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Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things
The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.
Journal Article
Policy agendas in British politics
\"Through a unique dataset covering half a century of policy-making in Britain, this book traces how topics like the economy, international affairs, and crime have changed in their importance to government. The data concerns key venues of decision-making - the Queen's Speech, laws and budgets - which are compared to the media and public opinion. These trends are conveyed through accessible figures backed up by a series of examples of important policies. As a result, the book throws new light on the key points of change in British politics, such as Thatcherism and New Labour and explores different approaches to agenda setting helping to account for these changes: incrementalism, the issue attention cycle and the punctuated equilibrium model. What results is the development of a new approach to agenda setting labelled focused adaptation whereby policy-makers respond to structural shifts in the underlying pattern of attention\"-- Provided by publisher.
Book
An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documents
Assembly is an essential stage in industrial electronic equipment manufacturing and needs to meet the complexity of manufacturing. Therefore, the assembly process planning for industrial electronic equipment still relies on the experiences of planners. The advent of knowledge graphs brings an opportunity to achieve automated assembly process planning. Thus, extracting process knowledge from historical assembly process documents and constructing assembly process knowledge graphs are indispensable. However, the complexity of industrial electronic equipment manufacturing leads to assembly process documents containing more complex assembly relations, longer texts, and high-density assembly entities. These characteristics pose challenges to assembly process knowledge extraction and knowledge graph modeling. The confidentiality of assembly process documents further hinders the development of this field. To address these challenges, we propose a pipeline for achieving assembly process planning from historical assembly process documents. First, we construct an assembly process dataset using historical assembly process documents from an industrial electronic equipment enterprise. Then, we propose a global relation-driven bidirectional extraction model, which automatically constructs the assembly process knowledge graph. In addition, we also propose a knowledge graph-based matching and searching method to support process planning. The proposed model is evaluated on the constructed dataset and a publicly accessible equipment fault diagnostic dataset, achieving F1-scores of 92.9% and 87.9%, respectively. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on both datasets. Furthermore, we construct an assembly process knowledge graph for industrial electronic equipment and perform assembly process planning, which validates the feasibility of our pipeline.
Journal Article
Discursive governance in politics, policy, and the public sphere
\"Discursive governance refers to implicit mechanisms of governance such as narratives, leitmotifs, and strategic metaphors in political language. It examines how the framing of policies affects political and social representations in accordance with the wishes of political authorities. Ad hoc discourses generate a space where politicians configure, transmit, and initiate politics ideationally, rather than vouchsafing substantial policy change with respect to governance. This book studies the dynamics of political discourse in governance processes. It demonstrates the process in which political discourses become normative mechanisms, first marking socially constructed realities in politics, second playing a role in delineating the subsequent policy frames, and third influencing the public sphere. The key contribution of this volume is tracing the discursive relationships among actors, namely governments and political parties, policy participants and societal actors, and the public in European nation states, intergovernmental organizations, subnational or regional entities, and geographies beyond Europe where European norms trigger ideational processes of change. The book extends earlier work in the field by exploring how policy and politics create social knowledge, make some ideas publicly salient, and bring together coalitions of actors that find certain policy alternatives attractive and eventually generate political and policy change\"-- Provided by publisher.
Book
Generative AI and DT integrated intelligent process planning: a conceptual framework
Process planning serves as a critical link between design and manufacturing, exerting a pivotal influence on the quality and efficiency of production. However, current intelligent process planning systems, like computer-aided process planning (CAPP), still contend with the challenge of realizing comprehensive automation in process decision-making. These obstacles chiefly involve, though are not confined to, issues like limited intelligence, poor flexibility, low reliability, and high usage thresholds. Generative artificial intelligence (AI) has attained noteworthy accomplishments in natural language processing (NLP), offering new perspectives to address these challenges. This paper summarizes the limitations of current intelligent process planning methods and explores the potential of integrating generative AI into process planning. With synergistically incorporating digital twin (DT), this paper introduces a conceptual framework termed generative AI and DT-enabling intelligent process planning (GIPP). The paper elaborates on two supporting methodologies: process generative pre-trained transformer (GPT) modelling and DT-based process verification method. Moreover, a prototype system is established to introduce the implementation and machining execution mechanism of GIPP for milling a specific thin-walled component. Three potential application scenarios and a comparative analysis are employed to elucidate the practicality of GIPP, providing new insights for intelligent process planning.
Journal Article
A hybrid method of blockchain and case-based reasoning for remanufacturing process planning
Remanufacturing plays a vital role in promoting the development of circular economy for its great advantages in energy saving, material saving and emission reduction. Remanufacturing process planning (RPP), which affects the performance of remanufacturing greatly, becomes increasingly important to the remanufacturing enterprises. In general, RPP is knowledge dependent. Some remanufacturing enterprises, especially small and middle-sized remanufacturing enterprises (SMREs) may have inadequate remanufacturing knowledge, which makes it difficult to implement a proper RPP. Therefore, how to share and make full use of the knowledge in different remanufacturing enterprises for RPP has become a bottleneck. To this end, a hybrid method integrating blockchain (BC) and case-based reasoning (CBR) for RPP, which can take full advantage of the remanufacturing knowledge by cross enterprises knowledge sharing, is presented in this paper. In this proposed method, a BC network was utilized to record the remanufacturing knowledge and its associated transactions to guarantee the security and reliability of knowledge sharing, and CBR was employed to retrieve and reuse the most suitable solution by analyzing the similarity between previous remanufacturing cases and new case with the nearest neighbor algorithm. Finally, a used lathe guideway was set as a case study to verify the feasibility and superiority of the proposed approach. The hybrid method has been applied in a prototype system written in HTML and JavaScript. The results indicated that the proposed approach can effectively help SMREs to obtain optimum solutions for RPP with comprehensive economic, environmental and social benefits.
Journal Article
Integrated process planning and scheduling in networked manufacturing systems for I4.0: a review and framework proposal
Integrated process planning and scheduling in networked manufacturing systems plays a crucial role nowadays and in the forthcoming context of Industry 4.0 to enable effective and efficient decisions, and to improve the business market, based on collaboration, along with computer-based distributed manufacturing and management functions. In this paper some insights regarding a literature review carried out about this main subjects analysed are presented and discussed. Moreover, a framework for integrated process planning and scheduling in networked manufacturing systems is proposed and briefly described, along with some main underlying issues, which are further discussed. Thus, the main purpose of this research consists on presenting a proposed methodology, based on the study conducted, to enable to further assist either academia or industry to develop new tools, techniques and approaches for integrated process planning in networked manufacturing environments. The findings and contributions of this research can help in the implementation and improvement in distributed manufacturing environments, to be linked with small and medium enterprises, to further expand their potentialities through well suited integrated process planning and scheduling decision making processes.
Journal Article
Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning
This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.
Journal Article
Digital twin for energy-efficient integrated process planning and scheduling
With the enhancement of environmental protection awareness, energy-efficient scheduling has attracted widespread attention. But little consideration is given to process flexibility in the production process. In fact, the integrated process planning and scheduling (IPPS) can further reduce energy consumption. Interference events often occur in production workshops, such as machine failures and new jobs arrival, which affect production operation. Therefore, a new energy-efficient IPPS (EIPPS) method based on digital twin (DT) is proposed in this paper. Firstly, the DT-based EIPPS framework of the intelligent workshop is introduced. Next, an event-based real-time mapping method is proposed to solve the problem of a low degree of virtual-real mapping. Interference events can also be quickly identified. Then, a dynamic EIPPS strategy is proposed to respond to interference events in time. In this strategy, game theory is used to reduce makespan and energy consumption. Finally, a prototype system is designed to verify the effectiveness of the proposed dynamic EIPPS strategy.
Journal Article