Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

IoT-enabled smart appliances under industry 4.0: A case study

Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.     

Analysis of fractal manufacturing systems framework towards industry 4.0

Currently, ways to approach the design of Cyber Physical Systems in Industry 4.0 are under development. Emerging concepts of Smart Factories require the development of specialized knowledge; new working methods are also needed to manage the transition from conventional industry to industry 4.0. To achieve this objective, fractal theory could provide the appropriate knowledge and tools. Fractal systems applied to manufacturing have been widely used over the last decades to design complex adaptive systems: it allows the introduction of resilience requirements (capacity to react to changes in a turbulent environment) and to reduce the complexity of its structure, operation and management. In order to know the potential and the possibility of applying fractal theory to the design of systems in Industry 4.0, this article reviews the publications that develop fractal systems for manufacturing engineering. The review includes contributions published between 1985 (approximate date of the first works on the theory applied to manufacturing engineering) and 2019. The objective is to gather those strategies, methodologies and successful case studies that can be useful for the approaches of Industry 4.0 and to define a set of future lines of work for the adaptation of the fractal theory to the new challenges posed by Industry 4.0.     

Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model

Recent findings have shown that Digital Twin served multiple constituencies. However, the dilemma between the scope and scale needs a sophisticated reference architecture, a right set of technologies, and a suitable business model. Most studies in the Digital Twin field have only focused on manufacturing and proposed explicit frameworks and architecture, which faced challenges to support different integration levels through an agile process. Besides, no known empirical research has focused on exploring relationships between Digital Twin and mass individualization. Therefore, the principal objective of this study was to identify suitable Industry 4.0 technologies and a holistic reference architecture model to accomplish the most challenging Digital Twin enabled applications. In this study, a Digital Twin reference architecture was developed and applied in an industrial case. Also, Digital Twin as a Service (DTaaS) paradigm utilized for the digital transformation of unique wetlands with considerable advantages, including smart scheduled maintenance, real-time monitoring, remote controlling, and predicting functionalities. The findings indicate that there is a significant relationship between Digital Twin capabilities as a service and mass individualization.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Implementation path and reference framework for Industrial Internet Platform (IIP) in product service system using industrial practice investigation method

With the development of intelligent sensing, edge computing, fog computing, cloud computing, parallel computing, smart grid, big data, block chain, 5G, cyber-physical systems, digital twins, machine learning and other technologies, the industrial internet has undergone control network stage, sensor network stage, internet stage, Internet of Things (IoT) stage, Industrial Internet of Things (IIoT) stage, and Industrial Internet (II) stage, etc. In the existing research, scholars focus on a local dot, such as: technology, function, elements and application based on industrial internet. However, there is a lack of an overall framework to study the top-level planning of Industrial Internet Platform (IIP) from a systematic perspective. On the other hand, there are few studies on the detailed path and steps for implementing IIP in a specific enterprise in a specific industry. The objective of this paper is to study a reference framework and industrial implementation path for IIP in product service system using industrial practice investigation method, which meets the needs of industry on the basis of existing theory and industrial practice, and to provide reference for government and industry planning, design, implementation and promotion of IIP. In addition, the proposed reference framework and industrial implementation for IIP in product service system can enhance the core value of the enterprise and increase benefits.     

A function-oriented biologically analogical approach for constructing the design concept of smart product in Industry 4.0

The development of the Industry 4.0 paradigm and the advancement of information technology have aroused new consumer requirements for smart products that are capable of context awareness and autonomous control. Nature holds huge potential for inspiring innovative design concepts that can meet the ever-growing need for smart products since biology perceive and interact with their living environment for survival. However, to date, very few studies have explored the application of natural wisdom in building innovative design concepts for smart products. This paper proposes a function-oriented design approach for smart products, by analogizing to biological prototypes. To do so, a unified functional representation, based on the Function–behavior–structure (FBS) ontology, is proposed to abstract biological prototypes, followed by a fuzzy triangular numbers-based algorithm designed to locate appropriate biological prototypes as analogical sources for smart product development. Moreover, functional innovative strategies and a hybrid design process are formulated to develop design concepts of smart products, by integrating several existing engineering design methods. Finally, an illustrative design case of a smart natural resource collecting system is used to demonstrate the workability of the proposed method.     

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing system paradigm A new mathematical cost model is proposed for assessing the cost-benefit analysis of introducing Industry 4.0 elements to the manufacturing facility, specifically, integrating and connecting external suppliers as strategic partners and establishing an infrastructure for communicating information between the manufacturing company and its strategic suppliers. The mathematical model takes into consideration the bi-directional relationship between hourly rates and total hours assigned to workcentres/activities in a certain production period. A case study, from a multinational machine builder, is developed and solved using the proposed model. Results suggest that though an additional cost is required to establish infrastructure to connect suppliers, the responsiveness and agility achieved resulting from uncertainty outweighs the additional cost.     

A human-in-the-loop manufacturing control architecture for the next generation of production systems

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.     

Towards online reinforced learning of assembly sequence planning with interactive guidance systems for industry 4.0 adaptive manufacturing

Literature shows that reinforcement learning (RL) and the well-known optimization algorithms derived from it have been applied to assembly sequence planning (ASP); however, the way this is done, as an offline process, ends up generating optimization methods that are not exploiting the full potential of RL. Today’s assembly lines need to be adaptive to changes, resilient to errors and attentive to the operators’ skills and needs. If all of these aspects need to evolve towards a new paradigm, called Industry 4.0, the way RL is applied to ASP needs to change as well: the RL phase has to be part of the assembly execution phase and be optimized with time and several repetitions of the process. This article presents an agile exploratory experiment in ASP to prove the effectiveness of RL techniques to execute ASP as an adaptive, online and experience-driven optimization process, directly at assembly time. The human-assembly interaction is modelled through the input-outputs of an assembly guidance system built as an assembly digital twin. Experimental assemblies are executed without pre-established assembly sequence plans and adapted to the operators’ needs. The experiments show that precedence and transition matrices for an assembly can be generated from the statistical knowledge of several different assembly executions. When the frequency of a given subassembly reinforces its importance, statistical results obtained from the experiments prove that online RL applications are not only possible but also effective for learning, teaching, executing and improving assembly tasks at the same time. This article paves the way towards the application of online RL algorithms to ASP.     

A survey on decision-making based on system reliability in the context of Industry 4.0

The business world is continually changing. Dynamic environments, full of uncertainties, complexities, and ambiguities, demand faster and more confident decisions. To compete in this environment, Industry 4.0 emerges as an essential alternative. In this context, the reliability of manufacturing is an essential aspect for companies to make successful decisions. In the literature, several technologies associated with Industry 4.0 have been applied to improve the availability of equipment, including the Internet of Things (IoT), Cyber-Physical Systems (CPS), blockchain, and data mining. Nevertheless, there is still no survey study that seeks to show how reliability has collaborated to support decision-making in organizations, in the context of Industry 4.0. In general, most applications still focus on the productivity and health of individual equipment. However, in today's volatile and complex businesses, local decisions are no longer sufficient; it is necessary to analyze the organization entirely. Thus, being aware of the impacts that a local failure can impose on the entire company has significant weight in the decision-making process. In this context, this article presents a survey to identify how researches on systems reliability has contributed to and supported the development of decision-making in Industry 4.0. The main contribution of this article is to highlight how reliability can be used to support different types of strategic decisions in the context of Industry 4.0. Finally, it highlights the need for research associating management decisions with the technologies of Industry 4.0.     

A knowledge-based tool for designing cyber physical production systems

Changing production systems and product requirements can trace their origin in volatile customer behaviour and evolving product requirements. This dynamic nature of customer requirements has been described as a constantly moving target, thus presenting a significant challenge for several aspects of product development. To deal with this constant and sometimes unpredictable product evolution, cyber physical production systems (CPPS) that employ condition monitoring, self-awareness and reconfigurability principles, have to be designed and implemented. This research contributes a CPPS design approach that proactively provides the required CPPS design knowledge. This approach aims to minimise or avoids future consequences and disruptions on the CPPS. This knowledge needs to be provided at the right time whilst not being intrusive to the production system designer’s cognitive activity. To effectively deal with the complexity of the cyber physical production system design activity with a manual method would lead to a time consuming, and complex support tool which is hard to implement, and difficult to use. The CPPS design approach has therefore been implemented in a prototype digital factory tool. This paper describes in detail the system requirements and system architecture for this tool. In order to establish the effectiveness of the proposed approach for designing cyber physical production systems, the prototype digital factory tool has been evaluated with a case study and a number of semi-structured interviews with both industrial and scientific stakeholders. The encouraging results obtained from this research evaluation have shown that such an approach for supporting the CPPS design activity makes stakeholders aware of their decision consequences and is useful in practice. This result can lead the way for the development and integration of such knowledge-based decision-making approaches within state-of-the-art digital factory and Computer Aided Engineering Design (CAED) tools.     

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

ContextSoftware architectures should be evaluated during the early stages of software development in order to verify whether the non-functional requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in software product line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development.ObjectiveWe present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI.MethodThe family of experiments was carried out by 92 participants: Computer Science Master’s and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM).ResultsThe main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results.ConclusionsThe results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.     

Hybrid multi-attribute QoS optimization in component based software systems

Design decisions for complex, component-based systems impact multiple quality of service (QoS) properties. Often, means to improve one quality property deteriorate another one. In this scenario, selecting a good solution with respect to a single quality attribute can lead to unacceptable results with respect to the other quality attributes. A promising way to deal with this problem is to exploit multi-objective optimization where the objectives represent different quality attributes. The aim of these techniques is to devise a set of solutions, each of which assures an optimal trade-off between the conflicting qualities. Our previous work proposed a combined use of analytical optimization techniques and evolutionary algorithms to efficiently identify an optimal set of design alternatives with respect to performance and costs. This paper extends this approach to more QoS properties by providing analytical algorithms for availability-cost optimization and three-dimensional availability-performance-cost optimization. We demonstrate the use of this approach on a case study, showing that the analytical step provides a better-than-random starting population for the evolutionary optimization, which lead to a speed-up of 28% in the availability-cost case.     

A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.     

A learning-based module extraction method for object-oriented systems

Developers apply object-oriented (OO) design principles to produce modular, reusable software. Therefore, service-specific groups of related software classes called modules arise in OO systems. Extracting the modules is critical for better software comprehension, efficient architecture recovery, determination of service candidates to migrate legacy software to a service-oriented architecture, and transportation of such services to cloud-based distributed systems. In this study, we propose a novel approach to automatic module extraction to identify services in OO software systems. In our approach, first we create a weighted and directed graph of the software system in which vertices and edges represent the classes and their relations, respectively. Then, we apply a clustering algorithm over the graph to extract the modules. We calculate the weight of an edge by considering its probability of being within a module or between modules. To estimate these positional probabilities, we propose a machine-learning-based classification system that we train with data gathered from a real-world OO reference system. We have implemented an automatic module extraction tool and evaluated the proposed approach on several open-source and industrial projects. The experimental results show that the proposed approach generates highly accurate decompositions that are close to authoritative module structures and outperforms existing methods.     

A feature-oriented approach for developing reusable product line assets of service-based systems

Service orientation (SO) is a relevant promising candidate for accommodating rapidly changing user needs and expectations. One of the goals of adopting SO is the improvement of reusability, however, the development of service-based system in practice has uncovered several challenging issues, such as how to identify reusable services, how to determine configurations of services that are relevant to users’ current product configuration and context, and how to maintain service validity after configuration changes. In this paper, we propose a method that addresses these issues by adapting a feature-oriented product line engineering approach. The method is notable in that it guides developers to identify reusable services at the right level of granularity and to map users’ context to relevant service configuration, and it also provides a means to check the validity of services at runtime in terms of invariants and pre/post-conditions of services. Moreover, we propose a heterogeneous style based architecture model for developing such systems.     

Understanding the characteristics of quality for software engineering processes: A Grounded Theory investigation

ContextSoftware engineering organizations routinely define and implement processes to support, guide and control project execution. An assumption underlying this process-centric approach to business improvement is that the quality of the process will influence the quality, cost and time-to-release of the software produced. A critical question thus arises of what constitutes quality for software engineering processes.ObjectiveTo identify criteria used by experienced practitioners to judge the quality of software engineering processes and to understand how knowledge of these criteria and their relationships may be useful for those undertaking software process improvement activities.MethodInterviews were conducted with 17 experienced software engineering practitioners from a range of geographies, roles and industry sectors. Published reports from 30 software process improvement case-studies were selected from multiple peer-reviewed software journals. A qualitative Grounded Theory-based methodology was employed to systematically analyze the collected data to synthesize a model of quality for software engineering processes.ResultsThe synthesized model suggests that practitioners perceive the overall quality of a software engineering process based on four quality attributes: suitability, usability, manageability and evolvability. Furthermore, these judgments are influenced by key properties related to the semantic content, structure, representation and enactment of that process. The model indicates that these attributes correspond to particular organizational perspectives and that these differing views may explain role-based conflicts in the judgement of process quality.ConclusionConsensus exists amongst practitioners about which characteristics of software engineering process quality most influence project outcomes. The model presented provides a terminological framework that can facilitate precise discussion of software engineering process issues and a set of criteria that can support activities for software process definition, evaluation and improvement. The potential exists for further development of this model to facilitate optimization of process properties to match organizational needs.     

Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This is applied greatly in business enterprises and government sectors for delivering the services to their customers, clients and citizens. But, the interaction is successful only based on the trust that each device has on another. Thus trust is very much essential for a social network. As Internet of Things have access over sensitive information, it urges to many threats that lead data management to risk. This issue is addressed by trust management that help to take decision about trustworthiness of requestor and provider before communication and sharing. Several trust-based systems are existing for different domain using Dynamic weight method, Fuzzy classification, Bayes inference and very few Regression analysis for IoT. The proposed algorithm is based on Logistic Regression, which provide strong statistical background to trust prediction. To make our stand strong on regression support to trust, we have compared the performance with equivalent sound Bayes analysis using Beta distribution. The performance is studied in simulated IoT setup with Quality of Service (QoS) and Social parameters for the nodes. The proposed model performs better in terms of various metrics. An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services. The most salient features of IoT system is to design it with scalability, extendibility, compatibility and resiliency against attack. The existing works finds a way to integrate direct and indirect trust to converge quickly and estimate the bias due to attacks in addition to the above features.