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The main aim of this study is to generate curved-form cut on the edge of an adaptive layer. The resulting surface would have much less geometry deviation error and closely fit its computer aided design (CAD) model boundary.

This method is inspired by the manual peeling of an apple in which a knife's orientation and movement are continuously changed and adjusted to cut each slice with minimum waste. In this method, topology and geometry information are extracted from the previously generated adaptive layers. Then, the thickness of an adaptive layer and the bottom and top contours of the adjacent layers are fed into the proposed algorithm in the form of the contour and normal vector to create curved-form sloping surfaces. Following curved-form adaptive slicing, a customized machine path compatible with a five-axis abrasive waterjet (AWJ) machine will be generated for any user-defined sheet thicknesses.

The implemented system yields curved-form adaptive slices for a variety of models with diverse types of surfaces (e.g. flat, convex, and concave), different slicing direction, and different number of sheets with different thicknesses. The decrease in layer thickness and increase of the number of the sloped cuts can make the prototype as close as needed to the CAD model.

The algorithm is designed for use with five-axis AWJ cutting of any kind of geometrical complex surfaces. Future research would deal with the nesting problem of the layers being spread on the predefined sheet as the input to the five-axis AWJ cutter machine to minimize the cutting waste.

The algorithm generates adaptive layers with concave or convex curved-form surfaces that conform closely to the surface of original CAD model. This will pave the way for the accurate fabrication of metallic functional parts and tooling that are made by the attachment of one layer to another. Validation of the output has been tested only as the simulation model. The next step is the customization of the output for the physical tests on a variety of five-axis machines.

This paper proposes a new close to CAD design sloped-edge adaptive slicing algorithm applicable to a variety of five-axis processes that allow variable thickness layering and slicing in different orientations (e.g. AWJ, laser, or plasma cutting). Slices can later be bonded to build fully solid prototypes.

Near-net-shaped processes of jet engine blade have better performance in both reducing the material waste during production and improving work reliability in service, while the geometric features of blade, both sculptured surface and thin-walled shape, make the precise machining of blade challenging and difficult owing to its dynamics behaviors under complex clamping and machining loads. This paper aims to present a fundamental approach on modeling and performance analysis of the blade–fixture system.

A computerized framework on the complex blade–fixture dynamic behavior has been developed. Theoretical mechanic analysis on blade fixturing and machining is proposed with an especial emphasis on the boundary conditions of the blade–fixture system. Then the finite element analysis (FEA) method is used to simulate the variation trend of preloads, stiffness and blade distortion. The strong influence of parameters of workpiece–fixture configuration on blade distortion and machining error is investigated.

With a case of real jet engine blade machining, the experimental results and theoretical predictions suggest good agreement on their variation tendency. The loaded pressure of clamps has a critical influence on the total stiff performance of the blade–fixture system, and the profile error of the blade contributes much to the inconsistency in geometric dimension and surface integrity of blades’ machining. In the end, the results also validate the effectiveness of this methodology to predict and improve the performance of the blade–fixture configuration design.

The adaptive machining of near-net-shaped jet engine blade is a new high-performance manufacturing technology in aerospace production. This study provides a fundamental methodology for the performance analysis of blade-fixture system, to clear the variation law of blade distortion during preloading and machining, which will contribute to minimize the machining error and improve productivity.

The continuous development of information technology leads to intelligent education research. In the context of internationalisation, the study aims to understand the relevant research status worldwide, research similarities and differences that need to be discovered, and research frontiers that need to be explored.

Web of Science (WoS) core collection was used as the data source, descriptive statistical analysis, geographic data visualisation and coupling analysis are used to reveal coupling relationships, present a cooperative situation and discover research frontiers.

Intelligent education research has been widely carried out in countries around the world, and there is extensive scientific research cooperation. According to coupling analysis results, the coupling strength of bibliographic between countries has been continuously improved, while the coupling strength of keywords has remained balanced, and there is standardisation and diversity of research.

The weakness of the research lies in the limitations of the data sources. Important research achievements on a certain topic in many non-English speaking countries are usually published in native journals. In the future research direction, more coupling analysis objects can be carried out, such as focussing on authors and institutions.

Through the coupling analysis of country bibliographic and keywords, it reveals the consistency and divergence of intelligent education research between different countries at different time spans.

Design and implement country bibliographic coupling (CBC) and country keyword coupling (CKC) strength indicators to calculate the strength of coupling between countries.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy.

The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE).

The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence.

The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data.

This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

The purpose of this paper is to examine the state of knowledge management (KM) in the energy sector and more broadly, and consider future directions for research and practice.

The paper reviews the literature on KM and the practice of KM as relevant to the energy sector.

There are many examples of good practice in KM in the sector, and some organisations, especially in the oil industry, are seen as leaders in KM practice. However, other organisations have yet to embark on explicit KM initiatives or projects at all. In addition, some parts of the energy sector discuss KM without any reference to the more general KM literature.

Although some parts of the energy sector have justifiably earned a good reputation for KM, other parts are completely unaware of the field, as is apparent from the literature. This review helps to raise awareness and guide future work.

In a small‐lot manufacturing facility, process planning is the task of specifying a machine series that will produce a certain part from a given raw material. Traditionally, the same machine path or routing is followed each time the part is released for manufacture. A prototype system is developed which adapts routings according to job specification (process quantity and due date), as well as shop conditions (the relative cost and availability of alternative resources). The performance of this system is then compared against the traditional fixed method, as well as two single‐focus, adaptive strategies (least‐cost and least‐load), drawn from research.

The aim of the paper is to provide an economically viable solution for the blade repair process. There is a continual increase in the repair market, which requires an increased level of specialised technology to reduce the repair cost and to increase productivity of the process.Design/methodology/approach – This paper introduces the aerospace component defects to be repaired. Current repair technologies including building‐up and machining technology are reviewed. Through the analysis of these available technologies, this paper proposes an integrated repair strategy through information integration and processes concentration.Findings – Provides detailed description and discussion for the repair system, including 3D digitising system, repair inspection, reverse engineering‐based polygonal modelling, and adaptive laser cladding and adaptive machining process.Originality/value – This paper describes a 3D non‐contact measurement‐based repair integration system, and provides a solution to create an individual blade‐oriented nominal model to achieve adaptive repair process (laser cladding/machining) and automated inspection.