A dynamic processing methodology of manufacturing data for the automated throughput analysis in cyber-physical production environment

2019 ◽  
Vol 27 (2) ◽  
pp. 155-169 ◽  
Author(s):  
Hyoung Seok Kang ◽  
Ju Yeon Lee ◽  
Sang Do Noh
Keyword(s):  
Real Time ◽  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Systematic Research ◽  
Practical Implementation ◽  
Successful Implementation ◽  
Production Environment ◽  
Throughput Analysis ◽  
Systematic Methodology

Various studies have been conducted on cyber-physical production systems (CPPS), a core technology for the implementation of smart manufacturing. However, existing studies are mostly conceptual or at an early stage, such as the proposal of a reference architecture. To achieve the practical implementation of CPPS, a systematic methodology for the collection, processing, and application of the data for CPPS is required. This is because CPPS can be successfully implemented only when processing criteria and application methods for the diverse data that change in real time because of the nature of a manufacturing shop floor are presented. Various technologies and systems have been developed for collecting raw data from a shop floor, but they are mainly focused on the automation of manufacturing. Thus, more detailed and systematic research is required for more efficient application of such technologies using a cyber model, which is the core of CPPS. For this purpose, in this article, a logic-based systematic methodology that can generate a throughput analysis model from the real-time data of a shop floor in a CPPS environment was proposed. Furthermore, logics that perform the Mapping, Scaling, and Calibration of the data of the shop floor into the machine, process, and factory levels were developed and their application to throughput analysis was described through a case study. The results of this study are expected to facilitate the practical implementation of CPPS and contribute to the successful implementation of smart manufacturing and the resultant revival of the manufacturing industry.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Interoperability in Smart Manufacturing: Research Challenges

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 21 ◽  
Author(s):  
Abe Zeid ◽  
Sarvesh Sundaram ◽  
Mohsen Moghaddam ◽  
Sagar Kamarthi ◽  
Tucker Marion
Keyword(s):  
Data Exchange ◽  
Research Area ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Successful Implementation ◽  
Integrated Networks ◽  
Software Packages ◽  
Industrial Internet ◽  
Logical Semantics ◽  
And Control

Recent advances in manufacturing technology, such as cyber–physical systems, industrial Internet, AI (Artificial Intelligence), and machine learning have driven the evolution of manufacturing architectures into integrated networks of automation devices, services, and enterprises. One of the resulting challenges of this evolution is the increased need for interoperability at different levels of the manufacturing ecosystem. The scope ranges from shop–floor software, devices, and control systems to Internet-based cloud-platforms, providing various services on-demand. Successful implementation of interoperability in smart manufacturing would, thus, result in effective communication and error-prone data-exchange between machines, sensors, actuators, users, systems, and platforms. A significant challenge to this is the architecture and the platforms that are used by machines and software packages. A better understanding of the subject can be achieved by studying industry-specific communication protocols and their respective logical semantics. A review of research conducted in this area is provided in this article to gain perspective on the various dimensions and types of interoperability. This article provides a multi-faceted approach to the research area of interoperability by reviewing key concepts and existing research efforts in the domain, as well as by discussing challenges and solutions.

Implementing the Transformation of Discrete Part Manufacturing Systems Into Smart Manufacturing Platforms

2018 ◽  
Author(s):  
Bhaskar Botcha ◽  
Zimo Wang ◽  
Sudarshan Rajan ◽  
Natarajan Gautam ◽  
Satish T. S. Bukkapatnam ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Manufacturing Systems ◽  
Energy Savings ◽  
Advanced Materials ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Workflow System ◽  
Manufacturing Technologies ◽  
Discrete Part

Prior R&D efforts point to substantial performance enhancements and energy savings from adopting the Smart Manufacturing (SM) paradigm for process optimization and real-time quality assurance. Significant barriers and risks disincentivize the industry from investing in the adoption and training of SM component suites for discrete manufacturing applications. A diverse discrete part manufacturing enterprises, SM tools and platform vendors are yearning for a testbed reconfigurable to achieve three objectives of performance benchmarking, demonstration, and workforce training for a spectrum of their industrial scenarios and workflows. This paper presents the key ingredients towards the successful transformation of present machine tool and manufacturing environments into SM platform-integrated environments. The present implementation focuses on demonstration of the use of the Smart Manufacturing (SM) platform towards qualification of advanced materials and manufacturing technologies to meet an industry-specified functionality. This initial implementation uses Kepler workflow system residing as part of an Amazon Web Services environment to allow flexible workflows on multiple machines, each of which is integrated with an innovative sensor wrapper that integrates Commercial Off The Shelf (COTS) components from National Instruments (NI) to connect a legacy equipment to the SM platform. Here, an advanced analytics engine with modules customizable for both high-performance computing and shop floor environments was integrated into the commercial web service (from Amazon) to provide real-time monitoring and anomaly detection capability. This implementation indicates the potential of SM platform to achieve drastic reductions in the time and effort taken towards qualification of advanced materials and manufacturing technologies.

A Digital Twin Concept for Manufacturing Systems

2018 ◽  
Author(s):  
Wesley Ellgass ◽  
Nathan Holt ◽  
Hector Saldana-Lemus ◽  
Julian Richmond ◽  
Ali Vatankhah Barenji ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Virtual Space ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Digital Twin ◽  
Physical Resources ◽  
Virtual Resources

With the developments and applications of the advanced information technologies such as cloud computing, internet of thing, artificial intelligence and virtual reality, industry 4.0 and smart manufacturing era are coming. In this respect, one of the specific challenges is to achieve a connection of physical resources on the shop floor with virtual resources, for real-time response, real time process optimization, and simulation, which is merged by big data problem. In this respect, Digital Twins (DT) concept is introduced as a key technology, which includes physical resources, virtual resources, service system, and digital twin data. DT considers current condition of physical resource and prediction of future events to make a responsive decision. However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little applications have been developed with this purpose, especially in the industrial manufacturing area. Therefore, the types of data and technology required to build the DT for a manufacturing system are presented in this work, trying to develop a framework of DT based manufacturing system, which is supported by the virtual reality for virtualization of physical resources.

scholarly journals FASTEN IIoT: An Open Real-Time Platform for Vertical, Horizontal and End-To-End Integration

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5499
Author(s):  
Felipe S. Costa ◽  
Silvia M. Nassar ◽  
Sergio Gusmeroli ◽  
Ralph Schultz ◽  
André G. S. Conceição ◽  
...  
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Business Models ◽  
Production Systems ◽  
Transformation Process ◽  
Digital Transformation ◽  
Shop Floor ◽  
Decentralized Decision Making ◽  
End To End

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.

The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 11
Author(s):  
Tingyu Liu ◽  
Mengming Xia ◽  
Qing Hong ◽  
Yifeng Sun ◽  
Pei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Human Behavior ◽  
Manufacturing Industry ◽  
Behavior Modeling ◽  
Shop Floor ◽  
Human Behavior Modeling ◽  
Digital Twin ◽  
Production Behavior ◽  
Time Position ◽  
Human Limb

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently. As a key part of the shop-floor, humans' high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior. Therefore, the modeling system for cross-scale human behavior in digital twin shop-floors was developed, powered by the data fusion of macro-behavior and micro-behavior virtual models. Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position. Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation. In this study, we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors. Based on this theoretical system, we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling. Lastly, we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors. Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

scholarly journals Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 5994
Author(s):  
Sarvesh Sundaram ◽  
Abe Zeid
Keyword(s):  
Manufacturing Industry ◽  
Health Management ◽  
Phase 1 ◽  
Remaining Useful Life ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Prognostics And Health Management ◽  
Ongoing Research ◽  
Two Phases ◽  
Traditional Manufacturing

Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

scholarly journals Optimization of Material Supply in Smart Manufacturing Environment: A Metaheuristic Approach for Matrix Production

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 220
Author(s):  
Tamás Bányai
Keyword(s):  
Real Time ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Control Point ◽  
Smart Manufacturing ◽  
Scheduling Problem ◽  
Production Environment ◽  
Routing And Scheduling ◽  
Material Supply ◽  
Matrix Production

In the context of Industry 4.0, the matrix production developed by KUKA robotics represents a revolutionary solution for flexible manufacturing systems. Because of the adaptable and flexible manufacturing and material handling solutions, the design and control of these processes require new models and methods, especially from a real-time control point of view. Within the frame of this article, a new real-time optimization algorithm for in-plant material supply of smart manufacturing is proposed. After a systematic literature review, this paper describes a possible structure of the in-plant supply in matrix production environment. The mathematical model of the mentioned matrix production system is defined. The optimization problem of the described model is an integrated routing and scheduling problem, which is an NP-hard problem. The integrated routing and scheduling problem are solved with a hybrid multi-phase black hole and flower pollination-based metaheuristic algorithm. The computational results focusing on clustering and routing problems validate the model and evaluate its performance. The case studies show that matrix production is a suitable solution for smart manufacturing.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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