Future Manufacturing Systems Research Articles

A complementary Cyber-Human Systems framework for Industry 4.0 Cyber-Physical Systems

Humans are a vital element to automotive manufacturing; however, skilled production personnel have largely been designated as data receivers in Cyber-Physical Systems (CPS) of Industry 4.0. A renewed focus on the human worker who completes significant portions of manual value-added content in automotive assembly through Cyber-Human Systems (CHS) is allowing humans to perform their jobs more safely, efficiently, and supporting enhanced control and quality monitoring of manual manufacturing tasks. There is a need for a unified complementary framework of CHS and CPS to guide the implementation of future smart manufacturing systems.

Implementation of Eye-Tracking Technology in Holonic Manufacturing Systems

Abstract During the last decade, Eye-Tracking technology has been popular amongst practitioners and researchers from various disciplines. In spite of widespread applications of Eye-Tracking technology‘, considerably less attention has been paid to the potential of this technology in manufacturing system. An holonic manufacturing system is a manufacturing system with high agility, which is an important characteristic for future manufacturing systems. This paper provides a theoretical overview and guidance for the implementation of Eye-Tracking technology in the holonic manufacturing system. The description of the Eye-Tracking technology and a brief discussion of potential application and the limitations is provided. This study discusses the implementation of Eye-Tracking technology as an holon.

A Novel Networked Online Recursive Identification Method for Multivariable Systems With Incomplete Measurement Information

Network-based identification of multivariable systems plays a key role in future smart manufacturing systems in achieving the goals of industry 4.0. The incomplete information caused by network traffic congestion or cyber attacks in the networked environment will inevitably deteriorate the performance of system identification, and in the extreme cases, it will cause nonconvergence of the identifier. Unlike the traditional recursive least-squares algorithms based on the complete data, this paper investigates a novel networked online recursive identification method for multivariable systems with incomplete information. In this new algorithm, the characteristics of data packet dropouts are first formulated as a Bernoulli process, and the lost data are compensated by an auxiliary model. A new information set including networked parameters is then constructed, and the corresponding networked online identification algorithm for multivariable systems is proposed. The proposed algorithm can overcome the negative effect of data packet losses on the identification performance and can be updated recursively. Furthermore, using the Lyapunov and martingale methods, the convergence rate of the proposed algorithm as well as its computational complexity is analyzed in detail. Simulation examples confirm the feasibility and efficiency of the proposed method.

Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

CyberManufacturing system (CMS) is a vision for future manufacturing systems. The concept delineates a vision of advanced manufacturing system integrated with technologies such as Internet of Thing...

Developing and Harnessing the Potential of SMEs for Eco-efficient Flexible Production

Production-oriented ICT solutions, offering an intelligent networking of value adding systems, which are interconnected and able to communicate with each other and with the Internet, are seen as key enablers for next generation production systems in “Industry 4.0” scenarios. These future manufacturing systems will be more energy efficient, saving limited resources and having a minimized negative impact on the environment. They also will be able to adapt to new requirements and conditions, incorporating and harnessing human flexibility, creativity and problem-solving capabilities. That's why, apart from ready-to-use technical systems and adequate organizational structure, competent employees, qualified to interact with and to make use of those technologies are of major importance for the success of “Industry 4.0”. Especially SMEs with limited R&D capabilities have to overcome significant obstacles in order to implement the elements mentioned above. The paper introduces the “E3ResearchFactory” located at Fraunhofer IWU in Chemnitz and demonstrates on several examples, which specific solutions and use cases are developed and transferred towards small and medium-sized production companies.

Joint Optimization of Production Planning and Capacity Adjustment for Assembly System

Manufacturing companies are confronted with increasing market volatility, which is characterized by the increasing of product variants and decreasing of production volume of each variant to meet customers’ demands. To stay competitive and to fulfill quickly changing market needs, manufacturing companies have to cope with rapidly changing customer demands concerning product types, quantities and delivery dates and have the ability to enable a fast modification and system-change of future manufacturing systems. Production planning and control must compensate the resulting fluctuations in capacity demand. This paper deals with a problem of joint optimization of production planning and capacity adjustment based on products specifications, delivery time constraints and reconfigurable machines capabilities for assembly systems. At the production planning level, the production planning problem consists in a multi-product capacitated lot-sizing problem. At the capacity adjustment level, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality. In this context, the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. The main objective of this paper is to determine simultaneously the economic production quantity of each product variants and the optimal capacity of assembly systems to ensure the adherence to delivery dates. A stochastic mathematical model is developed and solved using a simulation optimization approach based on the response surface methodology. The obtained results show clearly strong interactions between production quantity, delivery time constraints and capacity of assembly line which confirm the necessity of jointly considering production planning and capacity adjustment in an integrated model.

From Adaptive Reasoning to Cognitive Factory: Bringing Cognitive Intelligence to Manufacturing Technology

There are two important aspects that will play important roles in future manufacturing systems: changeability and human-machine collaboration. The first aspect, changeability, concerns with the ability of production tools to reconfigure themselves to the new manufacturing settings, possibly with unknown prior information, while maintaining their reliability at lowest cost. The second aspect, human-machine collaboration, emphasizes the ability of production tools to put themselves on the position as humans’ co-workers. The interplay between these two aspects will not only determine the economical accomplishment of a manufacturing process, but it will also shape the future of the technology itself. To address this future challenge of manufacturing systems, the concept of Cognitive Factory was proposed. Along this line, machines and processes are equipped with cognitive capabilities in order to allow them to assess and increase their scope of operation autonomously. However, the technical implementation of such a concept is still widely open for research, since there are several stumbling blocks that limit practicality of the proposed methods. In this paper, we introduce our method to achieve the goal of the Cognitive Factory. Our method is inspired by the working mechanisms of a human’s brain; it works by harnessing the reasoning capabilities of cognitive architecture. By utilizing such an adaptive reasoning mechanism, we envision the future manufacturing systems with cognitive intelligence. We provide illustrative examples from our current research work to demonstrate that our proposed method is notable to address the primary issues of the Cognitive Factory: changeability and human-machine collaboration.

BLOCKCHAIN READY MANUFACTURING SUPPLY CHAIN USING DISTRIBUTED LEDGER

The blockchain technology as a foundation for distributed ledgers offers an innovative platform for a new decentralized and transparent transaction mechanism in industries and businesses. The inherited characteristics of this technology enhance trust through transparency and traceability within any transaction of data, goods, and financial resources. Despite initial doubts about this technology, recently governments and large corporations have investigated to adopt and improve this technology in various domains of applications, from finance, social and legal industries to design, manufacturing and supply chain networks. In this article, the authors review the current status of this technology and some of its applications. The potential benefit of such technology in manufacturing supply chain is then discussed in this article and a vision for the future blockchain ready manufacturing supply chain is proposed. Manufacturing of cardboard boxes are used as an example to demonstrate how such technology can be used in a global supply chain network. Finally, the requirements and challenges to adopt this technology in the future manufacturing systems are discussed.

An event-driven manufacturing information system architecture for Industry 4.0

Future manufacturing systems need to be more flexible, to embrace tougher and constantly changing market demands. They need to make better use of plant data, ideally utilising all data from the entire plant. Low-level data should be refined to real-time information for decision-making, to facilitate competitiveness through informed and timely decisions. The Line Information System Architecture (LISA), is presented in this paper. It is an event-driven architecture featuring loose coupling, a prototype-oriented information model and formalised transformation services. LISA is designed to enable flexible factory integration and data utilisation. The focus of LISA is on integration of devices and services on all levels, simplifying hardware changes and integration of new smart services as well as supporting continuous improvements on information visualisation and control. The architecture has been evaluated on both real industrial data and industrial demonstrators and it is also being installed at a large automotive company. This article is an extended and revised version of the paper presented at the 2015 IFAC Symposium on Information Control in Manufacturing (INCOM 2015). The paper has been restructured in regards to the order and title of the chapters, and additional information about the integration between devices and services aspects have been added. The introduction and the general structure of the paper now better highlight the contributions of the paper and the uniqueness of the framework.

A review of diagnostic and prognostic capabilities and best practices for manufacturing.

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange

Abstract: In the context of the Cyber Physical Systems toward the realization of a Digital Twin system for future manufacturing and product service systems we propose the use of AutomationML to model attributes related to the Digital Twin. Also, we propose that this model is very useful for data exchange between different systems that are connected with the Digital Twin. We present a case study where a industrial component was modeled and simulated to prove that our methodology works.

4D Printing Technology: A Review

Abstract 3D printing has been recognized as a disruptive technology for future advanced manufacturing systems. With a great potential to change everything from our daily lives to the global economy, significant advances in 3D printing technology have been made with respect to materials, printers, and processes. In this context, although similar to 3D printing technology, 4D printing technology adds the fourth dimension of time. 4D printing allows a printed structure to change its form or function with time in response to stimuli such as pressure, temperature, wind, water, and light. Recently, rapid advances in printing processes and materials development for 3D printing have allowed the printing of smart materials or multimaterials designed to change function or shape. In this review, we first compare the similarities and differences between 3D printing and 4D printing. We then look into the main factors composing 4D printing technology such as smart materials and designs. Finally, we summarize the curren...

Concept of dependent joints in functional reconfigurable robots

Purpose– The purpose of this paper is to discuss an example of modelling with experiments of robot prototype with dependent joint concept, including a full description of related functionalities. Reduction in active degrees of freedom in a machine can lead to improved accuracy, improved reliability and lower cost. The reconfiguration of machines and systems is a key technology for future responsive manufacturing systems. The concept of dependent joints helps to implement much specified sub-workspaces depending on functional needs in the machine.Design/methodology/approach– This is inherently made possible using smart mechanical concepts having embedded sensors and reconfigurable control systems. This paper introduces structural reconfiguration systems and discusses a sample approach to functional reconfiguration.Findings– A successful manipulator design with extended features when considering reduction in active degrees of freedom in a machine would lead to specific sub-workspace with improved accuracy, improved reliability and lower cost.Research limitations/implications– Reduction in active degrees of freedom in a machine can lead not only towards a dedicated functional workspace but also towards improved accuracy, improved reliability and lower cost.Originality/value– This paper is of value to engineers and researchers developing robotic manipulators for use in various aspects of industry.

Adaptable Capability-Based Planning

Future manufacturing systems are required to be adaptable and to quickly react on changes in markets and demands. There is a shift towards mass customization. Therefore, future manufacturing systems must be able to support individualized products that are tailored to customer needs. The switch between different products should involve little manual intervention. In this paper, the issues that need to be resolved to achieve adaptability in production planning are highlighted and ideas for solutions are presented. A detailed approach for the question of automatically generating action sequences is presented here. It is based on a capability model of the resources and their material flow. We also suggest decoupling product descriptions from factories and planning adaptably based on pre-defined product descriptions and factory setups. Action sequences can then be generated out of these models and later translated into executable action sequences. The action sequence can be automatically downloaded and executed on a resource. The presented approach is evaluated on an educational production system with industrial components. Issues and future research directions are illustrated as well.

An Event-Driven Manufacturing Information System Architecture

Future manufacturing systems need to be more flexible, to embrace tougher and constantly changing market demands. They also need to make better use of plant data, ideally utilizing all data from the entire plant. Low-level data should be refined to real-time information for decision making, to facilitate competitiveness through informed and timely decisions.The Line Information System Architecture, LISA, is designed to enable flexible factory integration and data utilization. In LISA, international standards and established off-the-shelf technologies have been combined with the main objective to be industrially applicable. LISA is an event-driven architecture with a prototype-oriented information model and formalized transformation services. It features loose coupling, flexibility, and ease of retrofitting legacy devices. The architecture has been evaluated on both real industrial data and industrial demonstrators and is also being installed at a large automotive company.

Simulation study of large production network robustness in uncertain environment

Robustness is an important success factor for production networks in which the operation of enterprises is subjected to an uncertain environment. In this paper, the robustness of networks is studied as a function of network size. The study is performed through a simulation experiment in which the uncertain environment is modelled by introducing perturbations in demand. The decision-making model mimics the behaviour of socially connected human subjects. The results show how robustness and production rate are affected by system size and social network structure, and how this is relevant for the design and operation of future manufacturing systems.

Moving towards green and sustainable manufacturing

The pressing needs of energy, water and other resource conservation worldwide is a major engineering challenge. In manufacturing, developing green technologies (from process and tooling to the entire enterprise) is one way to insure that future manufacturing systems are sustainable. To do this, innovation in advanced manufacturing is needed. The basic requirements of green technology are discussed along with methods and tools to insure they are effectively applied and their impacts measured. For situations in which the manufacturing environmental burden is less than the burden for the use of the product leveraging is proposed to insure a total life cycle impact reduction. Examples of several green technologies are presented.

Co-design of Products and Systems Using a Bayesian Network

Complexity in manufacturing arises due to the intertwined relationships between products and their manufacturing systems. If the system/product relations can be retrieved automatically and efficiently, complex systems would be better designed and utilized to manufacture more products effectively. In this paper, a comprehensive method is used to explore the inter- relationships in the products domain and machines domain, and map the relations between products and systems. The method uses structure learning by Bayesian Networks to capture and analyze these relations, hence facilitating synthesis of new systems and product. A case study of parts and respective machines for producing them is used to demonstrate the method. Results show that the dependency relations among products and systems features can be extracted by analyzing existing instances of related entities such as machines/products and specifications without explicitly identifying the relations between them, which is akin to reverse engineering. Using Bayesian Networks and the most probable algorithm, a new composite part is obtained for a 3-axis machine with a tool magazine. This new information is based on the inferred Bayesian Network that links products and machines. Thus, future manufacturing systems and parts/machines co-design can be done robustly without much human interference.

Towards a formal manufacturing reference ontology

Due to the advancement in the application of Information and Communication Technology (ICT), manufacturing industry and its many domains employ a wide range of different ICT tools. To be competitive, industries need to communicate effectively within and across their many system domains. This communication is hindered by the diversity in the semantics of concepts and information structures of these different domain systems. Whilst international standards provide an effective route to information sharing within narrowly specified domains, they are themselves not interoperable across the wide range of application domains needed to support manufacturing industry due to the inconsistency of concept semantics. Formal ontologies have shown promise in removing interpretation problems by computationally capturing the semantics of concepts, ensuring their consistency and thus providing a verifiable and shared understanding across multiple domains. The research work reported in this paper contributes to the development of formal reference ontology for manufacturing, which is envisaged as a key component in future interoperable manufacturing systems. A set of core manufacturing concepts are identified and their semantics have been captured in formal logic based on exploiting and extending existing standards’ definitions, where possible combined with an industrial investigation of the concepts required. A successful experimental investigation has been conducted to verify the application of the ontology based on the interaction between concepts in the design and manufacturing domains of an aerospace component.

The Future Manufacturing System and Cloud Computing

Modern manufacturing system and processes are becoming increasingly complex, and requesting much function and tools to user. It also is operating in highly dynamic environments. Traditionally, flexible manufacturing system (FMS) was used. Nowadays, communication networks are available to virtually every aspect of the manufacturing environment. In the contents, manufacturing system with cloud computing is a good example. In this paper, we consider the cloud manufacturing system and their future works.