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The increase in human resource cost puts forward higher requirements for the optimization of home appliance manufacturing processes. This paper studied an integrated human resource optimization problem considering the human resource selection, learning effect, skills degradation effect, and parallel production lines. There are multiple different manufacturing tasks with different normal processing times. Human resources have different abilities and costs. The actual processing time of a task is determined by its normal processing time, position, and ability of the human resource. The objective is to minimize production time and the labor cost. To solve the studied problem, we first consider the case where the human resources have been selected and assigned to the production lines. Then, some structural properties are proposed and a heuristic is developed to arrange tasks on every single production line. Also, we derive a lower bound for the problem. Since the investigated problem is NP-hard, a Variable Neighborhood Search is designed to solve the problem in a reasonable time. Finally, computational experiments are conducted and the experimental results validate the performance of the proposed methods.

Studies on primary agricultural production in Slovakia aimed mainly at biological, economic, or market point of view and there is a lack of managerial approach providing a specific recommendation for farmers according to their production lines or planning process. As a reaction, we are providing a practically oriented study based on the selected livestock producer, which is aimed at statistical significances as an establishment for further recommendations for management and marketing of this entity. The paper aims to analyze the views and preferences of customers of the monitored company, which sells its meat and dairy products in its stores and verify the expected success of the planned expansion of the product lines. Regarding this, the online questionnaire study was conducted on a sample of 202 respondents. Realized statistical analysis based on the Kruskal-Wallis nonparametric test and subsequent Bonferroni post hoc correction signify that meat products, fresh meat, and slaughter specialties are the most favorite products among customers of the monitored company. Fresh milk, on the other hand, is not as attractive as a meal for them. However, selling it from vending machines placed out of the store would increase the sales. Dairy products of own production should be even more successful, as we assume they would be as interesting for customers as the currently expanding portfolio of meat products. The benefit of this study lies in the consideration of the practical use of the obtained results in the business practice of the monitored company. Regarding this, we are providing a set of recommendations, according to the planning of their product lines and connected marketing tools for their successful placement on the market.

Defect inspection is an essential part of ensuring the quality of industrial products. Deep learning has achieved great success in defect inspection when a large number of labeled samples are available. However, it is infeasible to collect and label numerous samples in many manufacturing processes. Meanwhile, deep learning methods cannot conform to the high defect recognition accuracy of strict production requirements when the labeled samples are scarce but varied. This paper proposed a novel convolutional neural network architecture and a semi-supervised learning strategy using soft pseudo labels and a mutual correction classifier to improve the defect inspection accuracy when labeled samples are scarce. The effectiveness of the proposed method is verified on a famous industrial defect inspection benchmark dataset and a practical dataset containing images collected from actual injection molding production lines. The results indicate that the proposed method achieves an accuracy of 99.03% on the benchmark defect dataset, which is approximately 13.2% higher than other methods when the training dataset contains only 45 labeled images and 135 unlabeled samples per category. The best accuracy on the benchmark dataset obtained by the proposed method reaches 99.72%. Besides, an average accuracy of 99.25% is achieved with only 20 labeled samples and 180 unlabeled samples per category in the practical defect inspection task. Visualization methods prove that the performance improvement comes from the proposed multiscale architecture and the semi-supervised learning strategy. The proposed method can be used in practical defect inspection applications of industrial manufacturing, such as steel rolling, welding, and injection molding.

The analytical evaluation of production system performance measures is a difficult task. Over the years, various methods have been developed to solve specific cases of very short production lines. However, formulae for estimating the mean production rate (throughput) are lacking. Recent developments in artificial intelligence simplify their use in the solution of symbolic regression problems. In this work, we use genetic programming (GP) to obtain approximate formulae for calculating the throughput of short reliable approximately balanced production lines, for which the processing times are exponentially distributed. A hybrid GPG the algorithms are quickly retrained to generate a new approximate formula.

A defect prevention is a part of manufacturing company practice. Paper proposes a formal approach for solving scheduling problems with unexpected events as extension of general frameworks for Zero-Defect Manufacturing (ZDM) strategy. ZDM aims to improve the process efficiency and the product quality while eliminating defects and minimizing process errors. However, most of ZDM applications focus on using the technological achievements of Industry 4.0 to detect and predict defects, forgetting to optimize the schedule on the production line. We propose formal method to create predictive-reactive schedule for problems with defect detection and repair. Our proposal is based on the formal Algebraic-Logical Meta-Model (ALMM). In particular, it uses the model switching method and combines defect detection, heuristics construction and decision support containing predictions of disturbances in the production process and enabling their prevention. Production defects are detected and repaired, and consequently, production delivers components without defects, and in the shortest possible time. Moreover, the collection and analysis of data related to the occurrence of disturbances in the production process helps the management board in making decisions based on analysis gathered and stored data. Thus, the proposed method includes strategies such as detection, repair, prediction and prevention for defect-free production. We illustrate the proposed method on the example of a flow-shop system with different types of product defect problem.

Based on the previous research on the production line automation, this paper carries out further research and further design and development on the basis of the original production line automation equipment. In this paper, the overall design of the automatic production line is carried out, and the various systems in the automatic production line are optimized, and the backward instruments are eliminated, and then some more advanced and convenient instruments are applied. Then, the hardware and software of the automatic production line are studied respectively, and the human-computer interaction module and real-time main control circuit module are re developed, and the electric shaft is applied to the automatic production line. Finally, the fuzzy PID controller of the stepping motor is designed. The experiment shows that the fuzzy PID control scheme is better than the traditional PID control scheme. After the rationalization of the system, the quality robustness of proactive planning is improved obviously. Then, the temperature of motorized spindle was tested.

[EN] This article presents a multi-objective formulation of the buffer allocation problem (BAP) in a serial-parallel production line, which aims to maximize the throughput rate and minimize the total cost of the allocation of buffers. Three case studies involving operating conditions are analyzed: reliable, unreliable and reprocesses. Process times, times between failures and repair times, consider distribution functions: Exponential, Normal and Weibull. The evaluation method used in this document implies simulation meta-models constructed from experiment designs and production line simulations. On the other hand, the optimization method implemented is a hybrid metaheuristic of Genetic Algorithms and Simulated Annealing. The results report the allocation of buffers in the case studies, their impact on the objectives and the computational efficiency of the proposed hybrid algorithm., [ES] Este artículo presenta una formulación multi-objetivo del problema de asignación del buffer (BAP, por sus siglas en inglés) en una línea de producción paralela en serie, que pretende maximizar la tasa promedio de producción y minimizar el costo total de la asignación de buffers. Se analizan tres casos de estudio que involucran condiciones de operación: confiables, no confiables y reprocesos. Los tiempos de proceso, tiempos entre fallas y tiempos de reparación, consideran funciones de distribución: Exponencial, Normal y Weibull. El método de evaluación empleado en este documento, implica meta-modelos de simulación construidos a partir de diseños de experimentos y simulaciones de la línea de producción; por su parte, el método de optimización implementado, es una metaheurística híbrida de Algoritmos Genéticos (AG) y Recocido Simulado (RS). Los resultados reportan la asignación de buffers en los casos de estudio, su impacto en los objetivos y la eficiencia computacional del algoritmo híbrido propuesto., Se agradece al Consejo Nacional de Ciencia y Tecnología (CONACYT) por el financiamiento de esta investigación con número de registro CVU: 375571; y al Tecnológico Nacional de México / Instituto Tecnológico de Celaya, por el apoyo brindado. Finalmente, un reconocimiento a Juana Cinthia Lizbeth Nava Torres, Rafael Paniagua Soto y Juan Pablo Gallardo Ochoa por su ayuda en la fase de programación.

Automate finishing processes is a global challenge in several industrial sectors. Concretely, when dealing with aero-engine components, only simple finishing processes are automated nowadays. Most of the high-added value components manufactured are finished hand working, using deburring and polishing manual techniques. The driver of the proposed work is to achieve the necessary knowledge to introduce in a production line a complete finishing process for automated robotic deburring applications with low machinability materials (Inconel 718 in this case-study) on aero-engine casings with complex geometries: extruded casting bosses, internal features, etc. For this purpose, a three-step methodology is presented and analysed, providing a feasible workflow combining visual inspection for part positioning and edge location, with multi-edge solid tools and flexible abrasive tools to automate finishing operations, taking into account all process singularities. Results show that, using correct techniques, processes and parameters, an automated finishing process reducing operating time can be implemented in production lines. Thanks are addressed to the HAZITEK program from the Department of Economic Development and Infrastructures of the Basque Government and from FEDER founds, related to the project with acronym FAKTORIA. Authors are also grateful to the Basque government group IT IT1337-19. Finally, thanks are owed to the Ministry of Mineco REF DPI2016-74845-R and PID2019-109340RB-I00.

This chapter focuses on the technological and scientific state of the art about information technology advances. It discusses the scientific improvements transforming the production lines and machines in intelligent systems following the logic of Industry 5.0. The chapter provides elements useful to comprehend how technologies can be implemented in flexible information architectures for innovative industrialization processes. The main flexible technologies are integrated in robotic systems. The chapter also provides different examples to comprehend how innovative tools, including artificial intelligence (AI), can be applied in a new production scenario. Intelligent systems are implemented by AI algorithms, applied also for the intelligent movements of robotic arms. The production process control is usually performed by image vision techniques and by Internet of Things sensors placed inside the machines or outside. Technological approaches able to transform the production into an auto‐adaptive system are horizontal, vertical, and end to end integration.

Real time production performance evaluation plays a vital role in diagnosing manufacturing system health status and achieving productivity improvements. However, most existing studies on system performance evaluation are based on steady state analysis and focused on the production system with fixed cycle time machines. The real-time performance evaluation for a manufacturing system with variable cycle time machines, although typical for a large number of realistic scenarios, has been mostly ignored. The development of smart manufacturing and increasingly available sensor data have provided unprecedented opportunities to carry out thorough analysis on the real-time performance of such complex systems. In this letter, we developed a data-enabled methodology to efficiently identify and predict the real-time permanent production loss for a serial production line with variable cycle time machines. The concept and evaluation method of opportunity window are introduced to facilitate the permanent production loss estimation. Numerical case studies are presented to demonstrate the effectiveness of the proposed methods for opportunity window evaluation and production loss identification.

Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments.

In production systems, the bottleneck is typically understood as the operation that affects the system performance in the strongest manner. Identification and mitigation of bottleneck are among the most important problems in production systems research and practice. The current literature offers numerous methods for the identification of production bottlenecks. Most of these methods focus on steady-state system behavior under constant parameters, and the corresponding mitigation approach is typically to prioritize the improvement of that particular operation. This applies when the improvement cannot be dynamically reallocated to other operations in the system, and the improvement is to be fixed to an operation for a long period of time. In this article, we attempt to study the bottleneck problem in a dynamic environment. Especially, we consider serial production lines with finite capacity buffers and the Bernoulli reliability machines and use a control-theoretic approach to formulate the bottleneck identification and mitigation problem as a state-based feedback control problem with the objective being the maximization of steady-state throughput. Computation formulas and procedures are developed to calculate the performance metrics of such systems with dynamic bottleneck control. Properties of the optimal control policies are investigated, and a computationally efficient algorithm is developed to obtain an effective real-time bottleneck indicator. Numerical experiments are used to demonstrate the efficacy of the proposed method, and a case study is presented to illustrate its application. Note to Practitioners —It is very common in manufacturing practice that operators, engineers, and managers need to make decisions on-the-fly to ensure efficient production operations. One of the key tasks involved is to identify real-time bottleneck (RTBN). This is typically carried out based on the practitioners’ intuition and experience. With the advances in sensing, computing, and communication technologies emerging in the Industry 4.0 era, manufacturers now have the capability to collect, process, and visualize an enormous amount of data in real time. This provides a foundation for more rigorous real-time production management and control. The research described in this article is intended to provide such a tool for RTBN identification in production lines and, thus, guide timely decision-making on the factory floor for smarter production activities.

The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionary algorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. Note to Practitioners —Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionary algorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problems.

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. Note to Practitioners —A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature.

This chapter provides important solutions about digitalized information and data monitoring of production lines, merging informatic with electronic aspects. The hardware solutions are combined with the software ones allowing big data analytics, augmented reality facilities, and predictive maintenance of production machines. According to the new Industry 5.0 facilities, the chapter also provides the details about electronic elements useful for the design of advanced information technology infrastructures, including circuit implementations describing feedback logics to apply improving intelligent production systems. A tool suitable to model production processes is the business process modeling. The chapter provides an overview of electronic logic and its possible implementation in neuron basic models. It describes the implementation of predictive maintenance in industries, by discussing a methodology to perform corrective actions avoiding machine failures and potential break down. Predictive maintenance is an important upgrade of the production quality assessment.

Traditionally, the objective of industrial production focuses on fast and low-cost production, regardless of resources and energy consumption (EC). However, in alignment with the UN Sustainable Development Goals (SDG), governments worldwide have proposed regulations to reduce resources and energy. In their production lines, an increasing number of companies are using collaborative robots (cobots). Cobots are programmed to accomplish their task as fast as possible, ignoring the robot's EC. This letter estimates the cobot EC from individual instructions of user-defined robot programs. Thus, the user has an additional design parameter to create energy-optimal programs. In the literature, current EC estimation models for manipulators are not reliable or have not been assessed to test the model's reliability. Our modeling methodology possesses three steps: motion planning, dynamic model, and EC model. Using cobots of different sizes (UR3e and UR10e) and loading, we collected over 55000 samples per case and trained the model to identify the model's unknown parameters. The model estimated the power consumption of a testing dataset with a maximum RMS error of 6 [W] - 3.85%. In the final experiment, the complete system was tested using a user-defined program composed of six instructions. The results showed an accurate estimation of the power profile with an RMS error of 2.39 [W] and 4.23 [W] for UR3e and UR10e.

This paper mainly introduces an improved PLC communication program based on PLC link network communication. Read and write corresponding production data at different time periods through the shared link area, and use link location soft elements as interactive handshake signal. The main station download module and slave station upload module are designed to complete the large range of data transmission interaction between master and slave stations, and the control system is realized in the automatic production line for drum brake pad processing.

Annotation. The article deals with the problem of automation of control of raw materials and finished products on the example of a chocolate production line. As you know, chocolate is a high-margin product. For its production, expensive raw materials are used, including beans, which are supplied from abroad. In the final cost of production, 65-80% is the cost of raw materials. For a medium-capacity chocolate production line, the cost of raw materials is estimated at several million rubles per day. The problem of controlling the consumption of raw materials and the output of finished products has a specific economic significance for the company and its shareholders. The experiment on the introduction of digitalization in production processes should be considered a promising direction of research. In conditions of shortage of personnel and high cost of costs, the introduction of a amortized system will minimize the human factor, eliminate additional staff workload, and not increase current production costs. As a result of the experiment, modern means of controlling raw materials and finished products were introduced into the production process, programmable logic controllers were used, databases were formed, and software for data analysis was configured. This made it possible to make the process more transparent for accounting, to ensure prompt access to data and to prevent excess overspending of expensive raw materials, to prevent unauthorized actions of the operator in a timely manner.

To be in the competitive industrial world, industries required high quality, speed in completing the required work, and commitment to the delivery dates. One of the most important issues in the field of production management is the job shop scheduling problem (JSSP). In this paper, the researchers tried to solve JSSP of factory by presenting a method to improve the factory's production. Job shop scheduling (JSS) is a suitable method for solving these types of problems, which aims to improve the production flow through minimizing the whole operation time of the products. Moreover, considering the factory that depends on workers as same as machine, human factor should be considered while scheduling by using the workers' weightage, in order to improve the workers' working time flexibility in terms of their waiting time among their tasks by proposed model of JSS. In addition, the researchers proposed a new combination of weightage values by using Taguchi method, regarding to improve the workers' working time and using genetic algorithm (GA) to solve the proposed model of JSS. One of the factories which is located in Jordan, and it is considered as one of the important factories; nevertheless, it can cover the local demands hardly, and hence, it deserves to be as a study case for this research. The findings of the studies decreased the whole operation time of the products by saving 75 min for each production line and 90 min by using GA, and the proposed model improved the flexibility of the workers' working time in terms of their waiting times among their tasks.

In the industry, production lines depend on small and medium-sized electrical machines, and for this reason, continuous monitoring of these machines is important to avoid unexpected interruptions. Excessive rise in temperature during electric motor operation is usually associated with several causes, including voltage unbalance and overload. This article presents the measurements taken using a Raman Distributed Temperature Sensing (RTDS) device with 1 m spatial resolution to identify induction motor (IM) faults. In this regard, an algorithm to improving spatial resolution through signal processing allows measuring lengths of up to 15 cm. Experimental tests showed that it was possible to identify the temperature increase in four slots with an improvement of 60%. In addition, it was possible to detect faults in conditions of unbalanced voltage through temperature measurements. The proposed method shows that the measurements acquired by the distributed sensors installed in the IM stator allow thermal mapping of the machine’s entire structure.

Production lines are the backbone of the manufacturing industry. To gain the best profit out of a line it is necessary to design each line using the production system engineering. Therefore, three approaches can be used, the numerical, the analytical, and the semi-analytical approach. The aggregation method, finite state method, and the numerical approach are statistically compared concerning the analytical approach using the STATISTICA software. We analyzed the interaction between the input data and the output data for the finite state method in an illustrative example, using a full factorial design and the Design Expert software

In order to meet the needs of customers in an Internet of Things (IoT) environment, the traditional manufacturing production strategy has gradually shifted from mass production to a small number of diverse forms. In traditional industry, when the production type changes to a small number of diverse forms, the complexity of scheduling increases and the rules of adaptability between products and production lines is not easy to judge. However, in traditional production management scheduling, the adaptability of production lines is mostly planned based on past experience. If the number of orders is too large or the production schedule changes, errors will increase. This situation will cause the actual production situation to be far removed from the planned result, which will affect the schedule achievement and delivery time. The present paper reports research using association rules to explore production lines and apply logic to solve the problem of production rules between production lines and products in the car manufacturing industry. The results show that the application of data mining association rules has an accuracy above 87%. The application of data mining can provide manufacturing production rules to assist managers to make better decisions in the IoT environment and to reduce the time required for manufacturing production.

Currently, the application of technical advances to production plays an important role in improving productivity and saving production costs. The techniques applying information technology bring high efficiency, accuracy, reliability, pre-assessment of the results. Modeling and simulation are method that are widely used from research, design, manufacturing to operate the systems. With the help of computers, with high computational speed and large memory, the modeling method was strongly developed, bringing great efficiency in research and production practice. The assessment of effective use of equipment in the production line has a decisive role in increasing the productivity and decreasing cost. Therefore, the analysis and evaluation of the production line by the simulation model method is highly practical, bringing many effects in the management and use of equipment. In this study, a simulation of a specific problem was performed to estimate the simulated workshop. Then, building a new plan and comparing the proposed plane with the original plan to provide the reasonable solutions for the production process to effectively use the equipment in the production line.

This article presents a methodology to formalize the behavior of the machines composing a production line, and to automatically generate their virtual prototypes for efficient and correct plant simulation. The approach exploits assume-guarantee reasoning through contracts to model the interaction between the different components of a production line. The approach is guided by a well-known taxonomy of industrial machines and associated manufacturing processes to identify each elementary action related to a specific machine. Contracts enable to build executable models of all the machines available in the production line by using automatic synthesis. The generated models can be integrated into a state-of-the-practice industrial plant simulation software to estimate and validate the production line's behavior. The presentation of the methodology is supported by a running example based on a real production line, showing the step-by-step application of the approach to a concrete scenario.

Cyber–physical system (CPS) is a complex system that contains multiple components working cooperatively. According to its characteristics, we propose an object-oriented generalized stochastic Petri net (OGSPN), in which the CPS is abstracted into several types of objects and its logical structure and working process is visually described. Moreover, we model and measure the time consumed by each activity in CPS for quantitative analysis. To simplify the process of performance analysis on this model, in this article we propose a compression algorithm to convert OGSPN into a generalized stochastic Petri net (GSPN). Considering the uncertainty in CPS, we use a fuzzy mathematics based method to process the compressed model of GSPN for improving the accuracy of the performance analysis. We apply our method to a real-world thick metal plate production line in a manufacturing company, and the availability of our method is verified by extensive experiments.

The constant changes in the world generate demands for improvements in processes, either by reducing costs or increasing capacity. One of the most used methods today for process optimization is Discrete Simulation. This research presents a discrete simulation application, using the Tecnomatix Plant Simulation software to simulate a production line in the Manaus Industrial Pole. Mathematical modeling made it possible to understand the parameters involved in the production process and worked as a guide for the production line's composition in the Plant Simulation environment. The production line modeled in Plant Simulation used real input data obtained in two months of production in 2020. The results obtained showed that the modeling reached the objective of virtualizing the production process, once that the differences between the simulation and the real process were at most 1.07%.

In the existing literature, extensive investigations focus on evaluating the performance of production systems that typically assumed to be operating under rigid production mode. Mass production as well as single type of products is the representative characteristics of this production mode. However, by applying prompt equipment adjustments and resets, modern production lines are capable of making different products in small batches with varying processing requirements. This is referred to as flexible manufacturing mode. With the flexible manufacturing mode, which has been widely used in recent years, real-time performance evaluation and prediction, small-batch-based real-time scheduling, and management of such systems, etc., are having important research significance. In this article, by considering adjustments and resets on serial production lines with multitype and small-batch-based production, the transient performance evaluation problem is studied. Specifically, for the system with machines having the Bernoulli reliability model and buffers having finite capacities, the mathematical model is formulated first. Then, we develop a Markovian model and a computational procedure for evaluating various system real-time performance indicators, such as production rates and completion times of different batch-based production tasks, etc. In addition, an order-reduced dynamic decoupling approach is proposed to approximate system real-time performance with high accuracy and computational efficiency. Finally, theoretic properties of the system under the flexible manufacturing mode are discussed.

Facility layout planning (FLP) has an important role in manufacturing industries. There are few approaches to solve FLP such as procedural, algorithmic, virtual reality (VR) based and augmented reality (AR) based approaches. This research is about AR assisted FLP (ARFLP) to visualize the arrangement of virtual models in real facilities environment in real-time. It can check the material travel distance and total area occupied by the facilities. In the ARFLP system, the virtual model design is flexible and can be modified easily to suit the actual environment. A single row layout production line was chosen as a case study with four common types of layout considered: straight line, W-shaped, U-shaped and V-shaped layouts. The best location for the new machine was suggested for each layout. ARFLP has proven to be able to check and visualize the position of a new machine to be added to the existing layout.

Interior design and construction (IDC) is a sophisticated and often prolonged process that delivers a building to occupation. Traditional practice is rather unproductive, involving the work of several different trades crowded in situ and delivered sequentially one after another. To enhance productivity in IDC, offsite practice is receiving increasing attention as a process innovation along with Design for Manufacture and Assembly (DfMA), an emerging concept in the industry. This paper aims to investigate offsite IDC practice and develop a set of DfMA enablers for better achieving this building process. It undertakes a literature review, case study, and 18 semi-structured interviews. To support the offsite IDC and its production line, standardized procedure, automated machinery, and supply chain, 10 DfMA enablers are adopted, such as early collaboration, design standardization and simplification, and light material selection. These findings indicate a paradigm shift not only in interior design methodology but also in IDC professional practice process. This research enriches the literature on DfMA and IDC, in particular their synergy, and offers a new model for interior designers and offsite IDC practitioners.

Industrial companies operate in increasingly competitive international environments; thus, they need to continuously innovate to improve their competitiveness, productivity, and quality. Digital transformation, which is one of the foundations of Industry 4.0, is essential to addressing these innovation challenges. The objective of this study is to present the methodology, development, and implementation of a new cloud computing platform that collects, stores, and processes data from shop floors. Manufacturing shop floors use connected, intelligent devices that produce thousands of data points that, once computed, provide a high added value. This study presents the architecture to collect this data, store it in a big data cloud computing solution, and then process it using advanced artificial intelligence algorithms and/or optimization techniques. The proposed platform has been developed to minimize the complexity and costs required to facilitate its adoption. The platform’s implementation and evaluation were conducted by two companies from two different sectors of the Brazilian industry. The objective of the first company was to diagnose production losses in a compressor production line. Using the developed solution, the company identified prospective changes in the layout and automation that could increase productivity by approximately 5%. The objective of the second company was to implement dynamic and optimized process planning in clothing manufacturing. The first assessment of the gains of the proposed solution exhibited an average productivity increase of 10.69% ± 1.82% (confidence interval).

Since today, most of the manufacturing companies have the operated CAD model, so CAD-based object autonomous bin picking which using 6DOF Manipulator may be a good option that can save time and increases productivity for an assembly line. This research aims to present an effectively autonomous method that can increases productivity as well as respond quickly of changing items based on customer demand for an assembly line which using 6DOF Manipulator by combining CAD data and computer vision system. Firstly, The 3D CAD model of grasped object is projected onto six different 2D planes, then combining six views to form the final pointcloud. Secondly, a voting scheme is used to estimated the 3D pose of object which is obtained by a 3D camera. For tuning a precision of an estimation such as surface normal, angle and location of an object, Iterative closest point (ICP) algorithm is applied. Before doing experiments, the recognition algorithm is verified through the simulation program. Through implement experiments, the system proved that it is stable, good precision and applicable in production line where mass product is produced. Moreover, the developed system allows non-expert users with basic knowledge about CAD drawing and image processing can generate a pose of an object from CAD model and transmit data to a manipulator for the bin picking task.

Recent advances in sensing, data analytics, and manufacturing technologies (e.g., 3-D printing, soft robotics, nanotechnologies, etc.) provide the potential to produce highly customized products by allowing flexible system design, endless device configurations, and unprecedented information flows. These opportunities also increase the complexity of controlling such systems optimally, which typically requires fast exploration of an increasingly large number of alternative operation strategies. Simulation and stochastic models have been particularly successful to support control and optimization of production systems, and methods have been developed to exploit them separately. Herein, we argue that the simultaneous use of these models can allow for better control and optimization by balancing the simulation accuracy, and related high computational costs, with the computational efficiency and lower accuracy of stochastic models. In this article, we assume that high fidelity models have higher accuracy and computational costs, and we present a novel multifidelity approach, which utilizes several models at different levels of fidelity to efficiently and effectively estimate and optimize the performance of asynchronous serial production lines with machines suffering from multiple failure types. Experimental results show that the multifidelity approach leads to better estimations, requiring less computational effort for optimization compared with the use of only high fidelity simulations.

In the context of new retail and personalized, small-batch, distributed collaborative production, orders arrive in real time, and each workshop needs to organize production lines based on suborders in real time under the constraints of smart contracts. However, the existing cloud centralized scheduling method has very high calculation and communication costs when inserting orders in real time, and the fully reactive edge scheduling method is difficult to meet the various order-level requirements of customers. Therefore, this article proposes a real-time edge scheduling model that considers real-time trial insertion of orders based on order-level requirements. Further, a real-time edge adjustment method to eliminate fluctuations is proposed. The proposed strategies were implemented using the lightest methods, avoiding cascading effects, to apply to the edge. Experimental results show that proposed strategies have significant advantages in order-level indicators such as customer satisfaction and have slightly better performance in workshop-level indicators such as resource utilization, energy consumption, and makespan.

The study deals with the topic of the implementation of modern production methods with emphasis on the solution of a new production with the utilization of lean principles and respecting the limiting conditions of the production company. In the summary, an economic assessment of the designed concept using the Lean Method is presented. The outcomes of the researched problem are a performed analysis, its acceptance of the user and the acceptance of the designed concept of the new production line.

Desde 2015 la Argentina cuenta con un Plan Nacional de Reducción de Pérdidas y Desperdicio de Alimentos (PDA) y, a partir de 2017, con una Red Nacional para la Reducción de Pérdidas y Desperdicio de Alimentos conformada por 90 contrapartes de diversos sectores y zonas del país. Para el sector de frutales de carozo en Argentina específicamente se estiman valores de PDA del 43% (con un promedio mundial para el sector hortícola y frutícola del 45%), con un 88% de esas pérdidas registradas en las etapas de producción primaria, poscosecha /almacenamiento y procesamiento/envasado (Cocaro, 2021). Para disminuir las pérdidas que se producen durante la cosecha y poscosecha de las cerezas es necesario aplicar tecnología apropiada para lograr fruta de calidad. Además, es importante implementar actividades y procesos que permitan reducir las PDA, entre los que se encuentran: prevenir/reducir en origen y reprocesar/obtener subproductos, algo que la Cooperativa realiza mediante la producción de cereza al marrasquino. Este trabajo se realizó en el marco del Convenio INTA-Cooperativa El Oasis (N°26805) y del Proyecto Estructural INTA I010 “Intensificación sostenible de las cadenas frutícolas”. EEA Santa Cruz Fil: San Martino, Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz. Agencia de Extensión Rural Los Antiguos; Argentina. Fil: Muñoz. Miriam. Cooperativa El Oasis Ltda. Los Antiguos. Santa Cruz; Argentina. Fil: Cura, Ena Yamila. Cooperativa El Oasis Ltda. Los Antiguos. Santa Cruz; Argentina.

Purpose: The main objective of this paper is to validate the results of the Lean manufacturing application via the DMAIC concept. This study concerns a case study carried out in an automotive company which aims at improving its flows within the production workshop by pulling the physical flow and minimizing the different wastes. Methodology/Approach: By conducting a literature review to examine the different frameworks for applying the lean method and to extract case studies related to the DMAIC approach which is missing on the selected articles, only one article that addresses this possibility. Findings: DMAIC has allowed a better structuring of the entire project, choosing the right improvement solutions with the right choice of Lean tools and several advantages that are not valid for other frameworks. This implementation shows a spectacular improvement in the production planning, the fluidity of the flow as well as an important financial gain for the company. Research Limitation/Implication: The project duration was not sufficient to apply other beneficial lean tools as the study was limited only to a single production line. Originality/Value of paper: This article demonstrates the added value of the structured DMAIC approach to lean manufacturing methodology and implementation.

The traceability of products in supply chains is crucial. The low-cost information collection system, tools, and transparent information proposed in this study were used to effectively manage and assign work on clothing production lines. In this system, each manufactured product was assigned a unique identification number. Information collection tools and collected data were continuously used in the supply chain. In clothing production lines, work is generally assigned based on the experience and intuition of production line team leaders rather than factual data. Therefore, when a production line team leader or worker leaves, management efficiency drops, which compromises production capacity. This study used information technology to develop a suitable management system to collect live production data and display the data on screens for analysis and interpretation. Consequently, new team leaders without experience can use the data provided to continue managing the production lines. The study results showed that the work assignment system converted team leaders’ experience into reliable data. In addition, all products maintained a consultable production history.

As the late processing of rice straw may consume the cost and pollute the environment, this paper put forward a method to use the rice straw, namely, the rice straw is used as the raw material for making seedling trays. The process of seedling tray production was designed, and the method of obtaining raw materials and steps of seedling tray production were introduced. In addition, the overall structure, transmission system and the forming mold of the pneumatic molding machine were also designed and produced. Next, the seedling trays were made by using the production line to analyze the characteristics of the rice straw seedling strays. The study was intended to explore the rules about the influence of moisture content on the characteristics of the seedling strays. As the moisture content of the seedling trays increased, the seedling trays expanded, increasing the volume, density and mass. However, the ultimate tension and shear force borne by the seedling stray decreased along with the increase of moisture content. It could be known from the analysis that: rice straw seedling trays could satisfy the needs of rice nursery. Compared with the traditional plastic seedling tray, the rice straw seedling tray can simplify the production process and provide natural nutrients for rice seedlings.

In the process of detecting materials inside a tray in an automated production line, it is necessary to detect not only the known materials and blank space in the designated area, but also the unknown materials misplaced inside the tray. However, the supervised detection algorithm based on deep learning can only detect the known and blank material areas. Therefore, this paper proposed a phased material detection. The first stage is to detect the tray and then identify the material area in the second stage. In order to improve the tray detection accuracy during the first stage under the condition of a high intersection ratio, an improved YOLOv5s tray detection method is proposed. The structure of YOLOv5s is improved using the SENet. This paper proposes to use the rich geometric information of the shallow network and the high‐level semantic information to integrate the bypass features. MAP@0.5:0.95 of the improved model increased from 95.7% to 96.6% and MAP@0.95 from 78.5% to 90.8%. The challenge of detecting unknown wrong materials on a tray can be resolved through the recognition of material area segmentation images processed by using the improved pre‐detection algorithm, together with the relative position reference between the material area and the tray. The experimental results showed that the improved method proposed meets the industrial detection requirements with an overall recognition accuracy of 91% within a 250 ms detection interval.

In the literature, the design approach has been mainly focused on products, however, manufacturing process awareness lacks structure measurements of the organization resources and their productive capacity to systematize the adoption of newer technologies and automation of production lines. Due to this fact, there is a remaining gap in the adoption of technological resources to automate them. The study of manufacturing processes has made it possible to identify three opportunity areas in any type of organization: i) structured processes, ii) industrial environmental knowledge and iii) technological adoption. This article presents a taxonomy that structure available information in manufacturing processes into five levels to characterize, redesign, or develop them from scratch. To get a better comprehension of the environment, it is possible to enhance awareness of energy consumption, waste management, information flow and emissions control through Sensors, collection, processing, and communication of information through Smart components. The technological adoption goes hand in hand with the sustainable objectives of the organization and dictates the search for alternatives throughout the development or redesign of the manufacturing process to pursue economic, social and environmental objectives. Besides, the taxonomy was used in the characterization and updating of four manufacturing processes for a modular machine. These four case studies allow for better insight into the design process. The changes between the initial and final stages after the update were recorded in the S3 Scorecard to visualize the increase of the characteristics and to chart a route for future technological acquisitions. The proposal has made it possible to locate the characteristics of the manufacturing process at one level and align the objectives of the organization to a technology acquisition plan since manufacturing firms are looking to future-proof their investments by acquiring solutions to comply current regulations while maintaining low-cost operations.