Search

This paper aims to understand how the knowledge field of production engineering interacts with the field of public affairs, which is related to government, public bodies and public policies for many sectors. It is structured under bibliometric analysis and reveals a panorama of 188 papers from 2000 to 2012 that presents where and how these relationships between production engineering and the public affairs occur. This study presents five different analysis to understand these possibilities: papers by year, papers by production engineering areas, papers by author, papers by journal and papers by public functions. Main findings of this research show that the leading areas where these interfaces occur in academic research are environment, sustainability, industry and organizations. Another relevant result is that the intersection of production engineering and the public sector has not been figured year as a core research topic to any individual researcher or to a group of researchers around the world. The main contribution of this paper to the academic community is the pioneering on presenting a view on the size and on the main characteristics of this interface through bibliometric analysis. [ABSTRACT FROM AUTHOR]

Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high computational speed, cloud computing and artificial intelligence techniques (like machine learning and deep learning) has reformed how many engineering and manufacturing professionals approach their work. These technologies offer thrilling innovative ways for engineers and manufacturers to tackle real-life challenges. On the other hand, the field of Artificial Intelligence (AI) is extensive. Several diverse theories, algorithms, and methods are available, which presents a challenge and a barrier in choosing the right AI technique for the appropriate engineering process or manufacturing process and environments. Besides, the pertinent literature is disseminated over various journals, conference proceedings, and research communities. Hence, conducting a systematic survey to scrutinise and classify the existing literature is worthwhile. However, it is challenging, but previous review studies have not adequately addressed AI's use and advancement in engineering and manufacturing (EM). Besides, some concentrated on single AI models, and others focused on a specific area in EM. This paper presents a comprehensive systematic review of studies on AI and its application in EM. To limit the scope of the current study, we conducted a keyword search in official publisher websites and academic databases, such as Springer, Elsevier, Scopus, Science Publication, Taylor & Francis, Directory of Open Access Journals (DOAJ), Association for Computing Machinery (ACM), Wiley online library, Inderscience and Google scholar. The search results (173 articles) were filtered according to a proposed framework, which resulted in ninety-one (91) relevant research articles. We reviewed the articles based on a proposed taxonomy (the year of publication, the AI algorithm and machine learning task adopted, the application area in EM, the train and test split of data, the error, and accuracy metrics used, the potential benefits). Our assessment using the proposed taxonomy gave a helpful insight into the literature's anatomy on various AI applications in engineering and manufacturing. Also, we identified opportunities for future research in AI application in the field of EM. [ABSTRACT FROM AUTHOR]

Fixtures play a significant role in ensuring the quality of products in manufacturing engineering. The paper considers fork-type parts that have a complex spatial configuration and needs to be machined by drilling-milling-boring equipment in one setup. In fixture design, it is necessary to provide wide tool accessibility, and therefore the choice of locating surfaces of the workpiece is an important step. The mathematical approach for determining the coordinates of contact points between the workpiece and the locating and clamping elements fixtures in the coordinate system is developed in this paper. The geometric parameters of the workpiece are taken into account in determining the coordinates. It allows to apply the proposed approach for parts of another standard size, but the same design and technological features. The technology of augmented reality is an effective tool at the design stage of fixtures and allows to visualize the developed fixture configurations. This approach has been successfully applied for such parts as levers, connecting rods, flat parts. [ABSTRACT FROM AUTHOR]

This theme section aims to disseminate the latest research results in the area of Multi-Paradigm Modeling for Cyber-Physical Systems (MPM4CPS). MPM has a long tradition within the Model-Driven Engineering community, e.g., several workshops have been held at the MODELS conference for over more than a decade. The MPM4CPS workshop series is a continuation of the successful MPM workshop series with a stronger focus on CPS as especially these systems pose several new challenges on the engineering process and beyond. This theme section covers papers on the foundations and applications of MPM for CPS. In total, we accepted five submissions for publication in the theme section after a thorough peer-reviewing process. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical multi-agent based routing has been introduced. In dynamic situations, the previously planned entire optimum path may not stay optimum over time. Thus the approach in this paper routes a job to the next optimum neighboring node from the current position, instead of deciding over the entire path before the journey begins. Whenever there is a need to choose the next optimum node for routing or whenever a job enters the system, the master agent calls the worker agents. The worker agents run in parallel and return the results to the master agent. The worker agents are killed after their tasks are completed. The master agent takes decision based on the data delivered by the worker agents through a multi-criteria decision analysis technique known as PROMETHEE. A total of five worker agents are used for seven criteria and fuzzy approach is applied in a fuzzy shortest path algorithm performed by a worker agent and in fuzzy weight calculation in PROMETHEE. Three examples with three different kinds of networks have been used to show the effectiveness of the entire approach. The motivation of the idea introduced in this paper has come from the mating behavior of a spider known as Tarantula where the female spider sometimes eats the male spider just after mating. [ABSTRACT FROM AUTHOR]

The article presents a discussion with Stephen H. Fuller, Berth Jönsson, Donald F. Ephlin, and Dr. Jeremiah J. O'Connell. Ephlin comments on the quality of work life of employees of General Motors and how negotiations between the company and the labor union representing the employees affected job satisfaction. Jönsson says that Volvo's Kalmar Plant still utilizes teams in the workplace and is very effective.

Since the 1950s, the field of process systems engineering (PSE) research experienced growth. PSE research has contributed several ways in which a process or energy system could be designed. To describe the aforementioned "ways" to design process or energy systems, several papers have used varied terminologies such as approach, methodology, method, procedure and technique (AMMPT). These five terms have been used frequently and interchangeably. However, these five terms do possess distinct meanings and must be used appropriately. In this respect, we argue for the need for a systematic framework to clearly distinguish AMMPT terminologies from the PSE perspective. In this paper, we offer a perspective on AMMPT terminologies and provide a conceptual framework to understand the distinctions between them as well as how they could be used appropriately in PSE. [ABSTRACT FROM AUTHOR]

This paper reviews the literature regarding Genetic Algorithms (GAs) applied to flexible manufacturing system (FMS) scheduling. On the basis of this literature review, a classification system is proposed that encompasses 6 main dimensions: FMS type, types of resource constraints, job description, scheduling problem, measure of performance and solution approach. The literature review found 40 papers, which were classified according to these criteria. The literature was analyzed using the proposed classification system, which provides the following results regarding the application of GAs to FMS scheduling: (1) combinations of GAs and other methods were relatively important in the reviewed papers; (2) although most studies deal with complex environments concerning both the routing flexibility and the job complexity, only a minority of papers simultaneously consider the variety of possible capacity constraints on an FMS environment, including pallets and automated guided vehicles; (3) local search is rarely used; (4) makespan is the most widely used measure of performance. [ABSTRACT FROM AUTHOR]

Organizations are actively seeking efficient solutions for the management and protection of their assets. However, Cybersecurity is a vast and complex domain, especially for large enterprises because it requires an interdisciplinary approach. Knowledge Graphs are one of the mechanisms that organizations use to explore security among assets and possible attacks. The grounding of concepts is fundamental to implementing Knowledge Graphs, and it is one of the most relevant ontology applications. Therefore, Cybersecurity Ontologies have emerged as an important research subject. The first contribution of this paper is a search for previously existing works that have defined Cybersecurity Ontologies. We found twenty-eight ontologies in this search. Based on this result, we propose a Cybersecurity Terminological Validation and a Framework for Classifying Ontologies. Then, we provide a cross-analysis of these two proposals and present a proposal of best practices for improving the ontological approach in the cybersecurity domain. We also discuss the impact of this proposal with regard to the Ontology Engineering process. Our goal is to provide a solution that meets the organization's needs in terms of Cybersecurity and to contribute to Ontology Engineering research. [ABSTRACT FROM AUTHOR]

The authors present a set of new measures for comparing optimization algorithms in dynamic environments. The authors introduce a new decomposition based multi-objective genetic algorithm with adaptive multi-population strategy to address multi-objective permutation flowshop scheduling problems. The authors describe a self-adaptive fruit fly optimization algorithm for solving high-dimensional global optimization problems. [Extracted from the article]

This paper presents investigation of surface veracity of Wire EDM machined surfaces of Al / ZrO 2 (p) - MMC obtained utilizing the optimal parametric combinations. The effect of process parameters on the desired performance characteristics, i.e., minimum spark gap (SG), maximum material removal rate (MRR) and minimum surface roughness [SR( R a & R t )], is also presented. The second-order numerical expressions developed by response surface methodology (RSM) are used as a function in.M file. Genetic Algorithm and Direct Search Toolbox of MATLAB ® are utilized to identify the multi-objective optimum set of the process parameters for the desired performance characteristics. In the present paper, surface veracity aspects that include surface defects and thickness of recast layer are investigated for the machined surfaces obtained after Wire EDM to achieve the desired optimum performance characteristics. From the investigation, it can safely be concluded that for the Wire EDM of Al / ZrO 2 (p) - MMC s, lower parameter setting values of pulse width and short pulse time along with higher parameter setting value of time between pulses are desirable from the view point of good surface veracity of the machined surfaces as well as to obtain low average recast layer thickness ( Rl tavg ). The Rl tavg decreases as weight fraction of ZrO 2 (p) increases in Al / ZrO 2 (p) - MMC from 5 to 15%. The investigation of surface veracity of the Wire EDM obtained surfaces will provide the significant guidelines to the production engineers to plan the need of subsequent operations to improve the surface veracity and further the life of the products fabricated using these MMCs. [ABSTRACT FROM AUTHOR]

The paper deals with the situation of manufacturing of customized engineering products under intense competitive environment. Such customized engineering products can be manufactured by different types of production systems such as job production, batch production, mass production, and flexible manufacturing systems. It then becomes an important decision to choose the best one among these production systems which is also dependent on multiple criteria. The three basic criteria predominantly considered in the context of manufacturing are cost, quality, and flexibility. The paper applies the efficient interpretive ranking process (IRP) as an interpretive multi-criteria ranking method to demonstrate its utility in this context. It considers the design of the decision problem by treating the cross-interaction matrix of 'Alternatives × Criteria' as a unit matrix, i.e. each alternative is linked with each criterion. In this type of decision problem, the IRP method is implemented with transitive dominance relationships. Further, it carries out the sensitivity analysis concerning different ordinal weights of various criteria. The paper also makes a methodological contribution in IRP by way of computing dominance index rather than simply considering net dominance (which would be either positive or nil or negative). The dominance index gives a better depiction of the dominance of one alternative over the others. [ABSTRACT FROM AUTHOR]

Abstract. During engineering processes, many kinds and amounts of information are used and produced. Such information is useful in successive cases, and thus its reuse is desired. In this paper, information on attributes of the products is referred to as 'product information', and information on the reasons for and/or history of a designer's thought process is referred to as 'process information'. According to this classification, process information can be used for obtaining an explanation of product information. The purpose of this work is to build a database, called an 'engineering history base', from which engineers can retrieve explanations to enable the reuse of product information. In this paper, explanation from the 'process' viewpoint is thought to be important in promoting the reuse of product information. There are two types of explanation for the process: teleological and causal. However, until now, little attention has been paid to the causal explanation for the process. Thus, in this paper, an information model which focuses on both types of explanation is proposed. The model is adopted for the engineering history base and a prototype system is developed. The appropriateness of this model is discussed by analyzing the actual data in the development of a color video printer. [ABSTRACT FROM AUTHOR]

Recently, sustainable development has obtained increasing attentions from governments, industry, and academia owing to the limited natural resources. In the area of energy consumption, manufacturing accounts for a major portion of the total energy usage in industry. There is a clear necessity for energy-efficient manufacturing by optimizing manufacturing activities. Condition monitoring is the technology that provides runtime information for optimization. This paper aims to provide a better understanding of past achievements and future trends of condition monitoring towards energy-efficient manufacturing. Since there are a variety of sensors and technologies that can be used for condition monitoring towards energy-efficient manufacturing, this paper divides manufacturing activities into three levels, namely unit process level, shop-floor level, and supply chain level, and summarizes and discusses the sensors and technologies required to enable energy-efficient manufacturing on each level. With the advancement of technology, condition monitoring shows the characteristic of intelligence. Intelligent sensors that can be applied to condition monitoring in energy-efficient manufacturing are also reviewed. This paper can be helpful to manufacturers who are willing to improve energy efficiency in own manufacturing practice. [ABSTRACT FROM AUTHOR]

This paper considers the fitting of a CAD template model to tessellated data as strategy to implement a reverse engineering process that aims at the reconstruction of a parametric associative CAD model. The reconstruction methodology, called Template-Based CAD Reconstruction (TCRT), has been presented and fully discussed in a previous paper Buonamici et al. (J Comput Des Eng 5:145–159, 2018). The present paper focuses on the study of a fast and robust strategy to perform the fitting of the Template CAD Model to reference data. The study explores how different optimization strategies and evaluation metrics can affect a parametric CAD-fitting methodology. Two different optimization algorithms (PSO and GA) and three formulations of the objective function are tested to find the most effective combination. Reconstruction test cases are presented and discussed in the text. [ABSTRACT FROM AUTHOR]

Mass customization (MC) is recognized as a new manufacturing paradigm for market competition, and several empirical studies have discussed elements of organizational structure that are conducive to MC. A previous study proposed a model of quality at source and identified cultural values that can be changed by cultural artifacts, such as the stop-the-line system and the Andon system. This paper explores the impacts of three groups of MC enabler variables (personalized design, modular design, and automated processes) on two internal dimensions of organizational culture (work cooperation and relationship harmony).This paper surveyed 160 samples across five manufacturing industries. The results confirm that the three MC enablers (personalized design, modular design, and automated processes) correlate positively with two internal dimensions of the organizational culture of Chinese manufacturers (work cooperation and relationship harmony). Of the two internal dimensions, work cooperation enhances organizational performance, while relationship harmony has no impact on organizational performance. Modular design, automated processes, and work cooperation are powerful tools in the implementation of MC and organizational performance. Personalized design and relationship harmony are useful in MC. However, these tools are intermediate and limited, and they do not contribute to the general performance of the entire organization. Thus, in terms of internal- and team-oriented cultural factors, work cooperation is more important than relationship harmony in the implementation of MC. Relationship harmony is a tool for managing work atmosphere, and this is a new characteristic feature of MC as a new operational paradigm. [ABSTRACT FROM AUTHOR]

Smart contracts are seen as the major building blocks for future autonomous blockchain- and Distributed Ledger Technology (DLT)-based applications. Engineering such contracts for trustless, append-only, and decentralized digital ledgers allows mutually distrustful parties to transform legal requirements into immutable and formalized rules. Previous experience shows this to be a challenging task due to demanding socio-technical ecosystems and the specificities of decentralized ledger technology. In this paper, we therefore develop an integrated process model for engineering DLT-based smart contracts that accounts for the specificities of DLT. This model was iteratively refined with the support of industry experts. The model explicitly accounts for the immutability of the trustless, append-only, and decentralized DLT ecosystem, and thereby overcomes certain limitations of traditional software engineering process models. More specifically, it consists of five successive and closely intertwined phases: conceptualization, implementation, approval, execution, and finalization. For each phase, the respective activities, roles, and artifacts are identified and discussed in detail. Applying such a model when engineering smart contracts will help software engineers and developers to better understand and streamline the engineering process of DLTs in general and blockchain in particular. Furthermore, this model serves as a generic framework which will support application development in all fields in which DLT can be applied. [ABSTRACT FROM AUTHOR]

The firefly algorithm is nature-inspired, and it belongs to swarm intelligence category optimization. Firefly algorithm is the latest algorithm developed under the nature-inspired algorithm (NIA), which is fruitful for business and engineering optimization. So, for business optimization, healthcare industries are affected through big data where big data affects in different ways such as patients understanding and care, improved personalized care, analysis trends, predict health outcomes, etc. Big data analysis is required to optimize a broad set of data generated by the different mediums in the healthcare and engineering sectors. A firefly algorithm can be used to optimize data analysis and results. Therefore, the firefly algorithm becomes essential to optimize the healthcare sector application process and provides optimized solutions. This paper discussed firefly algorithm implementations, optimization solutions for an engineering problem, fireflies' algorithm (FA) performance through MATLAB 2019b, formulation, optimization problems in healthcare, firefly algorithms in other applications, and conclusions. This paper focuses on how the firefly algorithm (FA) can be used, modified, and merged with other optimization algorithms to solve engineering problems. [ABSTRACT FROM AUTHOR]

Implementing machine learning in an enterprise involves tackling a wide range of complexities with respect to requirements elicitation, design, development, and deployment of such solutions. Despite the necessity and relevance of requirements engineering approaches to the process, not much research has been done in this area. This paper employs a case study method to evaluate the expressiveness and usefulness of GR4ML, a conceptual modeling framework for requirements elicitation, design, and development of machine learning solutions. Our results confirm that the framework includes an adequate set of concepts for expressing machine learning requirements and solution design. The case study also demonstrates that the framework can be useful in machine learning projects by revealing new requirements that would have been missed without using the framework, as well as, by facilitating communication among project team members of different roles and backgrounds. Feedback from study participants and areas of improvement to the framework are also discussed. [ABSTRACT FROM AUTHOR]

The literature on the modeling and management of data generated through the lifecycle of a manufacturing system is split into two main paradigms: product lifecycle management (PLM) and product, process, resource (PPR) modeling. These paradigms are complementary, and the latter could be considered a more neutral version of the former. There are two main technologies associated with these paradigms: ontologies and databases. Database technology is widespread in industry and is well established. Ontologies remain largely a plaything of the academic community which, despite numerous projects and publications, have seen limited implementations in industrial manufacturing applications. The main objective of this paper is to provide a comparison between ontologies and databases, offering both qualitative and quantitative analyses in the context of PLM and PPR. To achieve this, the article presents (1) a literature review within the context of manufacturing systems that use databases and ontologies, identifying their respective strengths and weaknesses, and (2) an implementation in a real industrial scenario that demonstrates how different modeling approaches can be used for the same purpose. This experiment is used to enable discussion and comparative analysis of both modeling strategies. [ABSTRACT FROM AUTHOR]

Thread milling is increasingly used in engineering manufacturing because of its advantages, but there must be some error in the thread due to the machining principle. In response to this problem, this paper discussed the causes of thread milling error and established the machining error model for the full machining process of thread milling. Firstly, the geometric model of thread milling is developed based on the parametric definitions of the cutter tooth surface, thread surface, and tool-path. Then, based on the cutter motion characteristics of the thread milling penetration and retraction segments and machining segment, the machining error model is established. The calculation of machining error is transformed into solving for the relationship between the position of the intersection of the cutter profile line and the thread profile line generated in the cross-section, which means that the three-dimensional problem is transformed into the two-dimensional problem. Finally, the proposed model is proven via thread milling experiments. The simulation results of machining error agree well with the experimental results, which prove the correctness and validity of the model. The model provides theoretical support for further research into thread milling machining error. [ABSTRACT FROM AUTHOR]

The industrial domain is undergoing a major transformation, pushed forward by emerging technologies originating from research or industry. The resulting trend, better known by the term Industry 4.0, advances automation within these manufacturing companies by providing ubiquitous interconnection. This enables the integration of technologies mainly used in the Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS) or Big Data with the goal to optimize production processes or facilitate intelligent decision-making. While those processes are progressively supported by methodologies coming from the area of artificial intelligence (AI) like machine learning algorithms, sustainable and consistent storing of production data becomes increasingly important. Concluding, production systems engineering and information engineering are correlating with each other, as the respective results could be used to the advantage of the respective other discipline. In order to address these issues while developing such flexible production systems, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been introduced. However, practical applications are lacking as this standard is mainly described in theory, which makes it difficult to actually apply this framework. Thus, the main goal of this paper is to specify a detailed architecture description of the Information Layer to ensure the practical application of RAMI 4.0, which allows stakeholders to utilize model-based Systems Engineering (MBSE) for developing data aspects of industrial systems on the one hand and enable Information Engineering on the other hand. Supported by the concept of the Zachman Framework, the resulting architecture is applied and validated with the help of a real-world case study. [ABSTRACT FROM AUTHOR]

Some content forms, such as animation, are superior in many aspects but rarely created in UGC systems partially due to the lack of available content-producing tools. Requirements engineering of User-Generated Content project can be difficult because of the diversities of user types, user abilities and client performance resulted from heterogeneous stakeholders. Based on the joint perspective of requirement analysis and quality control, this paper aims to propose an Exploratory Factor Analysis (EFA) integrated Quality Function Deployment (QFD) method. It divides users into expert and regular users, and allows the former to directly participate in design decisions and the latter to participate in prioritization. A case study of the requirements engineering of a Web-based animation application was carried out. The results showed the applicability of the proposed method to develop and manage requirements from all stakeholders. The integration of EFA contributed to the identification of unstated requirements to some extent. Also, the House of Quality was constructed and the quality requirements were obtained during the analysis. This approach helps analysts to sort out the priorities of user and technical requirements and achieve balance between them. [ABSTRACT FROM AUTHOR]

In the former paper (Liu et al. Int J Adv Manuf Technol 41:66–74, ), a novel policy was proposed to prevent deadlocks in a class of flexible manufacturing systems (FMSs). This policy is based on the notion that each shared buffer is partitioned for storing different types of parts. Furthermore, A subclass of Petri nets, resource- shared net with buffers (RSNB), was defined to characterize this policy and model the controlled system. Two extensions of RSNB are defined in this paper. The first extension, which is called extended resource- shared net with buffers (ERSNB), permits the resources to be shared among different stages in one manufacturing process. The second extension, which is called resource- shared assembly net with buffers (RSANB), takes the assembly and choice operations into account. Both ERSNB and RSANB have a well property that each minimal siphon is exactly a minimal trap. It has been proven that an ERSNB or RSANB is live if and only if each minimal siphon is marked under the initial marking. Two well-known examples of FMSs are used to illustrate the proposed concepts and policy. [ABSTRACT FROM AUTHOR]

This paper is concerned with fault detection and diagnosis problem in manufacturing systems. In such industrial environment, production systems are subject to several faults caused by a number of factors including the environment, the accumulated wearing, usage, etc. However, due to the lack of accuracy or fluctuation of data, it is oftentimes impossible to evaluate precisely the correct classification rate of faults. In order to classify each type of fault, neural networks and fuzzy logic are two different intelligent diagnosis methods that are more applied now, and each has its own advantages and disadvantages. A new hybrid fault diagnosis approach is introduced in this paper that considers the combined learning algorithm and knowledge base (Fuzzy rules) to handle ambiguous and even erroneous information. Therefore, to enhance the classification accuracy, three perceptron models including: linear perceptron (LP), multilayer perceptron (MLP) and fuzzy perceptron (FP) have been respectively established and compared. The conditional risk function “PDF” that measures the expectation of loss when taking an action is presented at the same time. We evaluate the proposed hybrid approach “Variable Learning Rate Gradient Descent with Bayes’ Maximum Likelihood formula” VLRGD-BML on dataset of milk pasteurization process and compare our approach with other similar published works for fault diagnosis in the literature. Comparative results indicate the higher efficiency and effectiveness of the proposed approach with fuzzy perceptron for uncertain fault diagnosis problem. [ABSTRACT FROM AUTHOR]

In order to enhance the strength of 3D-printed parts made of polymer materials and reduce the anisotropy caused by the fused filament fabrication process, this paper proposes an inter-layer interleaved composite path planning method based on the directional partition of the principal stress field. This method not only ensures intra-layer reinforcement under specific working conditions but also increases the strength of the printed part by enhancing the adhesion of adjacent layers through staggered filling between layers. The mechanical performance is improved by 10 to 30% compared to other conventional filling patterns such as the zigzag filling algorithm. Inter-layer interleaved composite enhancement path planning method is suitable for path planning of structures with complex shapes and is easily integrated into existing general computer-aided engineering processes. [ABSTRACT FROM AUTHOR]

The paper discusses the aspects of granulometric and morphological analysis of powders used in the technology of selective laser melting. Studies have been carried out to obtain statistical data concerning the particle size distribution, as well as their integral and differential distribution. The aspects of independent significance in this work had to do with identifying the morphological features of the particles. Experimental studies were carried out using certified and validated equipment. Conclusions were drawn about the specific features of the analyzed powders, and directions for further research were proposed, including the formation of databases of powder materials, which can be of value to process engineers who use additive technologies in practice. [ABSTRACT FROM AUTHOR]

Machine learning methods have shown potential for the optimization of production processes. Due to the complex relationships often inherent in those processes, the success of such methods is often uncertain and unreliable. Therefore, understanding the (algorithmic) behavior and results of machine learning methods is crucial to improve the prediction of production processes. Here, mathematical tools may help. This paper shows how efficient algorithms for the training of neural networks and their retraining in the framework of transfer learning are expressed in a discrete as well as a time-continuous formulation. The latter can be analyzed and investigated using mathematical techniques from kinetic gas dynamics. The results obtained provide a first step towards explainable artificial intelligence. Based on the mathematical description, an adapted ensemble method for retraining of neural networks is proposed and compared with backpropagation algorithms. The process of training and retraining is a common task and therefore demonstrated for two very different production processes. The first one involves the prediction of specific cutting forces and the second one the prediction of particle properties in a plasma spraying coating process. For both use cases, the presented algorithms are applied and their performance is evaluated giving thereby an indication how mathematically inspired methods improve classical tasks in production processes. [ABSTRACT FROM AUTHOR]

The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: "SysML – AutomationML" and "SysML4Mechatronics – AutomationML." Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems. [ABSTRACT FROM AUTHOR]

Riveting is an important interference-fit joining technology, which has been widely applied in aircraft assembly and many manufacturing fields. The rivet head flushness is an import industrial standard for the quality evaluation of a riveted joint. It would influence the aerodynamic performance of aircraft if not carefully controlled. In this paper, a finite element model (FEM) of automatic countersunk riveting is established and the formation of rivet head flushness is studied by the simulation results. Then, based on the coordinated motion of the riveting bar and the anvil tool, several compensation strategies are proposed to reduce the rivet head flushness and ensure the riveting quality, in which the riveting bar and anvil tool feed with the same speed (RASS) strategy is considered as the most efficient one. Finally, riveting experiments are conducted on a dual-machine-based automatic drilling and riveting system. The experimental results indicate that the simulation result is accurate, and the RASS compensation strategy is applicable and effective. This paper studies the formation mechanism and compensation method of the rivet head flushness for the countersunk rivet and provides scientific guidance for the riveting process optimization in engineering applications. [ABSTRACT FROM AUTHOR]

Aircraft structural analysis is a process that involves several engineers working concurrently to analyze in detail all structural elements of an airframe, as well as the behavior of the aircraft structure as a whole. The airframe has to be decomposed in its major components such as fuselage sections, wings and control surfaces to allow the distribution of the analyses among the engineers. Finite element models (FEM) are created for each major component and used in the analyses. The major components, such as the wing, are further decomposed into its constituent parts, such as spars, ribs, and stringers; which might be modeled in more detail using dedicated FEMs. It's a great challenge to manage the evolution of the structural modifications that happen during the course of these analyses. Design changes originated from stress analysis using higher fidelity FEMs have to be transferred to the lower fidelity FEMs and an assembly of the whole airframe FEM needs to be created and analyzed once again, using the correct versions of all the FEMs of the major parts. The objective of this paper is to present the results of the solution implemented by Embraer to overcome these challenges, by using the Simulation Process and Data Management (SPDM) technology. Embraer embarked on the SPDM journey eight years ago and during past years has matured various aspects of their SPDM implementation. The focus of the paper is on explaining applications of the SPDM system in the area of structural analysis at Embraer with a couple of representative business processes as examples, including the rapid generation and evaluation of aircraft assemblies and the consolidation and standardization of various simulation methods across the organization, by making those available over the SPDM platform. Key enablers for increasing simulation throughput and data traceability at Embraer are described. In addition, the approach that Embraer took for ongoing additions of new business processes to the SPDM system is presented. The results of the SPDM implementation at Embraer are encouraging, showing qualitative and quantitative gains of productivity and management of engineering data. The standardization and automation of engineering processes and the proper management of data generated by these processes are becoming an integral part of any competitive engineering department nowadays. [ABSTRACT FROM AUTHOR]

The paper considers the current state of the problem of choosing methods for surface hardening of metallic materials aimed at ensuring advanced tribological characteristics of friction pairs. Several technological processes of surface engineering are analyzed in the paper. The information on the macro- and microstructure of coatings, obtained by ion beam doping, thermochemical treatment, and vapor and gas deposition of coatings by PVD and CVD technique, is considered. The data on the wear characteristics of modified layers obtained by various techniques under dry and boundary friction conditions are presented. [ABSTRACT FROM AUTHOR]

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the Teaching–Learning-based Optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production scheduling problems. [ABSTRACT FROM AUTHOR]

Modern cutting tools like end mills, drilling tools, and reamers underlie high requirements regarding geometrical accuracy, cutting edge quality, and production costs. However, the potential for process optimization is limited due to the process kinematics during grinding. Consequently, a novel tool grinding process for the manufacture of cutting tools has been developed recently at the Institute for Production Engineering and Machine Tools (IFW). This continuous generating grinding process allows the simultaneous production of all flutes and circumferential flank faces of rotational symmetrical cutting tools. The present paper focuses on the geometrical process design and develops a method to determine the necessary basic rack and process parameters in order to create a desired cutting edge geometry by continuous generating grinding. The developed method can define all parameters with an accuracy of up to 5 µm and 0.2° within a simulation in five iteration steps and allows not only the quantitative design of the cutting tool geometry but a qualitative modification of the flute geometry as well. Subsequently performed grinding tests showed that the presented method allows the design of grinding worms for continuous generating grinding of cutting tools and enables the successful implementation of these processes. [ABSTRACT FROM AUTHOR]

Collagen extracted from fishes has been appearing as an alternative for commercial porcine and bovine collagen and it has been considered interesting especially for membrane manufacturing in tissue engineering. Despite the positive in vitro effects of fish collagen membranes, there is still no understanding of all the benefits that this natural biomaterial plays in the wound healing process, due to the lack of compilation of the results obtained in animal studies. In this sense, the purpose of this study was to perform a systematic review of the literature to examine the effects of fish collagen membranes for skin wound healing in experimental models of skin wound. The search was carried out according to the orientations of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and the descriptors of the Medical Subject Headings (MeSH) were defined: "fish," collagen," "skin," and "in vivo". A total of 10 articles were retrieved from the databases PubMed and Scopus. After the elegibility analyses, this review covers the different origins of fish collagen reported in the different papers from the beginning of 2015 through the middle of 2021. The results were based mainly on histological analysis and macroscopic evaluation, and fish skin collagen was responsible for improving the wound healing rate and the process of reepithelization and collagen deposition. In conclusion, fish skin collagen has shown positive results in in vivo studies and may be a potential biomaterial in tissue engineering. [ABSTRACT FROM AUTHOR]

The removal of platinoids (Pd, Rh, and Ru) from irradiated fuel the yield of which is kilograms per ton is a complex problem due to a number of technological and economic reasons. The paper proposes ways of a phased approach to the introduction of technology for the recovery of platinum-group metals (PGM) at one of the radiochemical enterprises. Information is presented on the dynamics of production volume and prices of PGMs in the world over the past decades and on the possibility of using reactor platinoids in various fields. According to the authors, it is of interest to consider the problem of isolating and using reactor Pd in ​​hydrogen power engineering and in the processing of waste from radiochemical enterprises. [ABSTRACT FROM AUTHOR]

Process reuse technology has been widely studied and applied in the manufacturing industry. However, the current NC machining process reuse generally assumes that the micro process is compatible with the macro process but, in fact, reusable processes from similar local structures from multiple parts are usually incompatible with each other when under the overall manufacturing requirements of the target parts. As a result, a large number of user interactions are still required for modification and adjustment in practical engineering applications and process reuse do not provide a significant improvement to the design efficiency. Therefore, an efficient NC process scheme generation method based on reusable macro and micro process fusion is proposed in this paper. Firstly, according to the calculation of semantic distance of process design intention, the micro process is mapped to the macro process to realize fusion of the two processes, and a compatibility credibility evaluation model is established to evaluate the fusion results. Then, when the fusion result is credible, the machining areas corresponding to the process scheme are adjusted and optimized at the geometric level. The adjustment and optimization of machining areas provides the integration of machining areas and the optimization of the machining sequence. Finally, the effectiveness and feasibility of the proposed method are verified through a test of example parts. [ABSTRACT FROM AUTHOR]

Most scheduling papers consider flexible machining and assembly systems as being independent. In this paper, a heuristic two-level scheduling algorithm for a system consisting of a machining and an assembly subsystem is developed. It is shown that the upper level problem is equivalent to the two machine flow shop problem. The algorithm at the lower level schedules jobs according to the established product and part priorities. Related issues, such as batching, due dates, process planning and alternative routes, are discussed. The algorithm and associated concepts are illustrated on a number of numerical examples. [ABSTRACT FROM AUTHOR]

Multiphase flow modeling is crucial for the oil and gas industry, enabling safer and more cost-effective subsea well development. In the past 60 years, there have been significant advancements in this field, addressing complex challenges in petroleum engineering. Different topics have been developed over time, some documented in literature while others require further research. The key question is the current level of development and capabilities of advanced simulators based on multiphase flow models. This paper classifies multiphase flow models and methods, including transient mechanistic models. In petroleum engineering, multiphase simulations utilize two primary methods: steady-state and unsteady-state (transient) methods. Steady-state methods are categorized into correlations and mechanistic models, depending on the formulation of the flow equation. Meanwhile, transient methods in the system code can be further classified into empirical slug specification models, slug-capturing models, and mechanistic models. This study also discusses the applications in oil engineering and lists applications in oil production engineering, specifically subsurface and surface systems. It covers steady-state and dynamic multiphase flow simulators in the oil industry, highlighting their continuous development for better simulations. The paper compares dynamic and steady-state multiphase flow simulators, discussing their applications and differences. The authors designed and constructed the tables, providing recommendations for unexplored research areas. The study concludes that there has been development in multiphase flow models and simulators, but there are still areas requiring further research. [ABSTRACT FROM AUTHOR]

Ranking aggregation is an ancient problem with some characteristic elements: a number of experts, who individually rank a set of objects according to a certain (subjective) attribute, and the need to aggregate the resulting expert rankings into a collective judgment. Although this problem is traditionally very popular in fields such as social choice, psychometrics, and economics, it can also have several interesting applications in manufacturing, e.g., for customer-oriented design, reliability engineering, production management, etc. Through a case study related to a cobot-assisted manual (dis)assembly, the paper illustrates an operational methodology and various useful tools that assist in tackling the problem practically, effectively, and with a critical mind. Some of the proposed tools allow to estimate the degree of concordance among experts, and the collective judgment’s consistency and robustness. The paper is aimed at scientists and practitioners in manufacturing. [ABSTRACT FROM AUTHOR]

Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such "industrial big data" can relay its usefulness to reach further utilitarian applications. In this context, Machine Learning (ML) is among the major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes. [ABSTRACT FROM AUTHOR]

Game-based learning (GBL) is familiar in several respects, as it can embed problem-solving challenges in an interactive virtual environment to improve students' motivation. Designing a stealth assessment is a tedious task that requires experienced professionals to manually derive evidence and competency approaches to precisely deduce student knowledge and skills. This necessitates that stealth assessment designers perform manual feature engineering and develop probabilistic graphical models. Since the manual feature engineering process is complex, this paper develops Optimal Deep Learning with GBL (ODL-GBL) for stealth assessment. The proposed ODL-GBL model involves parameter-tuned bi-directional long short-term memory (Bi-LSTM) with an oppositional salp swarm algorithm. The optimal Bi-LSTM model extracts hierarchical features from low-level data in an automated way (e.g., a series of student behaviours in a GBL platform) to infer evidence for competencies. The proposed ODL-GBL model alleviates the need for the costlier and more tedious process of developing evidence models. A detailed experimental analysis is used to validate the effective predictive performance of the proposed method. Among all the compared methods, the Bi-LSTM model shows superior performance, with accuracies of 60.56% and 61.34% under 70 and 40 hidden units, respectively. [ABSTRACT FROM AUTHOR]

Manufacturing industry plays a very significant role in the economic functioning of any country. In recent times, reverse engineering (RE) has become an integral part of manufacturing set-up owing to its numerous applications. The quality of RE product primarily depends on the quality of digitization i.e., part measurement. There is a diverse range of digitization devices which can be employed in RE. These machines have variability in terms of cost, accuracy, ease of use, accessibility, scanning time, etc. Therefore, the decision regarding the selection of a suitable device becomes important in a particular RE application. The decisions taken in the planning stage for RE can have a long lasting impact on the functionality, quality and the economics of components to be used by manufacturing industries. To accomplish the selection procedure, a comparative study of three digitization techniques has been carried out. The determination of an appropriate digitization system is basically a multi-criteria decision making (MCDM) problem. MCDM techniques are yet to be applied in the selection of digitization systems for RE. MCDM is one of the most widely used decision methodologies in business and engineering spheres. The aim of this work is to describe various MCDM methods in the selection of digitization systems for RE. This paper intends to employ combinations between different MCDM methods such as group eigenvalue method (GEM), analytic hierarchy process (AHP), entropy method, elimination and choice expressing reality (ELECTRE), technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighing (SAW) method. In this work, GEM, AHP, Entropy methods has been used to elicit weights of various selection criteria, while TOPSIS, ELECTRE and SAW have been applied to rank the alternatives. A comparative analysis has also been performed to determine the efficacies of different approaches. The conclusion of the paper reveals the best digitization system as well as the characteristics of different MCDM methods and their suitability in RE application. [ABSTRACT FROM AUTHOR]

This paper was motivated by the requirement of remote coordination for flexible manufacturing systems (FMS), in which certain requested commands may lead to undesired states. For such discrete event systems (DES), this paper applies a command filtering scheme for modeling and synthesis of filters using Petri nets so as to guarantee that the closed coordinated manufacturing system satisfies the requirements. Moreover, a systematic algorithm for the design and integration of modular command filters is proposed in order to cope with the complexity and realization issues in large-scale FMS. To show the practicability of the developed method, an application to an FMS with three product types is provided. The proposed approach contributes a promising tool for preventing undesirable actions from occurring in FMS which may be also applied to other types of DES if appropriately modified. [ABSTRACT FROM AUTHOR]

In the context of engineering-oriented companies, a basic distinction can be made between the information needed during sales, engineering and manufacturing. When representing such information in diagrammatic models, these models will most often include both individual and shared content. For instance, during the sales phase, information about sales prices may be relevant, while this information is not relevant in the engineering and manufacturing phases, where, on the other hand, more detailed information about components and assembly is needed. However, such information models often share basic component definitions. Having this overlapping information across models means that when maintaining these, redundant work has to be carried out for the overlapping parts of the information. This, obviously, can be both time-consuming and a significant source of errors. In this paper, a modelling method for the management of diagrammatic models with different perspectives on product information is proposed. The aim of this method is to avoid redundant information across models, in order to reduce time needed for ensuring of consistency across models and minimize the chances of errors. The need for information models with different perspectives on the same product information is common in cases that involve design/redesign of complex products and/or the construction of product configurators. Besides a knowledge representation technique, the paper suggests two different solutions for software support, of which the application of one of these in an actual project is described. [ABSTRACT FROM AUTHOR]

In this paper, we consider two single-machine scheduling problems with the effect of deterioration and learning. In this model, the processing times of jobs are defined as functions of their starting times and positions in a sequence. For the following two objective functions, the weighted sum of completion times and the maximum lateness, this paper gives two heuristics according to the corresponding problems without learning effect. This paper also gives the worst-case error bound for the heuristics and provides computational results to evaluate the performance of the heuristics. [ABSTRACT FROM AUTHOR]

This paper is the first of two which present a methodology for determining the dimensional specifications of all the component parts and sub-assemblies of a product according to their dimensional requirements. To achieve this goal, two major steps are followed, each of which is described in a paper. In the first paper, all relationships necessary for finding the values of dimensions and tolerances are represented in a matrix form, known as a Dimensional Requirements/Dimensions (DR/D) matrix. In the second paper, the values of individual dimensions and tolerances are determined by applying a comprehensive solution strategy to satisfy all the relationships represented in the DR/D matrix. The methodology is interactive and suitable for use in a concurrent engineering (CE) environment. The graphical tool presented in this paper will assist a CE team in visualizing the overall D&T problem and foreseeing the ramifications of decisions regarding the selection of dimensions and tolerances. This will assist the CE team to systematically determine all the controllable variables, such as dimensions, tolerances, and manufacturing processes. [ABSTRACT FROM AUTHOR]