Search results

This paper is supposed to provide a critical review of current research progress on thermal management in grinding of superalloys, and future directions and challenges. By understanding the current progress and identifying the developing directions, thermal management can be achieved in the grinding of superalloys to significantly improve the grinding quality and efficiency.

The relevant literature is collected from Web of Science, Scopus, CNKI, Google scholar, etc. A total of 185 literature is analyzed, and the findings in the literature are systematically summarized. In this case, the current development and future trends of thermal management in grinding of superalloys can be concluded.

The recent developments in grinding superalloys, demands, challenges and solutions are analyzed. The theoretical basis of thermal management in grinding, the grinding heat partition analysis, is also summarized. The novel methods and technologies for thermal management are developed and reviewed, i.e. new grinding technologies and parameter optimization, super abrasive grinding wheel technologies, improved lubrication, highly efficient coolant delivery and enhanced heat transfer by passive thermal devices. Finally, the future trends and challenges are identified.

Superalloys have excellent physical and mechanical properties, e.g. high thermal stability, and good high-temperature strength. The superalloys have been broadly applied in the aerospace, energy and automobile industries. Grinding is one of the most important precision machining technologies for superalloy parts. Owing to the mechanical and physical properties of superalloys, during grinding processes, forces are large and a massive heat is generated. Consequently, the improvement of grinding quality and efficiency is limited. It is important to conduct thermal management in the grinding of superalloys to decrease grinding forces and heat generation. The grinding heat is also dissipated in time by enhanced heat transfer methods. Therefore, it is necessary and valuable to holistically review the current situation of thermal management in grinding of superalloys and also provide the development trends and challenges.

The purpose of this study is analysis of deformation and vibrations of turbine blades produced by high electrolyte pressure during electrochemical machining.

An experimental setup was designed, experiments were conducted and the obtained results were compared with the finite element results. The deformations were measured according to various flow rates of electrolyte. In finite element calculations, the pressure distribution created by the electrolyte on the blade surface was obtained in the ANSYS® (A finite element analysis software) Fluent software and transferred to the static structural where the deformation analysis was carried out. Three different parameters were examined, namely blade thickness, blade material and electrolyte pressure on blade disk caused by mass flow rate. The deformation results were compared with the gap distances between cathode and anode.

Large deformations were obtained at the free end of the blade and the most curved part of it. The appropriate pressure values for the electrolyte to be used in the production of blisk blades were proposed numerically. It has been determined that high pressure applications are not suitable for gap distance lower than 0.5 mm.

When the literature is examined, it is required that the high speed flow of the electrolyte is desired in order to remove the parts that are separated from the anode from the machining area during electrochemical machining. However, the electrolyte flowing at high speeds causes high pressure in the blisk blades, excessive deformation and vibration of the machined part, and as a result, contact of the anode with the cathode. This study provides important findings for smooth electro chemical machining at high electrolyte flows.

The aviation industry has seen consistent growth over the past few decades. To maintain its sustainability and competitiveness, it is important to have a comprehensive understanding of the environmental impacts across the entire life cycle of the industry, including materials, processes and resources; manufacturing and production; lifetime services; reuse; end-of-life; and recycling. One important component of aircraft engines, integral rotors known as Blisks, are made of high-value metallic alloys that require complex and resource-intensive manufacturing processes. The purpose of this paper is to assess the ecological and economical impacts generated through Blisk production and thereby identify significant ‘hot-spots’.

This paper focuses on the methodology and approach for conducting a full-scale Blisk life cycle assessment (LCA) based on ISO 14040/44. Unlike previous papers in the European Aerospace Science Network series, which focused on the first two stages of LCA, this publication delves into the “life cycle impact assessment” and “interpretation” stages, providing an overview of the life cycle inventory modeling, impact category selection and presenting preliminary LCA results for the Blisk manufacturing process chain.

The result shows that the milled titanium Blisk has a lower CO₂ footprint than the milled nickel Blisk, which is less than half of the global warming potential (GWP) of the milled nickel Blisk. A main contributor to GWP arises from raw material production. However, no recycling scenarios were included in the analysis, which will be the topic of further investigations.

The originality of this work lies in the detailed ecological assessment of the manufacturing for complex engine components and the derivation of hot spots as well as potential improvements in terms of eco-footprint reduction throughout the products cradle-to-gate cycle. The LCA results serve as a basis for future approaches of process chain optimisation, use of “greener” materials and individual process improvements.

Because many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.

The methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.

From the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.

Machining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.

This paper describes the suitability of MQL for different machining operations using M-NF and H-NF.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/

This study aims to investigate a systematic approach to the production and use of additively manufactured injection mould inserts in product development (PD) processes. For this purpose, an evaluation of the additive tooling design method (ATDM) is performed.

The evaluation of the ATDM is conducted within student workshops, where students develop products and validate them using AT-prototypes. The evaluation process includes the analysis of work results as well as the use of questionnaires and participant observation.

This study shows that the ATDM can be successfully used to assist in producing and using AT mould inserts to produce valid AT prototypes. As a reference for the implementation of AT in industrial PD, extracts from the work of the student project groups and suitable process parameters for prototype production are presented.

This paper presents the application and evaluation of a method to support AT in PD that has not yet been scientifically evaluated.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

The authors investigate the relationship between instant messenger (IM) use and work performance, mediated by interruptions and two key indicators of the stress associated with technology use: overload and complexity.

The authors validate this research model using partial least squares structural equation modelling (PLS-SEM) with data collected through a survey of 416 working professionals.

The data reveal that while IM use contributes minimally to work interruptions and to a greater extent to technological complexity, these two constructs fully mediate the direct influence of IM use at work on technology overload, and meanwhile significantly and directly contribute to work performance.

This research provides theoretical insights into the deployment of IM and its actual impacts in the workplace. To improve the generalisation of the findings, the authors call for more IM-related research in other countries, with more native theories and various methodologies in this domain.

The level of stress generated through IM use is moderate, considering IM is not a significant contributor to work interruptions. Thus, despite the potential negative effects of IM communication, the positive effects of using IM at work prevail. As a result, the technology can be promoted as long as employees, their managers and the organisation as a whole are well prepared. Employees can transfer skills and behaviour from the personal setting to their work environment and thus may find an intrinsic motivation to make better use of the IM technology at work.

The authors argue that this research model is novel for its perspective on evaluating the actual impacts of IM use at work instead of the reasons of using it. The authors conceptualise the process to explain how IM contributes to interruptions and other technostress indicators in the working context, and the impact on performance. Contrary to some prior research, the authors find that overall IM applications do not have a negative impact on work performance, and instead may enhance it.

During everyday work, individuals often engage in unplanned conversations that help them develop relationships, share information and coordinate tasks. Unfortunately, the work-from-home mandates issued due to the pandemic have reduced the frequency of unplanned conversations among employees. So, as businesses reopen, organizations are considering post-pandemic workplace solutions that can facilitate unplanned conversations. To aid these efforts and move research and theory on unplanned conversations forward, this study proposes and tests a multi-factor model of the antecedents of unplanned conversations.

This study adopted a multi-methodological approach and collected data using automated sensing technology, structured observations and cross-sectional survey methods. A total of 5,297 unplanned conversations among 61 employees were recorded using the custom mobile application and structured observations. Cross-sectional survey data about these employees' work contexts were also collected.

The study results showed that the model significantly predicted the frequency of unplanned conversations. Notably, technical expertise, perceived time pressure and team psychological safety significantly predicted the frequency of unplanned conversations. The study findings have both theoretical and practical significance.

Previous research studies have primarily focused on the influence of workplace designs on unplanned conversations. However, this study demonstrates that several other factors facilitate unplanned conversations. This research theorizes and empirically tests the relationship between unplanned conversations and several individual, team and organizational factors.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.