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Source errors in a workpiece fixture system include the compliance of the workpiece fixture system and workpiece dynamics. The purpose of this paper is to study the relative significance of these two. The findings would help to achieve computational economy in optimization of fixture layout and/or clamping forces.

Different layouts are generated with the help of a reconfigurable fixture set up and a slot is end milled on the workpiece. Using these data and the finite element software ANSYS, the machining error due to system compliance is computed. The machining error due to workpiece dynamics is obtained using a data acquisition system with the LabView software. These steps are repeated for different clamping forces and the relative contribution of these two sources to the overall machining error is studied.

Results show that the system compliance is much smaller in magnitude compared to workpiece dynamics and hence does not contribute appreciably to the overall machining error. This leads to the conclusion that, for bulky and stiff parts, evaluation of the machining error due to compliance can be done away with.

The paper's originality lies in comparing the two sources of machining error using experimental work and finite element models. To the author's knowledge such a comparison has not been reported in the literature.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

Sustainable construction is widely considered to be the best practice in construction, helping to create a healthy built environment. Social media is identified as a valuable data source for research on sustainable construction, and Twitter is a popular social media platform in relation to the construction. Green Building construction is identified as one of the methods that promotes sustainable construction. The purpose of this study is to characterise “Green Building” as a topic in Twitter.

Social network analysis methods were applied to a large set of Twitter data related to “green building”. Time sequence analysis and network visualisation were used to characterise Twitter activity and to identify influential users. Text analytics and visualisation methods were applied to the same data set to visualise the text content of Twitter posts relating to green building.

Peaks in Twitter activity were associated with physical “green building” events. The network visualisation of the Twitter data revealed a complex structure and a range of types of interactions. The most “influential” users depended on the ranking method used; however, a number of users had high influence in all measures used. The tweet text visualisation showed evidence of a global and interactive audience on Twitter engaged in conversations about green building. Also, it was found that external links, emoji and popular terms related to a particular topic can be used to increase the engagement of Twitter users on that topic.

Certain Green Building events were observed to be associated with high levels of Twitter activity. The virtual was found to be closely linked to the physical, and for the promotion of green building construction, their respective impact is potentially the most powerful when used in conjunction. The most influential Twitter accounts did not belong to one class of user, including both individuals and organisations. Twitter offers a platform for a range of stakeholders in the area of green building construction to reach a substantial audience and to be influential in the public sphere. The findings of this research provide a valuable reference for industry practitioners and researchers to deepen their understanding of the application of Twitter to green building construction, and the methods of using Twitter to promote important information related to sustainable construction.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.

This study aims to examine how embeddedness influences consultants' information seeking when making decisions within a social network of relationships, and how these social networks evolve throughout the project delivery stages. The study is grounded in social network theory and examines embeddedness from three perspectives: structural (network cohesion), relational (tie strength in terms of friendship and knowledge awareness) and actor prominence.

A social network analysis (SNA) questionnaire was administered to a team of consultants working on a management consultancy project in Shanghai, China. The SNA measures of density, degree centrality and betweenness centrality were used to analyse relationship patterns among project team members, permitting comparison between the networks. Networks were also compared across the three project delivery stages of collect, consider and create.

Structural embeddedness was observed in the active information seeking behaviour among consultancy team members. The moderate network density of the self-organising information seeking networks across the project delivery stages ensures that the team remains connected but avoids information redundancy and overload. Relational embeddedness was evident through the multiplexity of ties among team members with overlapping friendship and information seeking relationships. The knowledge awareness network's sparseness indicates a team of autonomous knowledge workers with distributed expertise. Project managers were the most prominent actors across the three project delivery stages, underlining these actors' relational leadership role.

The study provides a deeper understanding of collaborative decision-making behaviours in dynamic-project environments. Limited attempts have been made to visualise and analyse the relationships involved in small consulting teams. The novelty of the network approach adopted stems from its ability to offer a structural view of the relationship among consultants, thus offering a distinctive and arguably more complete picture of consultancy team dynamics.

The study validates the social network theory of embeddedness in a real-world collaborative decision-making setting and provides a deeper understanding of information seeking behaviours for decision-making in dynamic-project environments. From a project management process viewpoint, the evolving nature of the information seeking network as it changes across the project stages with associated actors' roles was also visualised graphically, offering a distinctive and arguably more complete picture of consultancy team dynamics.

In this chapter we develop a new approach, based on the identification of strategy classes, to study how firms face multiple demands. The procedure that we propose (called Relational Class Analysis) stems from an analysis of the similarity of associative patterns across multiple observable outcomes, which reflect the underlying set of choices firms make to similarly address demands. Empirically, the study of 18 financial and extra-financial performance outcomes for 3,655 firms shows the existence of three main strategic classes. Drawing on our analysis, we redefine strategy as the set of committed decisions undertaken to resolve trade-offs between multiple concurrent objectives and discuss the implications of our approach for eight core questions for strategy and organizational theory.

Body forces are always applied to structures in the form of the weight of materials. In some cases, they can be neglected in comparison with other applied forces. Nevertheless, there is a wide range of structures in civil and mechanical engineering in which weight or other types of body forces are the main portions of the applied loads. The optimal topology of these structures is investigated in this study.

Topology optimization plays an increasingly important role in structural design. In this study, the topological derivative under body forces is used in a level-set-based topology optimization method. Instability during the optimization process is addressed, and a heuristic solution is proposed to overcome the challenge. Moreover, body forces in combination with thermal loading are investigated in this study.

Body forces are design-dependent loads that usually add complexity to the optimization process. Some problems have already been addressed in density-based topology optimization methods. In the present study, the body forces in a topological level-set approach are investigated. This paper finds that the used topological derivative is a flat field that causes some instabilities in the optimization process. The main novelty of this study is a technique used to overcome this challenge by using a weighted combination.

There is a lack of studies on level-set approaches that account for design-dependent body forces and the proposed method helps to understand the challenges posed in such methods. A powerful level-set-based approach is used for this purpose. Several examples are provided to illustrate the efficiency of this method. Moreover, the results show the effect of body forces and thermal loading on the optimal layout of the structures.

This study aims to determine the minimum force required to pull out a surface mount component in printed circuit boards (PCBs) during the wave soldering process through both experimental and numerical procedures.

An efficient experimental technique was proposed to determine the minimum force required to pull out a surface mount component in PCBs during the wave soldering process.

The results showed that the pullout force is approximately 0.4 N. Comparing this value with the simulated force exerted by the solder wave on the component ( ≅ 0.001158 N), it can be concluded that the solder wave does not exert sufficient force to remove a component.

This study provides a deep understanding of the wave soldering process regarding the component pullout, a critical issue that usually occurs in the microelectronics industry during this soldering process. By applying both accurate experimental and numerical approaches, this study showed that more tests are needed to evaluate the main cause of this problem, as well as new insights were provided into the depositing process of glue dots on PCBs.

A conveyor device is studied with the aim to reduce the friction between the inner surface of the beam and the chain. The lower is the friction between the chain and the beam, the lower is the surface wear. The magnetic repulsion force among permanent magnets (PMs) placed on the beam and on the chain is utilized to reduce friction. The paper aims to discuss these issues.

The considered magnetic suspension is realized with PMs in repulsive configuration; it is designed by solving a constrained optimization problem, with reference to the geometry of the 90° horizontal bend FlexLink WL322 conveyor. Flux density field and its gradient are evaluated using volume integral equation method, allowing to calculate the forces acting on the chain and the stiffness of the magnetic suspension.

The magnetic suspension prototype was manufactured and tested. The experimental and calculated values of the forces acting on the chain compares well. A stable horizontal equilibrium of the chain was obtained during both static and dynamical tests.

The quasi-static model used neglects the dynamical interactions among the elements of the chain, the PMs and loads weight during motions and the eddy current losses in the aluminium beam. However the dynamical tests on the prototype show that the chain motion is regular up to the nominal velocity all along the conveyor with the exception of the trailing edge of the 90° curve.

The tests on the prototype show the possibility of a removal or at least a reduction of the friction force between the chain and the inner side of the beam by means of a passive magnetic suspension. As a consequence a reduction of noise and vibrations and an increase of the mean-time-to-failure is expected.

Prototype testing shows that the unavoidable vertical instability of the magnetic forces has no practical consequence since, reducing the allowed vertical gap, the chain is stabilized by the gravitational force.

The purpose of this paper is to propose a general model for locating and clamping workpieces of complex geometry with two skewed holes under multiple constraints.

Numerous constraints related to application of the proposed model are discussed as prerequisite to design of fixture solution. Based on theoretical model, a fixture was designed and successfully tested in experimental investigation. Experimental results were also verified using FEM simulations.

This study showed that, opposed to conventional approach, novel solution results in significantly smaller fixture dimensions, while providing greater stability. Insertion of mandrels and supports element sub-assemblies into the workpiece holes significantly increases workpiece stiffness through an increased moment of inertia, while the internal support elements largely diminish the problem of thin wall deformation in the workpiece.

The fixture designed in this case was actually used in industrial application to accommodate a thin-walled casting of gearbox housing, where it proved to be a very stable framework. It can be used in industry without any major readjustments.

According to available literature, this work is the first successful implementation of a fixture solution in which the problem of multiple constraints is solved by attaching centering elements, support sub-assemblies, and other fixture elements to the internal workpiece walls, and then locating them in the second part of the fixture.