Discrete Manufacturing Research Articles

Rapid construction method of equipment model for discrete manufacturing digital twin workshop system

A large-scale manufacturing workshop includes a wide variety and a large amount of equipment. When equipment is mapped as a digital twin (DT) model, the DT model has complex and implicit or non-hierarchical relationships, which makes the development of a DT workshop system (DTWS) for a manufacturing plant challenging. To address this problem, this paper proposes a rapid construction method of equipment model (RCMEM) for a discrete manufacturing DT workshop system, which can meet the requirements of complex manufacturing business scenarios, improve the efficiency and quality of the DT model construction at the equipment level, and form an efficient equipment model construction method. First, a DT object (DTO), which represents a basic unit of the DT model, is defined. The DTO is used to derive the DT model at the equipment level. The DTO collects and wraps generalization-capable functions from equipment into components, and geometric, functional, and communicative descriptions are combined into a single model. The DT model can increase the scalability, reusability, and modeling quality by reusing the DTO capabilities. Further, a general construction process of the DTO model of multi-axis computer numerical control (CNC) machines is presented. This process standardizes the common capabilities of the equipment models and minimizes the construction time of machines’ DT models. Furthermore, a rapid mounting and communication configuration mechanism (RMCCM) is proposed. This mechanism enables fast DTO network communication and improves interoperability in a multi-source data environment. This paper also proposes a DTWorks Software development kit, which can help developers to build digital factory standalone applications. The RCMEM is implemented into the DTWorks. Finally, a real DTWS case is used to examine the differences in equipment model building before and after the application of the RCMEM. The results demonstrate the good effectiveness of the RCMEM in complex business settings.

Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning

In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.

Reusing and Extending Standards-Based Unit Manufacturing Process Models for Characterizing Sustainability Performance

Abstract Over the past two decades, numerous efforts have characterized manufacturing processes for sustainability performance. These efforts have been pursued primarily by manufacturing researchers in academic and governmental labs, and involve the development of frameworks, methodologies, and standards for characterizing discrete manufacturing processes and their representation as information models. Furthermore, characterization of sustainability performance of manufacturing process flows has been attempted through linking, or composing, these unit manufacturing process (UMP) models. This paper reviews these efforts and identifies existing research gaps that should be addressed by academic, industrial, and governmental researchers. The review includes the relevant sustainable manufacturing standards that have been recently published by ASTM International. A methodology for creating and extending composable models of UMPs that build upon these standards is presented. This research demonstrates how formalization of these prior efforts can address the identified gaps. It is shown that the reuse of UMP models can be enabled by encapsulating specific characteristics of complex processes into information models that can be applied for detailed process analysis and evaluation. This research proposes the concept of a template UMP information model, which can further be abstracted and customized to represent an application-specific, higher-order manufacturing process model. The template model concept is illustrated for manual and computer numerically controlled (CNC) milling processes.

Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production

The multilevel capacitated lot-sizing problem (MLCLSP) is a vital theoretical problem of production planning in discrete manufacturing. An improved algorithm based on the genetic algorithm (GA) is proposed to solve the MLCLSP. Based on the solution results, the distribution of energy consumption in a synchronous production case is analyzed. In the related literature, the GA has become a much-discussed topic in solving these kinds of problems. Although the standard GA can make up for the defects of the traditional algorithm, it will lead to the problems of unstable solution results and easy local convergence. For these reasons, this research presents an adaptive genetic algorithm based on fuzzy theory (fuzzy-GA) to solve the MLCLSP. Firstly, the solving process of the MLCLSP with the fuzzy-GA is described in detail, where algorithms for key technologies such as the capacity constraint algorithm and the algorithm of solving fitness value are developed. Secondly, the auto-encoding of decision variables for MLCLSPs is studied; within this, the decision variables of whether to produce or not are encoded into a hierarchical structure based on the bill of material; combined with external demand, the decision variables of lot-sizing are constructed. Thirdly, the adaptive optimization process of parameters of the GA for the MLCLSP based on fuzzy theory is expounded, in which membership function, fuzzy rule, and defuzzification of the MLCLSP is mainly presented. Experimental studies using the processed dataset collected from a synchronizer manufacturer have demonstrated the merits of the proposed approach, in which the energy consumption distribution of the optimized production plan is given. The optimal lot-sizing is closer to the average value of the optimal value compared with the standard GA, which indicates that the proposed fuzzy-GA approach has better convergence and stability.

Ball-end milling cutter design method towards the maximum material removal rate under surface roughness constraints

Cutter design and selection, as one of the indispensable tasks in discrete parts' manufacturing process planning, has seldom been investigated in terms of high machining quality and machining efficiency. In order to fill this gap, this paper studies the influences of main geometric parameters of ball-end cutter (helix angle, flute number, cutter radius) on surface roughness and material removal rate (MRR). Firstly, it is revealed that the fundamental reason why the helix angle affects the cutting force coefficients is the oblique angle, not the axial height or axial position angle. And a novel method of cutting force coefficients prediction for newly designed cutter based on local helix angle is proposed. Then, the influences of cutter parameters on surface roughness and MRR are investigated by theoretical and experimental method. Based on this analysis, cutter evaluation models for machining quality and machining efficiency are established, respectively. Particle swarm optimization (PSO) is used as optimization method to search the optimal cutter. Finally, a novel cutter design method combined with cutter evaluation models is proposed to obtain better surface performance and machining efficiency. According to the experimental results, it can be proved that the cutter designed by this method can maximize MRR under the constraint of meeting the parts' surface roughness requirements. This is of great significance for improve parts' performance and reduce machining cost.

Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System

Bottleneck identification is of great interest in discrete manufacturing fields, as they limit the system’s throughput. However, the bottlenecks are difficult to accurately identify due to the instability and complexity of discrete manufacturing systems. This paper proposes a dynamic bottleneck identification method (DBI-BS) that is based on effective buffers and fine-grained machine states to identify bottlenecks accurately. First, the complex manufacturing system (CMS) with strong coupling between elements is decoupled into several independent parts under the guidance of the effective buffer theory. Then, the machine activity duration method is improved through further fine-grained division, and the machine states are described by the timing flow model. The method to quantify the degree of bottleneck that restricts the system throughput (TH) is proposed on the basis of the turning point theory, and the one-to-one mapping relationship between the simulated and authentic complex manufacturing systems is also studied. Simulation results show that the DBI-BS can effectively identify dynamic bottlenecks in complex manufacturing processes, and the decoupling of complex systems can effectively improve the accuracy of dynamic bottleneck identification.

Average reward adjusted deep reinforcement learning for order release planning in manufacturing

One of the key challenges in production planning, especially in discrete manufacturing, is to determine when to release which orders to the shop floor. The major aim of this planning task is to balance Work-In-Process (WIP) and utilisation levels together with timely completion of orders. The two most crucial attributes of production planning are (i) the highly nonlinear relationship between WIP, flow times and output, and (ii) the dynamically changing environment. Nonetheless, most state-of-the-art models use static lead times to address this problem. Only recently, some papers set lead times dynamically based on the flow time forecasts to react to the dynamic operational characteristics reporting promising results. This paper contributes to this line of research by presenting an order release model that uses reinforcement learning (RL) to set lead times dynamically over time. The applied RL agent is especially designed for processes with periodic feedback and highly variable context. We compare the performance of our new RL algorithm to static order release models and state-of-the-art deep Q-Learning agents by using a multi-stage, multi-product flow-shop simulation model. The results show that, especially for scenarios with high utilisation, our proposed method outperforms the other approaches.

Dynamic Monitoring Method of Enterprise Power Consumption Based on Energy Big Data

With the global shortage of resources and energy and the intensification of environmental pollution, the problems of energy consumption and pollution emission in the manufacturing industry have become increasingly prominent. Green development, quality improvement and efficiency increase have gradually become an important development trend of the manufacturing industry. Green manufacturing engineering must be vigorously constructed and developed. Discrete manufacturing is characterized by discontinuous processes. The manufacturing process is accompanied by a large amount of primary energy consumption and environmental emissions. Compared with process manufacturing, discrete manufacturing process control is more complex and changeable, its green development level needs to be improved, and the implementation process is more difficult. As one of the common workshops in discrete manufacturing, NC workshop has problems such as high energy consumption. Based on the above reasons, this paper takes the energy-saving optimization and energy management of discrete manufacturing enterprises as the research goal, and carries out the research on energy-saving optimization and energy management system for NC workshop. It is of great significance to reduce the energy consumption of NC workshop, improve the energy utilization efficiency, solve the pain points of many enterprise information islands and difficult to control energy consumption, and improve the green level of production process in discrete manufacturing enterprises.

Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems

Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.

A New MINLP Continuous Time Formulation for Scheduling Optimization of Oil Refinery with Unreliable CDUs

Short-term scheduling of oil refinery operations is a complicated optimization problem that requires a high level of detail and needs efficient approaches and software tools. Oil refineries are naturally categorized as continuous process industries due to their production processes, which have essentially different characteristics and constraints from discrete manufacturing processes. It is a significant problem to design effective approaches for oil refinery scheduling optimization. Therefore, this paper aims to provide solutions to the above very challenging problem by proposing a novel methodology. The problem is how to optimize crude oil unloading to charging tanks and charging schedules for different types of crude oils to distillation units (CDUs). Several realistic operational features are considered, such as different arrival times for crude oils, continuous operation of CDUs, no rework of crude oils, and the ability to perform preventative maintenance on CDUs. To solve this problem effectively, a new mixed-integer nonlinear programming (MINLP) model is developed. Industrial case studies are used to demonstrate the effectiveness of the proposed formulation. The computational results show that the case studies are effectively solved with the proposed solution approach.

Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework

Industry 4.0 (I4.0), through the digitalization and interconnection of manufacturing processes, can offer opportunities to improve production systems' sustainability. Despite the increasing number of scientific review papers related to I4.0 and production sustainability, most approaches and tools for sustainability evaluation lack of a tangible implementation framework.The paper presents a framework that originated from the plant metabolism concept, a simplified version of industrial metabolism. It is based on Energy Material Flow Analysis (EMFA) and Life Cycle Assessment (LCA) tools for production plants' economic and sustainability assessment, using the I4.0 enabling technologies. A Multi-Criteria Decision Making (MCDM) method combines the two sustainability pillars for aiding companies in optimizing their production processes towards a reduction of energy/material flows. The combination of EMFA, LCA and MCDM tools into a plant metabolism-based model is the main novelty of this paper.The framework consists of three main phases. The first phase allows to model the manufacturing system by defining the plant layout, the assets, and the input/output flows. The second phase allows gathering information from the manufacturing plant to assess environmental and economic Key Performance Indicators (KPIs) following the LCA principles. The third phase consists of post-processing results, minimizing specific KPIs for establishing the optimal production scenario.A washing machine plant has been chosen as a case study to demonstrate the proposed method's capability in authentic contexts. Besides, the effectiveness in supporting companies in the analysis, identifying criticalities, and the proper energy and material flows management of production plants has been verified. Plant managers could use this framework for managing the production plans. From the scientific standpoint, the proposed method positively contributes to integrating the existing state of the art studies concerning the I4.0-related framework for the sustainability assessment and energy/material flows minimization of production systems.

CI-Block: A Blockchain System for Information Management of Collaborative Innovation

Blockchain technology ensures the security of cross-organizational data sharing in the process of collaborative innovation. It drives the development of collaborative innovation in discrete manufacturing to intelligent innovation. However, collaborative innovation is a multi-role, networked, and open resource-sharing process. Therefore, it is easy to form information barriers and increase the risk of cooperation between organizations. In this paper, we firstly analyze the blockchain-based information management models in the traditional discrete manufacturing collaborative innovation process. Then, we found that in the process of industry-university-research (IUR) collaborative innovation, consensus servers maintain too many connections due to the high latency between them, which leads to lower consensus performance and efficiency. To solve this problem, we proposed the ependency nalysis (DA) model, which separates and assembles transactional data and non-transactional data into different blocks by analyzing read_set and write_set of transactions. Besides, we propose the ut-of-rder (OORaft) model to allow non-transactional blocks to be copied in parallel, which can also prioritize transactional blocks. Finally, we implement the blockchain model in the discrete manufacturing scenario based on Hyperledger Fabric. The experimental results show that our model improves the transactions per second (TPS) performance of 1.9X-3.7X and reduces transactional data committing latency by more than 40% in the target scenario.

Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process

Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep autoencoder-based monitoring offers huge potential in enhancing the performance of existing Multivariate Statistical Process Control approaches. We investigate the application of deep auto encoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling’s T2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results.

Use cases of the platform for structuring a smart supply chain in discrete manufacturing

It has been proposed a method of constructing a supply chain in the cyber space as a multi-agent system by linking enterprise agents which is generated from each model of the component enterprise in a supply chain, by authors. Based on the proposed method, a prototype of the CPS platform for smart supply chain configuration has been implemented for discrete manufacturing. By using this platform, it is possible to register models of retailer, manufacturer and supplier enterprises with different behavior types, as an enterprise e-catalog. Furthermore, it is also possible to configure various virtual supply chains by changing the combination of enterprises. On the configured supply chain, it can simulate the behavior from the viewpoints of each enterprise and an entire supply chain. Several use cases of the platform have been executed. These use cases and their considerations are provided useful findings towards construction of a further practical CPS platform for a smart supply chain configuration.

Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing

Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing

Paths for the Digital Transformation and Intelligent Upgrade of China's Discrete Manufacturing Industry

Paths for the Digital Transformation and Intelligent Upgrade of China's Discrete Manufacturing Industry

An advanced model for solving industrial scheduling problems

This paper summarizes an advanced model and a practiceoriented approach to solve scheduling problems of discrete manufacturing systems. An advanced scheduling approach is presented which is able to adapt to the requirements of real-life situations by taking into consideration the specific characteristics of modern manufacturing and assembly systems. These detailed constraints and capabilities of the actual resource environment include the alternative technological routes, the limited available machines, the unrelated processing time, the sequence dependent setup time and the jobs with due dates. An extended flexible job shop scheduling model is defined to solve the resource allocation problems and to create the fine schedule of the execution of jobs, tasks and operations. The paper describes the most important characteristics of the analyzed problem class and shows the main approach of the developed heuristic solving method. Our approach combines a special searching technique based on fast execution-driven simulation with a multi-objective optimization model.

Streamline 3D simulation model development for virtual commissioning with IEC61499

Current discrete manufacturing systems are characterized by an ever-increasing complexity, demanding for innovative solutions, capable to optimize performances while increasing the resilience and the capability to adapt to production modifications. With such a background, changing perspective to deal with distributed modular architectures of Cyber Physical Systems is mandatory, and the IEC 61499 standard, its object oriented, and event-based approaches promote this paradigm shift. The multi-disciplinary nature of the CPS entities and the possibility to exploit their digital counterparts, paves the way for the development of enhanced decision-support systems like the ones dedicated to Virtual Commissioning (VC). VC supports the automation developer in evaluating the impact of different management strategies, increasing the reliability of the final control applications, while reducing the amount of time to carry on physical tests on the real mechatronic system. However, creating a virtual commissioning model is still a complex and potentially expensive process that needs to be carried out by different professionals who must tightly cooperate to generate an effective playground for the automation testing. We propose a new approach to the design and develop virtual commissioning models, that, leveraging the synergies between modular simulation and IEC 61499 automation technologies, aims at improving the efficiency of the overall process of implementing 3D simulation digital twins for complex automated discrete manufacturing systems. The paper describes an open architecture, composed of reference data models and software API, and presents a proof-of-concept implementation of an integrated engineering platform of VC models.

Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems

Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components’ behavior depends on the process’ specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal.

Bottleneck detection for discrete manufacturing system based on object-oriented colored petri nets and cloud simulation

To identify the bottleneck unit of discrete manufacturing system dynamically, an intelligent detection method of bottleneck (IDMOB) is proposed by combining object-oriented colored Petri nets (OOCPN) and cloud simulation. With the proposed method, the production cells of manufacturing system are abstracted as several Petri net sub-modules. And the messages are passed through mapping functions and message passing gates among Petri net modules. Then, the cloud simulation is created to simulate the real processing, and the production data and capacity function are plugged into the cloud to detect the bottleneck unit intelligently. Finally, the effectiveness of IDMOB method is applied and validated in a practical case study from the power transformer industry.