show that the iteration procedure is convergent and results in an acceptable accuracy in performance evaluation. In addition, system properties, such as monotonicity and reversibility, are investigated. Note to Practitioners —Manufacturing systems become more and more flexible, where multiple types of products are made on the same production lines. Although significant efforts have been devoted to studying flexible manufacturing systems (FMSs), most of the research contribute to the steady-state analysis. However, the transient performance is of critical importance for control and improvement of flexible systems, particularly, when the system’s setup times cannot be ignored. Therefore, in this article, an analytical method using approximation and aggregation procedures is introduced to evaluate the performance of flexible serial lines with Bernoulli machines and setups. Applying such a method, a long serial line is first decomposed into overlapped two-machine lines and then an iteration procedure is used backward and forward to estimate the line performance. Moreover, even in two-machine lines, the part sequence is approximated by the distribution of part types to reduce computation intensity. Using such a method, the system performance of long multiproduct Bernoulli lines with setups can be evaluated efficiently with an acceptable accuracy, which provides production engineers and managers an effective tool to study the transient performance of FMSs, and design real-time production control and improvement strategies.

In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a one-line production line in the workshop, establishes a mathematical model with the shortest transportation time as the objective function, and proposes an improved particle swarm optimization(IPSO) algorithm to obtain an optimal path. In order to be suitable for solving the path planning problem, we propose a new coding method based on this algorithm, design a crossover operation to update the particle position, and adopt a mutation mechanism to avoid the algorithm from falling into the local optimum. By calculating the shortest transportation time obtained, the improved algorithm is compared with other intelligent optimization algorithms. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of related improvement mechanisms.

Maintenance plays a highly important role in achieving production targets and system performance. Electromechanical equipment and facility infrastructure within motor manufacturing industries are expected to perform at optimal efficiency during the operational phase of production. A major problem in the automotive production plan from motor industry statistics is associated with unexpected downtime, which is largely linked to aging equipment. During production downtime, much time is lost to fault finding, repairs, and replacement of faulty components within production lines. This transforms into low throughput in production, and performance gradually declines during the operational life cycle of the equipment. This paper presents an approach taken to prevent such instances in the automotive manufacturing industry, which considers an optimized condition-based maintenance approach to predict the condition of each component and assembly line using Failure-Mode-and-Effect-Analysis (FMEA). The condition-based performance level prediction is designed to help in formulating maintenance schedules and strategies that eliminate unplanned downtimes.

Nowadays, industrial companies are constantly improving their processes and increasing their efficiency in order to achieve higher profits. One of the most common measures of efficiency at the semi-automatic assembly lines is the Overall Equipment Effectiveness (OEE) as a Key Performance Indicator (KPI). The goal of measurement of OEE is to improve the effectiveness of machines or production lines with minimal investments. The article presents a way to increase this indicator using an own hybrid analysis. Firstly, a literature review demonstrates scientific relevance. Secondly, a hybrid analysis is introduced for improving the efficiency. The effectiveness of the analysis is demonstrated by a practical example using data mining and line balancing, during which there was a 60% improvement in yield. Patterns recognitions by machine combined with human analysis provides this outstanding result. Hybrid analysis can be used not only on assembly lines, but also on individual machines where a lot of data is generated and the cycle time or takt time needs to be reduced for higher yields.

With the increasing pressure from the market and the surge of “Industry 4.0,” staying competitive and relevant is becoming more and more difficult. The assembly line, which represents a long-term investment of the manufacturing industry, needs to be efficiently utilized. While assembly line balancing (ALB) problem had been studied for decades, oversights on the bottleneck resources could significantly impede its efficiency. In leveraging such information as part of the optimization problem, a contagious artificial immune network (CAIN) approach is proposed to simultaneously address ALB efficiency and bottleneck resources while achieving a truly balanced production line. A computational experiment conducted on benchmark data sets has demonstrated a proof-of-concept, where leveraging knowledge-intensive optimization approach had successfully produced high-quality solutions up to 100% improvement with statistically significant justification. Such findings may play an essential determinant in the manufacturing industry, whether being relevant or left out in the era of increasingly being information-driven.

One of the problems encountered in the design and implementation of a serial production line (SPL) is the buffer size between the machine tools. The buffer size of the SPL has an important impact on the productivity of the whole production system. The machine tools’ characteristics including their uptimes and downtimes and the process parameters are the main factors that affect the decision regarding the buffer size, and thus the productivity of the SPL. Due to the dynamic nature of this problem, it is complex to find the optimal buffer size in SPL. Thus, in this paper, an Efficient Prediction Model (EPM) is developed using Artificial Neural Network (ANN). The purpose of the developed EPM is to find the buffer size between each succeeding pair of machine tools in SPL at any given uptimes and downtimes of machine tools. An optimization model based on genetic algorithms (GA) is used to generate the learning data for the prediction model to find the optimal or near optimal buffer size of the bay of each machine tool in SPL. The proposed approach integrates the optimization and prediction methodologies to evaluate, and predict the optimal buffer sizes for maximum productivity. Including uptime and downtime parameters enable the proposed method to be used to improve the design of running SPL as well as to design a new SPL. Numerical examples for five and fifteen machine tools were conducted independently in this research and the results show the ability of the proposed method to determine the optimal buffer sizes in a reasonable amount of time. In particular, the results of case studies show that the developed model accurately predict the optimal buffer size, especially for the case of five machines and even for a higher number of machine tools yet with acceptable but less accuracy. Finally, the performance of the proposed approach was compared with some results of the state of the art methods reported in the literature. The comparison shows the superiority of the present approach to identify buffer sizes for higher throughput under the same uptimes and downtimes.

Fog computing has been widely deployed in intelligent production lines to provide real-time computing services for terminal devices to alleviate the transmission problem between cloud and terminal. Nevertheless, when there are many services provided in a single fog node, how to choose an optimal data processing path is a challenge to minimize optimization latency and power consumption while ensuring the reliable transmission of data with different priorities. To address the problems as mentioned above in this paper, we first propose a joint evaluation model of time latency and power consumption based on task priority. Then, we formulate a fog computing adaptive scheduling algorithm (FCAS) based on dynamic programming to obtain the optimal path for processing data with different priorities at fog nodes. Experimental results illustrate that our proposed evaluation model and algorithm have lower power consumption, high efficiency, and higher reliability.

As a response to the low utilization of production lines in the context of an increasing number of product variants with significant differences in cycle times, line-less modular assembly concepts known as matrix production have evolved. Whilst the many degrees of freedom a matrix production offers provide different ways to react to disturbances and balance utilization, it also increases the complexity for scheduling exponentially. For real-time scheduling high solution quality and high efficiency are needed. This paper contributes a multi-objective scheduling approach based on Monte-Carlo Tree Search that self-adjusts to the scheduling problem to improve solution quality and execution time.

Aggregation method has been widely utilized to evaluate the performance measures of production lines. However, traditional aggregation method (TAM) has low prediction accuracy in production lines with multiple bottlenecks, such as “inverted bowl” lines and “oscillatory” lines. Therefore, the root causes of the low prediction accuracy are first investigated. Extensive numerical studies indicate that $\Lambda $ -units are one of the major causes, where a $\Lambda $ -unit is defined as the subsystem between two consecutive bottlenecks. Then, an improved aggregation method (IAM) is proposed to improve the prediction accuracy of TAM. IAM is established by extending the traditional two-machine aggregation building blocks to general multimachine aggregation building blocks in $\Lambda $ -units. Specifically, for a small-scale $\Lambda $ -unit, an aggregation building block is established for all the machines and buffers in the $\Lambda $ -unit. For a large-scale $\Lambda $ -unit, a heuristic rule is proposed to divide the $\Lambda $ -unit into several small-scale production line segments, where an aggregation building block is established for each segment. Numerical studies indicate that IAM can effectively improve the estimation accuracy of the aggregation method while maintaining a reasonable computational efficiency. Note to Practitioners— Bottleneck machines refer to the machines whose performance impedes the production systems in the strongest manner. They have been widely utilized to improve productivity, energy efficiency, and so on. However, there are few efforts devoted to the impacts of bottlenecks on the aggregation method. The numerical studies indicate that the traditional aggregation method (TAM) has lower estimation accuracy in production lines with multiple bottlenecks. Therefore, in this article, improved aggregation method (IAM) is established to improve the performance of the aggregation method in the production lines. The research contributes to the literature with a generalized aggregation method that is very effective in production lines with and without bottlenecks. It can be utilized by production managers to evaluate production performance, predict the impacts of system changes, and accelerate control and investment decision-making.

In this study, a method for realizing an intelligent production process by reducing quality variation in the manufacturing industry is proposed. A quality fluctuation model in a production process is abstracted, and the quality is improved using adaptation rules based on the model. In this framework, the value directly related to product quality is expressed as multiplying the coefficient and the setting parameter. This expression makes it possible to regard the quality variations as being caused by the coefficient variations. Hence, it is possible to reduce the variation of quality by predicting the fluctuation of the coefficient from various data acquired from the production line and increasing or decreasing the setting parameter based on the predicted value. Moreover, the element description method is applied to predict the fluctuation of the coefficient. The element description method has the advantages of a model-based method whose physical meaning can be understood and the advantages of a database method applicable to an unknown system. Therefore, the mechanism of fluctuation can be abstracted and can be used as explicit knowledge. In this study, this framework is applied to reduce the variation in filling weight of the powder filling process and is demonstrated. As a result, the filling weight variation has been reduced by approximately 33%.

This study aims to identify and reduce waste in PT Rajapaksi Adya Perkasa manufacturing company. The analysis uses the concept of green productivity with Green Value Stream Mapping as the analysis model. Through the GVSM current state, this study calculates the amount of green waste in the production line including seven green waste. The result of this study shows an improvement to minimize the waste that occurs through the GVSM future state. GVSM technique increases the green productivity value from 1.12 to 1.81.

The aim of this project was to study the quality improvement after accomplishment of 7QC tools in a manufacturing practice of visors used in speedometer. The main motive of this project was used to detect the defects that occurred in the manufacturing procedure and analyzed using 7 QC tools. Moreover that, the new design has been planned for defect decline and improving the quality of the product in the production line. Observation throughout the production line is done. There are reasons which causes scratches in visors are continuous movement of visors in conveyor, improper storage in bin, improper holding of visor in EOL fixture and improper training to assembly operator. The data were then arranged back using Pareto Chart based on the frequency of the defects. This data were arranged from the uppermost defect to the lowly defect. The uppermost defect detected was under cook problem. Brainstorming session was completed with the workers and also with the manager to make the cause and effect diagram to details the problem. All probable causes are categories under 4 M which are Manpower, Machines, Materials, and Methods. According to the cause and effect diagram, the improvement was generated. The improvement was to fabricate visors without scratches. Introducing stopper in conveyor and also changing the storage bin is reduced the scratches in visors. All data were tabulated in form of graphs and tables. Percentage of defects reduce was also calculated.

In the current manufacturing world, the utilization of proper resource and making the process fool proofing plays a significant role. These challenges can be addressed with the aid of lean manufacturing tools and concepts. Lean manufacturing focuses on eliminating wastes in the entire manufacturing process. Eliminating waste along the whole value streams adds value to the process, product, and the whole supply chain. The project focuses on standardization and line balancing of the CNC machine cells. The foremost objective is met by implementing Standard Operating Procedure (SOP) and Continuous Improvement (Kaizen approach) to standardize the process. Time study has been done using stopwatch and video-based study for identifying the critical time elements. As a result, cycle time has been abridged. However, the production line seems to be unbalanced. To overcome the issue, line balancing is done between operations, which enable the production line to meet takt time and enhance productivity.

Nowadays many manufacturing companies try to improve the performance of their processes by including innovative available technologies such as collaborative robots. Collaborative robots are robots where no safety distance is necessary, through cooperation with human workers they can increase production speed. In this paper we consider the collaborative robot assignment combined with the job shop scheduling problem. To solve this problem, we propose a genetic algorithm with a biased random-key encoding. The objective function for the optimization is a weighted function that factors in production cost and makespan that should be minimized. We propose a special encoding of the solution: the assignment of cobots to workstations, the assignment of tasks to different workstations and the priority of tasks. The results show how much the weighted objective function can be decreased by the deployment of additional collaborative robots in a real-world production line. Additionally, the biased random-key encoded results are compared to typical integer encoded solution. With the biased random-key encoding, we were able to find better results than with the standard integer encoding.

Smart factories should be able to respond to catastrophic situations proactively, such as recalls caused by production line disruptions and equipment failures. Therefore, the necessity for predictive maintenance technology, such as fault detection or diagnosis of equipment has increased in recent years. In particular, predicting the faults of collaborative robots is becoming increasingly crucial because smart factories pursue efficient collaboration between humans and devices. However, collaborative robots have the characteristic of executing programmable motions designed by an operator, rather than performing fixed tasks. If existing fault diagnosis methods are applied to non–fixed programmable motions, problems arise in terms of setting absolute criteria for fault analysis, interpreting the meanings of detected values, and fault tracking or fault cause analysis. Therefore, we propose a method of programmable motion-fault detection by analyzing motion residuals to solve the three problems mentioned above. The proposed method can expand the fault diagnostic range of collaborative robots.

Respecting the on-time delivery (OTD) for manufacturing orders is mandatory. However, for non-JIT Batch & Queue Push-manufacturing systems, the compliance of OTD is not intrinsically guaranteed. As an OTD related manufacturing theory is largely missing it is crucial to understand and formalize the necessary conditions of OTD compliance for complex production environments for maximum exploitation of the production capacity. This paper evaluates the conditions of post-optimality while being OTD compliant for production systems, which are characterized by stochastic order rate and a deterministic product-mix. Instead of applying discrete event simulation to explore the real case-by-case order scheduling optimization for OTD compliance, a Cartesian approach is followed. This enables to define theoretically the solution space of order backlog for OTD, which contributes to developing further manufacturing theory. At the base stands the recently defined new concept of virtual manufacturing elasticity by reducing lead-time to increase virtually production capacity. The result has led to defining additional two corollaries to the OTD theorem, which sets up basic OTD theory. Apart from defining the post-optimal requirements to guarantee for orders at least a weak solution for OTD compliance, this paper reveals that for a deterministic product-mix a non-ergodic order arrival rate can be rescheduled into an ergodic order input rate to the shopfloor if the virtual elasticity T is large enough, hence the importance of having fast and flexible production lines.

Storage technologies such as lithium-ion batteries (LIB) are a key technology to enable emerging transportation as well as sustainable energy policies. The manufacturing of LIB cells is characterized by high scrap rates of up to 40 % in the industry and a high energy demand, leading to a high environmental impact and high costs. Furthermore, the LIB cells themselves and their manufacturing are highly complex systems with various quality parameters and complex material flows. However, with the rising demand for energy storage technologies, the production of LIB cells needs to become more environmentally friendly and more cost-effective. To achieve this, a better understanding and a higher transparency of the material and energy flows in the context of quality parameters is needed. This paper presents a gate-to-gate methodology for the evaluation of material and energy efficiency in context of quality parameters of LIB cell manufacturing. The implementation of the presented concept is based on data acquisition and a tracking and tracing system and tested at the pilot production line of the Battery LabFactory Braunschweig (BLB). The results of this assessment can be used as inputs for further sensitivity analysis in system assessment methodologies such as Life Cycle Assessment (LCA) or Life Cycle Costing (LCC) respectively Total Cost of Ownership (TCO).

Today, most industries use statistical quality control tools to improve quality and reduce the defective products and waste, but the high volume of data requires the help of a powerful tool to control processes. One of the objectives of the present study is to predict defective products and prevent their production using data mining tools due to the high power in data analysis and its predictive nature, which is less used in the industry. In this study, the statistical population of all parts produced in 2017 by Shabrun Company. The statistical sample is 2400 pieces of radiators that were randomly selected from the production line. In the operational phases of data mining, three decision tree algorithms were used: C&R Tree, Quest Tree and Chaid Tree. Using these algorithms, the most important criteria affecting quality control and rules leading to the quality of parts were determined. Comparative results showed that despite the validity of all three algorithms, the C&R Tree algorithm had the highest accuracy. Adherence to the rules resulting from the implementation of these algorithms has led to the detection and prevention of waste generation, which has increased efficiency and prevented the loss of time and cost in this production unit.

This paper addresses the problem of identifying a profit-maximizing schedule for jobs in a just-in-time production line, where the decision maker can adjust each job’s processing time, and thereby its quality level and the profit that can be gained from it. The system comprises two sequential machines with a transition period between them and a setup time after completion of each job on the second machine. We first construct an exact algorithm that maximizes the profit. Since this problem is NP-hard, we construct a fully polynomial time approximation scheme (FPTAS) to address it and evaluate its computational complexity.

Residence time limits for the intermediate products are commonly seen in production systems due to quality requirements. A part in a buffer typically needs to be scrapped or reworked if its residence time exceeds the maximum allowable residence time, while a part needs to keep waiting in the buffer if its residence time is less than the minimum required residence time. Such limits could be required at multiple stages of the production line, making it difficult to analyze both the steady-state and transient production system performance. The problem under study is formulated as a multistage geometric serial production line with residence time limits. To deal with its large state space, a two-machine-one-buffer subsystem isolated from a multistage geometric serial production line is first analyzed for both steady-state and transient performance. Furthermore, a novel aggregation method, including the steady-state analysis and transient analysis, is proposed to evaluate the overall system performance. The proposed aggregation method substantially reduces the complexity of the problem and makes the analysis of the problem tractable. Compared with the simulation, the aggregation method maintains high accuracy in estimating both steady-state and transient performance measures. Such a method provides quantitative tools for effective performance evaluation and prediction on the factory floor. Note to Practitioners —It is well known that potential quality problems may occur and production cost may increase if the residence time of intermediate products is left uncontrolled. The residence time limits become a concern, especially in the automotive industry, food industry, and semiconductor industry, where intermediate products stay in a stage susceptible to defects. The limits also apply to large scale additive manufacturing of thermoplastic polymers. The deposition of each layer is subject to lower and upper bounds of surface temperature, which, in practice, are equivalent to minimum required residence time limit and maximum allowable residence time limit, respectively. With residence time considered, the size of state space for the overall production system model will grow exponentially, as the system’s size increases, which brings tremendous challenges to evaluate such a system both in the short term and in the long run. In this article, we introduce a novel modeling approach for a multistage geometric serial production line with residence time limits and propose a method that drastically reduces the complexity of the system. Such a method provides a quantitative tool to effectively evaluate the performance of multistage geometric lines with residence time limits.

Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.

In a personalized and various production mode, the production line needs to be updated quickly to meet market demand. The Optimization of production line is taken as the object. To address the coupling problems, such as unreasonable production line layout, unbalanced process capability, inaccurate logistics distribution and unintelligent equipment testing, a method of flexible cellular manufacturing based on digital twin is put forward. Decoupling based on event mechanisms and multi-objective optimization will be used in the design of methods, which will be continuously optimized in the simulation and will eventually be validated. After the implementation of an air conditioner line, the production capacity increased by 58.3 %, the WIP decreased by 77.8 %, the balance rate of the production line increased by 25.2 %, and the per capita production capacity increased by 29.8 %. The number of operators decreased by 28.3 %. The results show that the optimization method of flexible cellular manufacturing based on digital twin has practical value and guiding significance to improve the efficiency of production line.

In production systems, digital twins must be always aligned with the real system to guarantee an effective decision making process in a continuously changing environment. To allow the alignment, digital models can be updated with process mining techniques through data collected by sensors. This paper addresses the issue of detecting changes in the production system by analyzing data collected from sensors. Using raw collected data, a procedure is proposed to compute and plot relevant system measures that could help change identification. Simulation is used to test the effectiveness of the procedure in a realistic medium size production line.

The main purpose of this study is to optimize the parameters of the technological process of manufacturing parts by embossing in Industry 4.0. A multifunctional queuing system in relation to production was used for the research. We took the example of a production line at the plant, which demonstrates the functionality of the technological process of production and processing of parts by embossing. We achieved the strategic goals of the research due to a complex combination of detailed studies of the parameters of the technological process by modeling. The method is implemented in the FlexSim simulation system with a real set of data. In the process of production of details, it is possible to use the equipment of various quality. Its analysis shows that the effectiveness of even the most modern technological line depends to a greater extent on the performance of its weakest element, unit or part. In particular, in the production of stamping parts the weakest element is a stamp. Research, modeling and calculation of its surfaces, in particular by the method of finite elements make it possible to identify problem areas of the stamp and strengthen them by calculating with using this method. A simulation model of a part of the production process is created and the stages of production are analyzed. We take into account that the implementation of such processes is accompanied by the formation of queues of parts or products that come from the manufacturer for further processing on the conveyor.

In this study, the learning-forgetting (L-F) effect is considered in a mixed-model sequencing problem to investigate its impact on makespan minimization. To this end, mathematical models of the learning and forgetting effects are modified in accordance with a mixed-model production environment, and the L-F functions for a serial workstation and multiple products are established. Subsequently, their impact on production is demonstrated via data experiments. The relationships between the learning effect, forgetting effect, product model combination, and makespan are also discussed based on the experimental results. The results show that the learning and forgetting functions can significantly affect the work time in the mathematical scheduling model and that a balanced product model combination and small MPS (minimum part set) batch can help to reduce the L-F effect.

We deal with the problem of cyclic scheduling tasks in the distributed manufacturing system with factories having the flow shop layout. The optimization aim is to minimize the cycle time. We have proposed the proprietary model and introduced some special properties of the problem, which allow us to find the cycle time for each given particular solution quickly. Since the formulated problem is NP-hard, we propose a metaheuristic algorithm based on the tabu search approach. The quality of the algorithm has been verified experimentally on common benchmarks and compared to other methods known in the literature, namely the scatter search and list scheduling. The problem also has a relation with the problem of balancing workloads of identical parallel production lines.

The use of the Digital Twin as a promising technology during the reconfiguration of automated systems enables to meet the challenges of increasing product diversity and shortening product life cycles in today's industry. The use of Digital Twins supports engineers in all phases of the life cycle of a production line. Over time, however, production facilities are often modified and improved, while at the same time the created models during the engineering process of the system remain unchanged and no longer correspond to the real facility. Manual updating of the positions in the digital layout is very time-consuming and therefore expensive. This paper presents a concept for the automatic update of the layout of a production line. In this concept, the positions of the robots and their active and passive peripheral devices are automatically positioned in a digital plant model using information from the current configuration of the robots in a production line. Therefore, engineers can use the resulting synchronized digital plant model, Digital Twin of the system, to further optimize or expand the production plant.

This study presents and discusses the possibility of using 3D printed polymer tools to achieve a flexible metal forming production line. An example is given of the design of a sheet metal part from tool printing to the actual forming and evaluation of the achieved precision of the formed part. The time spent by the engineer/operator during the full process cycle is documented and compared to the time it would take to make the corresponding tool by conventional metal machining. The time savings revealed in the paper are relevant for a low number of parts to be formed in a flexible manufacturing environment, while it is less relevant in mass production where metal forming is conventionally an attractive process. Besides the time invested by the engineer/operator, it is also important to keep the costs of the tools at a minimum in flexible manufacturing. Therefore, the paper also presents a comparison of the costs for printed tools as compared to conventional tools made from metal machining. This analysis considers the cost of raw materials, machine time and electricity consumption. The environmental aspects of metal forming are also considered, with the lubricating oils and greases typically used to improve frictional conditions being replaced by the polymers, which themselves have anti-friction properties. Finally, the recyclability of the tools is significant when they are only used to form a few parts. Different types of polymers and printing strategies are discussed in relation to recyclability and compared to the alternative metal tools.

The paper deals with an overview of industrial robot usage possibility in automotive industry which nowadays is the most important customer of industrial robotic market. The first part of paper describes the situation of industrial robot usage and offers an overview of application of robots in world industry. Later we deal with the most common applications of robots, especially in the automotive industry, as well as trends and perspectives on the future of automotive robotics. Finally, there was presented a model task from real industrial practice in form of specific project overview about robotized tightening of screws on the production line of car seats at the end of the article.

Don’t just predict problems – prescribe a solution: that’s the premise behind prescriptive maintenance (PsM). Better and more data, coupled with Artificial Intelligence, Simulation, and Optimization are the keys to unlocking the benefits of PsM. However, PsM and Production Planning and Control (PPC) functions in a Cyber-Physical Production Environment still need to be integrated from an application-oriented perspective. The paper proposes a framework that establishes the missing link between the PsM and PPC. A novel decision support prototype system for smart planning leveraging on the shop floor digital twin has been developed to explore the feasibility, challenges, and benefits of an integrated determination of production schedules and maintenance plans in a real Make-to-Order/Engineer-to-Order manufacturing environment.

Cycle time is one of the essential parameters that could lead to a significant improvement on the productivity of a manufacturing system. In the traditional manufacturing systems, cycle time for each station is recommended by the vendor and determined based on production line design and through repeated experiments and simulations. Once determined, the cycle time is typically unchangeable during the production operation. Nowadays, the development of smart manufacturing enables a flexible production operation, and the cycle time of each machine can be made changeable to quickly respond to market demand fluctuation and to further improve the system efficiency. To evaluate the performance of a serial production line with variable cycle time, a novel data-enabled manufacturing system model is developed, in which sensor data inputs are allowed and cycle times are modeled as input variables. In addition, an opportunity window concept, which has been studied in the previous study and has been treated as a real-time system status measurement, is extended to the variable cycle time scenario. Numerical case studies are presented to validate the fidelity of the data-enabled model and demonstrate the influence of variable cycle time of individual machines on the production line throughput and the opportunity windows.

Due to the technological advancement in Today’s manufacturing systems, a large amount of data is generated in different volume, velocity, and variety of kinds. Extracting information from these data and make a real-time decision is a big challenge to the current manufacturing systems. This study presents a novel model that converts the iFactory learning facility into a fully Industry 4.0 (I4.0) manufacturing system. To achieve this purpose, we utilized the cyber physical system (CPS) components and sensors, the Internet of Things (IoT), deep learning methods, and cloud computing to fully meet the I4.0 enablers. Cloud computing is utilised in two phases: (1) during the model training phase to hold a large amount of product image data collected from the inspection station, and (2) during the execution of the model. The core learning model is based on a convolutional neural network (CNN) that is trained from the captured product images in the production line to predict the defective items in the line. The model was initialized by Resnet method and optimized to improve the learning rate and reduced loss function. The supervised learning model achieved high accuracy prediction performance up to 96.75% in the real-time decision making process. The model was able to extract the feature map of the normal non-detective product and use it to improving the accuracy and reducing the traffic between iFactory station and the cloud server. The model exploits the parallel computing big-data framework to achieve a real time decision making. The model can be applied to the current system and adopted as with all it is functionalities for the newer systems.

In the context of an automated assembly line and flexible manufacturing, good layout planning of the production facilities aids in improving production efficiency and reducing production costs. The current research on the facility layout problems mainly focuses on the static ones with the fixed flow between facilities, including the parallel row ordering problem (PROP). Based on existing research on the PROP, in this study, we first investigate the dynamic parallel row ordering problem (DPROP), considering the flexibility of the production line. This study proposes a DPROP model and utilises it to verify some PROP examples less than 15 facilities and some global optimal solutions are achieved. In addition, the performance of the model in solving DPROP is verified on some new small-scale examples designed based on the PROP. Owing to the limited performance of the exact methods for DPROP, this study proposes a hybrid algorithm of harmony search and tabu search to solve large-scale problems, and this new algorithm performs better than some existing ones so that some new better solutions for the PROP examples are obtained. The algorithm is first utilised to solve the small-scale PROP and DPROP and the results are compared with an exact methods. Moreover, by evaluating the large-scale PROP between 30 and 70 facilities, the results are compared with those of the improved genetic algorithm and the population-based simulated annealing algorithm, thereby 28.8 % of the current optimal solutions are improved. Finally, the hybrid algorithm of harmony search and tabu search is utilised to solve the large-scale DPROP with 30 and 40 facilities, and some optimal solution sequences are presented.

This paper deals with new opportunities of Industry 4.0 applications for capacity planning on mixed-model assembly lines for industrialized housebuilding. We formulate a linear programming model for capacity planning on several parallel production lines and show how this model is fed with accurate data. The solution of the linear program in turn generates a detailed allocation of personnel and workload assignment for a given planning period. We discuss the planning approach and describe the implementation on a real world environment.

The plastic injection molding process, including thermoplastic injection, is nowadays used in several industrial processes, including in the automotive industry for bowden cables. This process makes possible to produce a wide range of products with different sizes, shapes, and for varying applications. During the years, many challenges emerged, which obliged the injection machine manufacturers to adapt and continuously innovate in their equipment, and made the design process of these equipment highly complex. This work aims to design a thermoplastic micro over injection molding station adapted to the current injection rates in a real company, in order to increase efficiency, and that can be implemented in automated production lines, reducing the number of operators. The equipment should be used in the production lines of bowden cables, which are used to command different systems inside the car. These cables can have zamak terminations covered with plastic, or terminations entirely made of plastic. These plastic parts of the cables are over-injected using polyethylene, polyamide with and without the addition of glass fibers, and polyacetal. As a result of this work, the designed injection machine has an estimated production rate of 7 injected cable ends in a maximum of 39 seconds, which is equivalent to 5.57 seconds per cable end.

The present work aims at improving the production line efficiency of a hemming unit by implementing three different line balancing techniques. For real time study, a hemming unit which involves 12 different work stations with manual execution was considered. The average actual cycle time at each station obtained through past data and the takt time for each station were compared using Yamazumi chart, which pertains to a line efficiency of 44% initially. It is found out that implementation of the line balancing techniques such as Kilbridge and Wester Column, Ranked Positional Weight, and Largest Candidate Rule significantly improved the line efficiency to 69%. The methods also established the possibility of reducing the number of work stations from 12 to 8. Out of the three methods, the Ranked Positional Weight method is found to be more effective for the present case, through Arena® simulation.

Published in IFAC PapersOnLine, 54(11):61:66, 2021; International audience; In order to optimize the maintenance operations and limit the impact of material wearing on the galvanizing coating quality and defects, this paper proposes a data-driven model to estimate the remaining useful life of a roller system immersed in a galvanizing bath. The proposed model comes from the Hidden Markov Model family and is trained in non-supervised mode. The pros and drawbacks are given, in conclusion, for a possible use in a control loop.

Many small and medium-sized manufacturing companies process various product types to respond different customer orders in a single production line. To improve their productivity, they often apply batch processing while considering various product types, constraints on batch sizes and setups, and due date of each order. This study introduces a batch scheduling heuristic for a production line with multiple product types and different due dates of each order. As the process times vary due to the different batch sizes and product types, a recursive equation is developed based on a flow line model to obtain the upper bound on the completion times with less computational complexity than full computation. The batch scheduling algorithm combines and schedules the orders with same product types into a batch to improve productivity, but within the constraints to match the due dates of the orders. The algorithm incorporates simple and intuitive principles for the purpose of being applied to small and medium companies. To test the algorithm, two case studies are introduced; a high pressure coolant (HPC) manufacturing line and a press process at a plate-type heat exchanger manufacturer. From the case studies, the developed algorithm provides significant improvements in setup frequency and thus convenience of workers and productivity, without violating due dates of each order.

Sanayide enerjinin verimliliği ve tasarruf edilmesi son derece önemlidir. Bu çalışmada özellikle sanayinde faaliyet gösteren bir gıda fabrikada enerji verimlilik incelemesi yapılmıştır. Belirlenen gıda işletmesine ait gofret üretim hattı incelenerek detaylı enerji ölçümleri yapılmış ve enerji verimliliği potansiyeli araştırılmıştır. Fabrikanın mevcut enerji durumu incelenerek enerji verimliliğin sağlanması ve sürekli olabilmesi için çeşitli öneriler yapılmıştır. Yapılan öneriler doğrultusunda doğru yalıtım sistemi uygulaması sonucu basit geri ödeme süresi 0,25 yıl olan uygulama ile %89 ısı kaybının azaltılabileceği belirlenmiştir. Ayrıca kazan sistemlerinde brülör ayarlaması ile kazan veriminin %5,2 arttırılabileceği, reküparatör kullanımı, ısı kayıp ve kaçaklarının önlenmesi yoluyla yıllık 3391,9 $ kazancın sağlanacağı belirlenmiştir. Enerji tüketimi ile oluşan CO2 salınımı hesaplanarak işletmenin son üç yıllık çevresel etkisi incelenmiştir. Bu çalışma sonuçlarının gıda endüstrisinde tüketilen enerjinin verimli kullanılmasına ve ülkemiz ekonomisine katkı sağlayacağı öngörülmektedir., The efficiency and saving of energy in the industry is extremely important. In this study, energy efficiency examination was carried out in a food factory operating especially in the industry. The wafer production line belonging to the designated food company was examined and detailed energy measurements were made and the energy efficiency potential was investigated. Various recommendations have been made to ensure energy efficiency and continuous energy efficiency by examining the current energy situation of the factory. In accordance with the recommendations made, it was determined that 89% heat loss can be reduced with the application of 0.25 years payback.period as a result of the application of the correct insulation system. In addition, it has been determined that boiler yield surged by 5.2% with burner adjustment in boiler systems, and $3391.9 annual earnings will be achieved through the prevention of a recuperator use, heat loss and leaks. The environmental impact of the enterprise over the last three years has been examined by calculating CO2 emissions caused by energy consumption. The results of this study are expected to contribute to the efficient use of energy consumed in the food industry and to our country's economy.