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This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy.

Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects.

The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB.

The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

Powder bed fusion-laser beam (PBF-LB) is a rapidly growing manufacturing technology for producing Al-Si alloys. This technology can be used to produce high-pressure die-casting (HPDC) prototypes. The purpose of this paper is to understand the similarities and differences in the microstructures and properties of PBF-LB and HPDC alloys.

PBF-LB AlSi10Mg and HPDC AlSi10Mn plates with different thicknesses were manufactured. Iso-thermal heat treatment was conducted on PBF-LB bending plates. A detailed meso-micro-nanostructure analysis was performed. Tensile, bending and microhardness tests were conducted on both alloys.

The PBF-LB skin was highly textured and softer than its core, opposite to what is observed in the HPDC alloy. Increasing sample thickness increased the bulk strength for the PBF-LB alloy, contrasting with the decrease for the HPDC alloy. In addition, the tolerance to fracture initiation during bending deformation is greater for the HPDC material, probably due to its stronger skin region.

This knowledge is crucial to understand how geometry of parts may affect the properties of PBF-LB components. In particular, understanding the role of geometry is important when using PBF-LB as a HPDC prototype.

This is the first comprehensive meso-micro-nanostructure comparison of both PBF-LB and HPDC alloys from the millimetre to nanometre scale reported to date that also considers variations in the skin versus core microstructure and mechanical properties.

Driven by the need to deepen understanding of the mechanisms driving teacher collaboration for enhancing teacher learning and practices, this study aims to investigate the influence of collaborative organizational conditions, network intentionality and efficacy for leading curriculum learning on teachers’ professional growth within the context of New Zealand’s Communities of Learning-Kahui Ako (CoL) policy.

The study employs survey design collecting perceptual data from teachers within two CoLs comprising 12 schools in New Zealand. Structural equation modeling is used to analyze the relationships between collaborative organizational conditions, teachers’ network intentionality, leadership efficacy and teacher professional growth.

The findings reveal that collaborative organizational conditions significantly impact teachers’ professional growth such as their new learning and enhanced practices. Furthermore, teachers’ network intentionality and efficacy for leading curriculum learning serve as mediators, amplifying the effects of collaborative organizational conditions on teacher professional growth. Specifically, teachers who are more confident in their leadership abilities and intentionally build professional relationships are better at using collaborative opportunities to address teaching challenges and bring innovation to their schools.

This study contributes to the existing literature by examining the interplay between organizational conditions, internal motivational drive for collaboration, and teacher professional growth within the context of CoL policy in New Zealand. It sheds light on the mechanisms driving teacher professional growth and offers insights for enhancing teacher collaboration and professional learning experiences within CoL networks.

This study addresses a gap in the knowledge on how longitudinal engagement in a school improvement initiative influences change in middle leaders’ (MLs') interactions and assesses how school–university partnerships around school improvement can support teachers with formal leadership roles (i.e. MLs’) leadership development.

Using a two-year longitudinal research design, university staff facilitated middle leadership training in a school-defined improvement initiative on lesson study. Results from a pre-test followed by two post-tests administered at one-year intervals were collected on social networks. Analyses examined changes in indegree and brokerage patterns among groupings of senior leaders (SL), subject leaders, cross-school specialists and teachers.

Accounting for staffing changes, 27 of 67 staff members participated in each survey, yielding 1,623 distinct ties connecting school members. Over the first year, advice-seeking increased by 225%. SLs’ initial propensity to consult peers shifted towards MLs and teachers. Subject leaders advising other leaders and teachers increased tenfold. Teachers’ peer-to-peer consultation increased by 2,000%. Specialists with school-wide responsibilities became the dominant group for advising other leaders, such as SLs and subject leaders. These shifts were sustained over the second year.

The study demonstrates that engagement in the school–university partnership support and the corresponding structural changes stimulated robust cross-school dialogue among teachers and various leaders. Brokerage patterns indicated an enhanced role for MLs in driving the school-defined improvement initiative which corresponded to university-designed development activities.

This article aims to map the expectations of manufacturing companies towards suppliers in terms of implementing improvement activities. The article poses two research questions: RQ1: What kind of improvement of activities do the surveyed producers expect from their suppliers? RQ2: Do factors such as size, capital or implemented systems influence different assessments of the analyzed requirements toward suppliers?

The Computer Assisted Telephone Interview (CATI) technique was used to collect data. The sample consists of 150 producers (employing over 50 people) who were suppliers for enterprises from the automotive, electromechanical and chemical sectors operating in the Polish business-to-business (B2B) market. We analyzed 11 improvement activities, while their correlation structure was examined by exploratory factor analysis.

We have identified three latent factors – risk reduction, product innovation and increasing efficiency – which summarize the main expectations of manufacturing companies towards suppliers. Expectations for these factors are independent of the implemented management system, although the analysis showed higher expectations for product innovation in organizations with the implementation of Kaizen.

The article fills the research gap in the literature. The research results presented in the literature so far have focused on the expectations of enterprises towards suppliers in terms of meeting the criteria for their initial and periodic assessment. The research gap in the article is the result of empirical research presenting the expectations of manufacturers towards suppliers in terms of improving their processes. Based on the findings of the presented study, development trends and implications for managers responsible for purchasing processes and relationships with suppliers can be determined.

This study attempts to evaluate the effect of the corpus-based inductive teaching approach with multiple academic corpora (PICA, CAEC and Oxford Corpus of Academic English) and conventional deductive teaching approach (i.e., multiple-choice items, filling the gap, matching and underlining) on learning academic collocations by Iranian advanced EFL learners (students learning English as a foreign language).

This is a quasi-experimental, quantitative and qualitative study.

The result showed the experimental group outperformed significantly compared with the control group. The experimental group also shared their perception of the advantages and disadvantages of the corpus-assisted language teaching approach.

Despite growing progress in language pedagogy, methodologies and language curriculum design, there are still many teachers who experience poor performance in their students' vocabulary, whether in comprehension or production. In Iran, for example, even though mandatory English education begins at the age of 13, which is junior and senior high school, students still have serious problems in language production and comprehension when they reach university levels.

This paper aims to examine the interaction between gatekeeping and trust in a public sector organization, where employees at lower hierarchical levels are expected to autonomously translate and transform directives into public services. This requires them to have access to operational steering information, i.e. information about directives and how to interpret and apply them. This study focuses on how gatekeeping structures regulate flows of operational steering information and how the gatekeeping structures affect the development of trust.

The research design is qualitative. The data material consisted of semi-structured interviews with 26 employees in home care and schools and of eight complementary nonparticipant observations. Thematic analysis revealed the presence of static and dynamic gatekeeping structures, which are characterized by fixed and variable arrangements of information sources and channels, respectively.

In static gatekeeping structures, managers or domain experts typically act as gatekeepers, and employees also perform gatekeeping activities collectively. Gatekeeping structures allow employees to switch between acting as gatekeepers and being gated, depending on the situation. The results show that gatekeeping structures for intermediation of operational steering information may support or impede employees' work, thus affecting their trust in their peers and their work organization.

Although the present study included both interviews and observations, these primarily occurred within scheduled and prearranged activities rather than capturing the nuances of the typical daily work of teachers and home care employees. As a result, certain perspectives may have been unintentionally omitted.

The participants were recruited through the City of Oslo contact people, which may have impacted their status or perception in some way. Moreover, the study was conducted in the City of Oslo, a specific organization with its own unique set of values, norms and processes. The trust-based management in the City of Oslo is likely not representative of all public sector organizations.

This study contributes conceptually by introducing gatekeeping structures and operational steering information and empirically by providing evidence of their relationship to trust development in public service delivery. Thus, it contributes to the research fields of information management and public administration.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.