Autonomic smart manufacturing

A few studies on response blocking as well as response interruption and redirection (RIRD) as a treatment for automatically maintained problem behavior have examined different levels of treatment integrity. Results from these studies have shown continued suppression of responding under low levels of integrity for some participants after they were exposed to blocking at full integrity. The current study compared the treatment effects of RIRD at two levels of integrity (100 and 33%). The 33% condition was not preceded by exposure to full integrity. Four children diagnosed with an autism spectrum disorder between the ages of 8 and 16 participated. The effects of treatment integrity on vocal stereotypy were assessed using an ABAB‐variant design in which B consisted of an alternating‐treatments comparison of different levels of integrity. Results varied across participants, and although both conditions decreased stereotypy, 100% integrity produced lower levels of stereotypy quicker than 33%. The current study expands upon current literature by suggesting that lower levels of integrity (33%) may still suppress vocal stereotypy.

Chapter 4 - Analysis of factors influencing smart and sustainable manufacturing systems using a multi-criteria decision making tool

This paper proposes an assessment framework for smart manufacturing in terms of the extent of Information & Communication Technologies (ICT) adoption to manufacturing process. For an assessment framework, this paper establishes assessment principles and identifies activities of manufacturing process to be assessed. In addition, maturity levels of smart manufacturing are defined in this paper. Each level has different characteristics and different extent of ICT adoption in manufacturing process. Based on an assessment reference model, this paper gives a way of creating assessment indicators for each activities per each maturity levels. With answers to questionnaires/indicators, smart manufacturing can be assessed and expressed as a maturity level.

The research proposes a metric referred to as Smart Manufacturing Performance Measures (SMPMs), which are mapped with manufacturing decisions (structural and infrastructural) and manufacturing outputs (cost, quality, delivery, flexibility, and innovativeness). The mapping between manufacturing decisions, manufacturing outputs, and SMPMs, facilitates the organization in choosing SMPMs to deliver desired output as represented by the competitive priority. The approach provides a direction for the practitioners to implement and operate smart manufacturing systems efficiently and effectively by devising a performance measurement system. A total of 22 performance measures are identified through an in-depth literature review. These are further classified into hard and soft measures, which allow decision-makers to measure SMPMs and focus on a specific performance measure. Further, SMPMs is linked to 13 enablers, which are the driving technologies for implementing smart manufacturing systems. Smart manufacturing is evolving, and its implementation is still in the nascent phase. The proposed approach plays a vital role in redefining the manufacturing strategy while deploying smart manufacturing systems. The research contribution is a step-in assessment of smart manufacturing to understand the ‘what’ and ‘how’ of technological changes are transforming the operations and driving outputs.

In this work we, first, investigate multilayer network planning approaches with different levels of integration by extending a commercial planning tool that accurately models the IP network. The different levels of integration are defined with respect to the degree to which information from the optical layer is considered in the optimization of the IP layer. The impact of traffic demand and topology size is evaluated in terms of the obtained cost savings. Case study results indicate cost savings reaching 25% by holistic multilayer network planning. We then proceed to evaluate the impact of the IP layer routing policy, expressed by the IP link weights, on the cost and latency of a holistic multilayer network design. As many network operators prefer to assign IP link weights based on simple rules that are unaffected by the network conditions, we focus on two weight setting policies that are agnostic to the network conditions: the hop-based policy and the distance-based policy. We find that different IP layer policies may lead to different optimized IP topologies for the same traffic and optical layer topology. It is shown that the optical network's regeneration requirements affect the optimal policy.

Research on the interactions between risk, integration, and performance in supply chains (SCs) is increasingly attracting attention of researchers in recent years. Although risk usually has negative effects on performance, limited evidence has been provided to show whether companies differently exposed to operational risk (i.e., high, moderate, or low exposure) also have different levels of integration and operational performance. Therefore, this study aims to identify and characterize different profiles of operational risk (i.e., supply, manufacturing, and demand risks) between manufacturing companies along with considering contextual factors such as company size and industry type. Data are collected from the fourth round of the High Performance Manufacturing Project and subsequently analyzed by cluster analysis and analysis of variance (ANOVA). Three different clusters have been identified: Two clusters are moderately and highly impacted by operational risk, respectively, while the other cluster is almost not impacted by manufacturing risk but highly impacted by supply risk and demand risk. The results also indicate that companies with different profiles of operational risk have different levels of integration and operational performance. An important contribution of the current study is the development of a hypothesized framework of interactions between operational risk, integration, and operational performance to provide opportunities for further research.

Context-aware workflow management for smart manufacturing: A literature review of semantic web-based approaches

The purpose of this paper is to collect and structure the various features of Smart Manufacturing (SM). Researchers have previously identified various characteristics and technologies of Smart Manufacturing System (SMS); this paper collects, discusses and merges some of those characteristics and technologies available in the current body of knowledge. In the future, it is expected that this selection of characteristics and technologies will help to compare and distinguish other initiatives like Industry 4.0, smart factory, intelligent manufacturing, distributive manufacturing, etc. which are frequently used synonymous with SM. The result of this paper is a comprehensive list of characteristics and technologies that are associated with a SMS. As many of the listed items show variating overlaps, certain technologies and characteristics are merged and clustered. This results in a set of five defining characteristics and ten technologies that are considered relevant for a SMS. The authors hope to provide a basis for a broad and interdisciplinary discussion within the SM community about the defining technologies and characteristics of a SMS.

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the self-organized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.

The Industrial robots are widely used in smart manufacturing environments to improve productivity and automation, but their increasing energy consumption has become a critical issue. Traditional time-based control methods update control signals at fixed sampling intervals, which often leads to actuator redundancy and energy inefficiency, especially in steady-state or low-dynamic operating modes. To address this issue, this paper proposes an energy-efficient event-based control strategy for energy-efficient operation of industrial robots in smart manufacturing systems. The proposed approach integrates an event-based mechanism with nonlinear robot control so that control adjustments are made only when tracking accuracy or energy-related metrics exceed specified thresholds. This design significantly reduces unnecessary control signals while ensuring satisfactory trajectory tracking accuracy. A Lyapunov function-based analysis is performed to demonstrate the stability of the closed-loop control system using the proposed event-based method. Furthermore, the triggering mechanism guarantees a strictly positive lower bound for the intervals between events, thereby eliminating the Zeno effect and ensuring practical feasibility. The energy efficiency of the proposed strategy is further investigated by analyzing the behavior of the actuators and the overall energy consumption. Experimental results show that the proposed approach achieves significant energy savings compared to traditional time-based control methods without compromising control performance. This highlights its potential for energy-efficient operation of industrial robots in smart manufacturing applications.

In this paper, the authors proposed a set-theoretic model of the functioning of Smart Manufacturing (SM) system. As part of the work, an analysis of existing scientific works in the subject area was carried out. We focus on the description of the model of SM functioning as a cyber-physical system, as a result of which the physical and informational level of the model functioning are considered. The conditions and tasks for the SM functioning are defined, various types of representation of the information space are considered, and the system of access control to elementary messages is described. As one of the components of digital production, a description of the logistic system was proposed and the main parameters of its functioning were determined.

Denial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.

A framework for human-centered production planning and control in smart manufacturing

In the manufacturing industry, technologies of The Fourth Industrial Revolution have been integrated into Smart Factory and is evolving into an intelligent factory. In other words, smart factories create an optimal economic production environment by exchanging information through the Internet of Things and by providing real-time monitoring, analysis of all processes. The recent decline in birthrate, aging population and rising labor costs require automated production plants. At the same time, the more diversi- fied demands of consumers become, the shorter the life cycle of products becomes and the capability of customized mass production has emerged as the core of competitiveness. Therefore, smart factories are an essential product of innovation to adapt to these socioeconomic changes. In addition, smart factories provide breakthrough results such as high quality, reduced defect rate and improved productivity. In this study, we will first explore the cases of the operations of German automated factories, the place of origin of the Fourth Industrial Revolution and the world’s strongest manufacturer, to derive elements that can be benchmarked to Korean small and medium-sized enterprises (SME’s). Based on this, the ultimate purpose of this study is to suggest implications for the governmental projects supporting compa- nies that want to build, maintain and spread smart factories in Korea. In this study, prior literary studies on domestic and foreign papers and research reports and empirical case studies through corporate visits were used as research methodologies. While the existing research is an indirect study through domestic and international literature or information on the Internet, this study is differentiated in that it is an empirical case study of visiting demonstration. The implication is that in terms of corporate strategy, smart factories should first commence with manufacturing processes that require automation and then spread them sequentially in line with the corporate management environment (financial, market competition structure, and growth potential). Furthermore, various information and new technologies in the smart manufacturing process should be actually integrated to create optimal synergy, and various internal and external management resources such as human resources and environmental energy should be planned and implemented in an integrated manner. In terms of policy support, the focus should be on customized modular support that companies need. At the same time, it is also necessary to standardize the process of smart manufacturing through joint cooperative support for smart factories among the same companies and induce mutual utilization of generated data, minimization of use costs and maximum synergy efficiency. In particular, it is necessary to seek ways to utilize idle smart factories when ordering small or excessive amounts of orders. At the same time, expansion of smart factory supporting platforms and managemental aid are also required through a consortium between professional companies that supply new technologies to smart factories (e.g. Robots, Sense, AI, Total solution SW, VR etc.). Environment-friendly smart manu- facturing and optimal utilization of energy and materials should also be considered comprehensively. To this end, it is necessary to have a smart management organization, or a research advisory group, that studies human and environment-friendly smart manufacturing within the “Smart Manufacturing Innovation Promotion Group” to induce social innovation. It will be necessary to minimize the waste of policy support by thoroughly managing and supervising the implementation of the beneficiary entities. Finally, support for the introduction and application of smart factories for SME’s and Midsize Business should be strengthened to promote the smart manufacturing of SME’s to increase their competitiveness.

Promoting better economics, renewables and CO2 reduction through trade: A case study for the Eastern Africa Power Pool