Typical Manufacturing System Research Articles

A Decision Support Model for Lean Supply Chain Management in City Multifloor Manufacturing Clusters

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning urban CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions.

Data-driven carbon emission accounting for manufacturing systems based on meta-carbon-emission block

In the context of carbon neutrality and sustainable manufacturing, there is an urgent need for carbon emission accounting in the manufacturing industry, especially in high-energy, low-efficiency, and high-carbon emission manufacturing systems. However, the dynamics and coupling of carbon emissions caused by the multiple characteristics of manufacturing systems increase the challenges of carbon emission accounting. To this end, this research aims to develop a generic methodology to reveal the carbon emission characteristics and implement carbon emission accounting for manufacturing systems, so as to support the lean diagnosis and management of carbon emission hotspots. Firstly, the concept of the “meta-carbon-emission block” consisting of both static and variable carbon emission blocks is proposed to characterize the carbon emission behaviors of each device in various operating states. Then, a data-driven carbon emission accounting approach based on the meta-carbon-emission block is presented to calculate the carbon emission and diagnose the carbon emission hotspots. Finally, a typical manufacturing system, that is, a laser welding system for butt welding of aluminum alloy 6061 with multi-source of the carbon emission, multi-device of the processing system, multi-state of the sub-device, and multi-stage of the production, is conducted to validate the proposed approach, and the results revealed the effectiveness and practicality of this approach. This research provides an appropriate theoretical and methodological basis for carbon emission accounting of manufacturing systems and also supports the diagnosis and reduction of carbon emissions for manufacturing industries.

Joint Design of Communication and Computing for Digital-Twin-Enabled Aircraft Final Assembly

Aircraft final assembly line (AFAL) is a typical complex manufacturing system with multiple installation and test processes operating simultaneously at each workstation. Lots of robots and sensors are connected and operated for heterogeneous processes by sharing limited communication and computing resources. How to manage devices and resources in a coordinated and efficient way is thus very challenging. Digital twin (DT) is a powerful technology for multiple objects management in the complex assembly system. It enables us to coordinate various devices and allocated communication and computing resources at workstations. In this paper, two main processes, i.e. vision-assisted installation and flight control system test, are considered in the AFAL. We introduce a DT-enabled AFAL system, and propose a DT-assisted heterogeneous processes coordinated (DT-HPC) framework to coordinate various devices and resources at each workstation. The wirelessly connected robots and sensors are applied for perception and information fusion. In order to minimize the total energy consumption and computing resources of all the wireless devices, joint design of the wireless channel allocation, transmission power and computing resource allocation are proposed to satisfy the diverse quality of service (QoS) requirements. Firstly, we propose a priority-aware channel assignment (PACA) algorithm to allocate channels for sensors and robots. Then, the optimal computing resource allocation strategy for two processes is derived while guaranteeing the processing latency requirements. Next, we derive the minimum transmission power of wireless sensors to guarantee the monitoring accuracy and calculate the transmission power of robots to obtain the satisfied transmission rate. Finally, we apply the DT-HPC framework in the DT-enabled AFAL system. The simulation results prove that our proposed algorithms can save energy while guaranteeing different QoS requirements.

Multi-level modeling and robustness evaluation of disturbances in intelligent workshop with temporal snapshot network

Disturbances occurred in the workshop production process disrupt the original production order, thus affecting production efficiency and production stability. Previous research focused on addressing status perception and operation control problem of intelligent workshop, but pays little attention on the formation process of disturbances and the evaluation of disturbances disruption effect on process completion time or manufacturing system robustness. It leads to difficulty in quantifying the destruction degree of disturbances and ensuring production robustness stability after being disrupted. To this end, a multi-level modeling and robustness evaluation method of disturbances are proposed. Firstly, a multi-level modeling method which quantifies disturbances disruption degree is presented by the fluctuation between the ideal level and the actual level of process completion time. Secondly, the evolution models of gradual disturbance and abrupt disturbance are constructed to analyze the correlation between disturbance evolution process and production robustness fluctuation. Then, a critical vertex identification algorithm is also presented to reveal the influence of machine importance on the disturbance destruction range. Finally, the production process of a typical manufacturing system is taken as a case study to illustrate the effectiveness of the proposed methods in quantifying the disturbance disruption degree and evaluating the robustness fluctuation of the manufacturing system.

Proposition of a software-assisted methodology to solve safety issues in reconfigurable assembly systems in a short time

ABSTRACT Reconfigurable manufacturing systems have been proposed over the last decades to deal with mass-customization problems and volatile markets. Issues regarding the physical design, control, or scheduling of these systems have been studied intensively but very few studies have investigated inherent safety problems. Yet this issue is crucial for reconfigurable assembly systems where manual and robotized workstations must cohabit. The present paper first presents the main safety issues inherent to this new type of manufacturing system. A methodological approach based on adaptable solutions (e.g. safety plans, movable barriers, and safety laser scanners) and comprising two phases (‘offline’ and ‘online’) is then proposed to address in a short time the previous issues. At each step of the ‘offline’ phase, dedicated software assists the safety manager (in charge of and responsible for safety) to design a ‘safe’ reconfigurable manufacturing system. Once this design is validated by the safety manager, the ‘online’ phase of the methodology is deployed.

A Two-Phase Iterative Mathematical Programming-Based Heuristic for a Flexible Job Shop Scheduling Problem with Transportation

In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry 4.0. In this article, a two-phase iterative mathematical programming-based heuristic is proposed to minimize makespan using a machine-operation assignment centric decomposition scheme. The first phase approximates the FJSPT through an augmented flexible job shop scheduling problem (FJSP + T) that reduces the solution space while serving as a heuristic in locating good machine-operation assignments. In the second phase, a job shop scheduling problem with transportation (JSPT) network is constructed from these assignments and solved for the makespan. Compared to prior JSPT implementations, the proposed JSPT model considers job pre-emption, which is instrumental in enabling this FJSPT implementation to outperform certain established benchmarks, confirming the importance of considering job pre-emption. Results indicate that the proposed approach is effective, robust, and competitive.

An Integrated Investment Scheduling Model for New Job Shops: A Phase-In Approach

The establishment and sustainability of the small-scale manufacturing sector is a great contributor to any nation's economic and technological development. A job shop is a typical small-scale manufacturing system that produces simple equipment and machine parts as well as technical services in developing nations. This type of manufacturing system requires versatile production machines for its activities. This requires enormous capital that most interested investors could not afford, and a majority of the commercial banks are not willing to provide financial assistance. This study develops a model using the theory of internal funding by employing the little initial capital in phasing- in the acquisition of the needed machinery, construction of factory space in modular forms, and adequate consideration for recurrent expenditure (i.e. working capital). The initial capital is periodically augmented from the ploughed-back earnings for the gradual acquisition of other machinery until a fully functional job shop emanates within the shortest possible time. Job shop general scheduling methods available in the literature could not be employed to solve the model developed in this study due to its peculiarities. Hence a special heuristic that employs suitable decision rules was formulated and solved using software developed for the purpose. The model developed in this study will be helpful to the investors in job shops, especially in developing nations, to overcome the problem of financing the venture.

On test case reduction for testing safety properties of manufacturing systems

This paper presents an approach to reduce the number of test cases, and hence testing time for the safe input-output conformance simulation relation (safe-IOCOS). The safe-IOCOS relation requires the implementation to be trace equivalent with respect to the specification only for traces composed of safety behaviors, which makes safe-IOCOS a suitable relation to test safety properties in practical settings. However, in typical manufacturing systems, multiple safety behaviors are typically associated with each nominal operation in the implementation. Thus, if safe-IOCOS is used industrially then testing for safety related faults becomes time consuming as the traces composed of same safety behaviors gets tested multiple times. This is possible either if the target states reached after the execution of traces have the same past behavior or the same future behavior. To remedy this, two reduction methods are proposed in this paper, subset construction and bisimulation equivalence. Both reduction methods preserve the traces of the system. Using both subset construction and bisimulation, a given specification can be maximally reduced and then used to implement the manufacturing system. The implementation based on a maximally reduced bisimilar specification allows the test engineer to omit test cases if the same safety behavior has already been tested. Furthermore, faults related to missing safety behaviors that are associated with multiple traces can be uncovered more efficiently compared to if the non-reduced specification is used for testing. To summarize, testing is a laborious problem, which can benefit from methods that enable reduction in testing time and makes the testing procedure efficient in terms of uncovering errors.

Consistent Round Hash optimized SRP-6a-based end-to-end mutual authentication for secure data transfer in industry 4.0

When the Internet of Things (IoT) is used in a typical manufacturing system, the industrial plant can be controlled remotely through the Internet. This enables manufacturing and execution systems to obtain real-time work orders directly from the Enterprise Resource Planning (ERP) system. Therefore, workflows for development, production, and manufacturing can be integrated with sales, market, and finance business processes. The possibility of implementing this integration, however, is dependent on the trust, security, and authentication of IoT devices. Many IoT devices face significant security risks such as device hijacking and data leaks due to limited resources and inadequate self-protection capabilities. Despite the fact that several studies have been conducted using the physical unclonable function to protect communication between IoT devices from the aforementioned security threats, current solutions rely on the participation of the server to distribute the key parameters, which requires high message overhead and has a significant impact on efficiency. To fill this gap, this article proposes a Consistent Round Hash optimized SRP-6a-based end-to-end mutual authentication for secure data transfer technique with single-share trusted device collaboration can detect an unauthenticated device. In addition, our proposed technique ensures the overall system's integrity and stability during a scaling-out phenomenon, which is becoming increasingly common in complex industrial environments. Furthermore, we present a formal and informal security analysis of the proposed protocol. According to the results of the performance analysis, our proposed technique has the lowest communication overhead, computational cost, and round-trip time when compared to other state-of-the-art schemes.

Izbor politike održavanja n-komponentnog sistema koji se može popraviti korišćenjem genetskog algoritma

A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n-component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.

Design of AGV Control Strategy in a Processing System

A typical manufacturing system requiring a high degree of automation usually includes warehouse, AGV, CNC, assembly line and laser marking equipment. Among them, AGV plays an important role on the overall operating quality and operating efficiency. This paper studies AGV control strategy under the background of the typical manufacturing system. Firstly the two modes of AGV motion path are defined; the flow chart of AGV movement between any two points in the system is analyzed, and the AGV communication module in the minimum operation mode is designed; finally, control programs for several main scenarios are proposed based on PLC ladder diagram. The outcome helps the optimization of AGV control decision relating to a bottom operation layer of any manufacturing system.

Similarity Transformation of Discrete Part Production Lines

In order to improve the computational efficiency of models of production lines with unreliable machines, we propose the similarity transformation method by adjusting the design parameters, such as times between machine failures, times to repair and buffer capacities, in an equal proportion. It is found that the system throughput remains almost unchanged under the similarity transformation. The typical manufacturing system, serial production line, is taken as an example to verify the proposed method. Theoretical analysis and numerical experiments have been performed to confirm the feasibility and effectiveness of the method. The results show that the throughput error is no more than 5% and the calculation time is much less than that of the original model. Therefore, it is helpful to apply the similarity transformation method to large production line models to reduce computational complexity.

Idle Duration Prediction for Manufacturing System Using a Gaussian Mixture Model Integrated Neural Network for Energy Efficiency Improvement

Manufacturing activities dominate the energy consumption and greenhouse emissions of the industrial sector. With the increasing concerns of greenhouse gas (GHG) emissions and climate change in recent years, the significance of the performance in terms of sustainability of manufacturing has been gradually recognized by both academia and industry. Various researches have been implemented to analyze, model, and reduce the energy consumption of manufacturing activities toward sustainable manufacturing. In a typical manufacturing system with multiple machines and buffers, the state of a certain machine is not only determined by the machine itself, but also the states of the adjacent machines and buffers. Therefore, machines may be in idle states due to nonincoming part from the upstream section of the manufacturing system or noncapacity to hold the delivered part to the downstream section of the manufacturing system. Those idle machines consume energy without production if there is no appropriate energy control strategy. In this article, we focus on the reduction of the energy waste for those idle machines in a typical multi-machine and multi-buffer manufacturing system. A Gaussian mixture model (GMM) integrated neural network is proposed to predict the duration of the idle periods for the idle machines, during which optimal energy control action can be identified and implemented under the constraint of production throughput of the manufacturing system. A manufacturing system simulator is built to provide the training dataset including the information, such as production throughput, energy consumption, buffer content, and failure rate, to the proposed neural network. A numerical case study for a five-machine-and-four-buffer manufacturing system is conducted to validate the effectiveness of the proposed prediction model in terms of the energy waste reduction for the idle machines. Note to Practitioners —This article proposes a prediction model to forecast the idle duration of the manufacturing machines in a typical multi-machine and multi-buffer manufacturing system. With this predicted result, two concerns in energy control for the idle machine, i.e., throughput protection and energy consumption reduction, can be more accurately modeled in decision-making procedure. Optimal energy control actions under the constraints of throughput maintaining and energy saving can be identified and implemented considering different warmup energy consumption and warmup time of the machines to reduce the energy waste for those machines in idle states without any production and thus, improve the energy efficiency of the entire manufacturing system.

Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management

With advances in smart grid technologies, demand response has played a major role in improving the reliability of grids and reduce the cost for customers. Implementing the demand response scheme for industry is more necessary than for other sectors, because its energy consumption is often considered the largest. This paper proposes a multi-agent deep reinforcement learning based demand response scheme for energy management of discrete manufacturing systems. In this regard, the industrial manufacturing system is initially formulated as a partially-observable Markov game; then, a multi-agent deep deterministic policy gradient algorithm is adopted to obtain the optimal schedule for different machines. A typical lithium-ion battery assembly manufacturing system is used to demonstrate the effectiveness of the proposed scheme. Simulation results show that the presented demand response algorithm can minimize electricity costs and maintain production tasks, as compared to a benchmark without demand response. Moreover, the performance of the multi-agent deep reinforcement learning approach against a mathematical model method is investigated.

Flexible energy load identification in intelligent manufacturing for demand response using a neural network integrated particle swarm optimization

Various demand response programs have been widely established by many utility companies as a critical load management tool to balance the demand and supply for the enhancement of power system stability in smart grid. While participating in these demand response programs, manufacturers need to develop their optimal demand response strategies so that their energy loads can be shifted successfully according to the request of the grid to achieve the lowest energy cost without any loss of production. In this paper, the flexibility of the electricity load from manufacturing systems is introduced. A binary integer mathematical model is developed to identify the flexible loads, their degree of flexibility, and corresponding optimal production schedule as well as the power consumption profiles to ensure the optimal participation of the manufacturers in the demand response programs. A neural network integrated particle swarm optimization algorithm, in which the learning rates of the particle swarm optimization algorithm are predicted by a trained neural network based on the improvement of the fitness values between two successive iterations, is proposed to find the near optimal solution of the formulated model. A numerical case study on a typical manufacturing system is conducted to illustrate the effectiveness of the proposed model as well as the solution approach.

Speeding up computational times in simheuristics combining genetic algorithms with discrete-Event simulation

Many real-life systems in production and transportation logistics are complex, large-scale, and stochastic in nature. As a consequence, simheuristic approaches – which integrate simulation inside a metaheuristic framework – are becoming increasingly popular in the optimization and simulation communities. In a simheuristic algorithm, time-consuming simulation runs are required in order to: (i) obtain accurate results on the stochastic performance of solutions generated by the metaheuristic; and (ii) provide feedback that can be useful to better guide the metaheuristic search. If the underlying system is complex, discrete-event simulation might be needed, and then the simulation component could easily overrun the computational time of the metaheuristic component. Thus, for each new solution generated by the metaheuristic, several related questions arise: (i) should the simulation component be applied to that solution? – i.e., is that solution ‘promising’ enough to invest additional computational time on retrieving information about its performance in a stochastic environment?; and (ii) if so, how many simulation runs are needed in order to obtain useful information (i.e., statistics with a minimum level of accuracy)? This paper discusses these issues and proposes several concepts that allow to improve the efficiency (in terms of computational time) of simheuristic algorithms. A case study, based on a typical manufacturing system, is introduced. In order to illustrate and test different speeding-up techniques, the system is optimized by using a simheuristic that integrates a genetic algorithm with discrete-event simulation.

System efficient integration of standby control and heat pump storage systems in manufacturing processes

Prerequisite for system efficiency towards an industrial energy transition is the reducing of energy demand on the process level. In typical manufacturing systems with machine tools and washing machines, the proper design of intelligent standby control and heat pump storage system (HPS) represent high efficiency. The integration of HPS is complicated due to high non-continuity, especially when implementing a standby control system. Our approach aims at designing one single HPS for multiple heat sources and sinks. Robust design should consider the various influencing material flow system factors. For the generation of stochastic heating and cooling demand sum curves, 512 Design of Experiments-based material flow simulations for each of three standby scenarios have been conducted. These curves serve as input data for HPS sizing and dynamic thermal system simulation. The combined integration of an HPS and a practical standby control system offers the best compromise in terms of system efficiency with significantly lower investment costs and only slightly lower energy savings than ideal standby operation. Compared to the initial state, the electrical energy demand of the machines can be reduced by 27% and both the heating (83%) and cooling (48%) demand can be efficiently covered by HPs.

Dynamic Reconfiguration and Adaptation of Manufacturing Systems Using SOSJ Framework

One of the key challenges in modern manufacturing systems is how to dynamically reconfigure software behaviors that govern machines to reflect changes in physical manufacturing process without completely resetting the entire manufacturing operation. The existing software solutions used to describe software behaviors in manufacturing systems are typically not based on formal semantics and model of computation and have limited capabilities in handling dynamic adaptation/reconfiguration. This paper presents the Service-Oriented SystemJ (SOSJ) framework that supports a new programming paradigm for designing dynamic distributed manufacturing systems. SOSJ combines the system-level language SystemJ and service-oriented architecture (SOA) paradigm to take advantages of both SystemJ's correct-by-construction formal semantics and SOA's dynamic features, respectively. The paper describes the concepts and functionalities of SOSJ, which enable dynamic reconfiguration of a typical manufacturing system. Performance benchmarks are run to compare the capabilities of SOSJ to a multiagent system framework JADE.

Energy modeling and a method for reducing energy loss due to cutting load during machining operations

In a typical part manufacturing system, machining operations represent a major proportion of the total energy consumption. The energy consumption (in the form of electricity power) of a machining operation can be divided into four types, that is, standby power, operational power, cutting power and power loss due to cutting load. Power loss due to cutting load includes the power loss caused by the friction of mechanical transmission and the power lost in the motor when the cutting load is applied to the spindle system. While the first three types of power consumption have been studied intensively by previous researchers, the power loss due to cutting load, which accounts for up to 20% of the cutting power consumption during machining operations, has received relatively less attention. This article proposes a novel model to characterize power loss due to cutting load, in which the power lost in the mechanical transmission and in the spindle motor are analyzed and modeled separately. Cutting tests have been carried out to validate the proposed model using two numerical control lathe machines. And a method has been developed for reducing energy loss caused by cutting load, which includes cutting force prediction, power loss due to cutting load prediction and decision making. The method was evaluated through its application in the process design for a shaft part, and the results show a significant saving of up to 70.8% of energy loss caused by cutting load.

Communication mechanism of the discrete event simulation and the mechanical project softwares for manufacturing systems

Abstract This paper presents an integration mechanism for online communication between a discrete event simulation (DES) software and a system dynamics (SD) software. The integration between them allowed executing a hybrid and broader simulation, in which the complexity of the systems and their multi-faceted relationships may demand the combination of different simulation methods and the synergies between the techniques. The Ururau free and open-source software (FOSS) was applied to implement the DES model. In order to build the dynamic model, we used the software for mechanical design called CAD 3D Software Inventor®. Besides, we also employed the DES model in the test step of a control system in real time. The results of that mechanism implementation enabled the evaluation of different aspects of a typical manufacturing system. Furthermore, the integration between the control system and the DES model allowed validating the logic of the programmable logic controller (PLC). Highlights Mechanism for online communication between a discrete event simulation (DES) software and a system dynamics (SD) software. A free and open-source software (FOSS) was applied to implement the DES model. The results of that mechanism implementation enabled the evaluation of different aspects of a typical manufacturing system.