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This paper describes work undertaken to support an integrated approach to the autonomous maintenance management of machine tools and flexible manufacturing systems. The work described was initiated as part of a collaborative research project, funded under the European Community Eureka Maine project initiative as EU744: Integrated Condition and Machining Process Monitoring System for Flexible Manufacturing Systems and for Stand‐alone CNC Machine Tools. The paper considers the steps needed to develop and implement truly autonomous machine tool maintenance management systems, and outlines the benefits which can be obtained from such approaches. The systems described have been deployed within collaborating industrial partners, each of whom is an existing user of advanced manufacturing technology. Their impact on these manufacturing systems is considered, with reference to the improvements in overall equipment effectiveness (OEE). The paper cites the improvements made in relation to specific flexible manufacturing cells as justification for the outlined system improvements.

Autonomous maintenance (AM), one of the pillars of total productive maintenance (TPM), aims to achieve performance toward zero defects and zero breakdowns. AM system equipped with comprehensive lean tools provides continuous improvement during the AM activities. Despite its long duration, establishing a lean AM system with a robust guideline would provide significant benefits such as high quality and short lead time. Therefore, AM design approach should be provided in a holistic and detailed manner. This study aims to develop a framework for AM design, including preliminary, reactive, preventive and proactive steps using the axiomatic design (AD).

Requirements and technical parameters of the AM system are explored with AD. An extensive literature review and a real-life application are presented.

The proposed design was validated by adapting the proposed roadmap to a textile manufacturing system in Turkey. The application results justify the established AM system design with an average downtime improvement of 69.2% and the average elapsed time between two failures improvement of 65.1% for apparel department.

This study has the novelty of establishing an overall AM system design with all of its stages stepwise. It presents a comprehensive guideline in terms of integration of lean philosophy into AM design by generating maintenance-related use cases for lean tools. The developed approach facilitates creating and analyzing complex systems to improve maintenance implementations while reducing nonvalue-added operations.

In present context of globalization, maintenance of production systems is very important. Many of the organizations are facing a lot of problems in maintenance management. Therefore the purpose of this paper is to identify the main barriers in maintenance management and to rank them for effective maintenance strategies.

To rank the main barriers in maintenance management, technique for order preference by similarity to ideal solution is used. For giving score to different factors a team of three experts was made. All experts were having more than ten years of experience in area of maintenance management.

Lack of top management support, lack of measurement of overall equipment effectiveness (OEE) and lack of strategic planning and implementation have emerged as top three barriers in implementation of maintenance systems in industries.

Findings imply that for successful maintenance, top management should be very supportive for taking different initiatives, training programmes, etc. Organizations should try to improve overall performance of machines known as OEE rather than only machines productivity.

These findings will be highly useful for professionals from manufacturing sector in implementing effective maintenance management system.

Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.

This paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.

The R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.

The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

In the present context of globalization, maintenance of production systems is very important. A lot of organizations are facing many problems in maintenance management. Therefore, the purpose of this paper is to identify the main problems in maintenance operations and compare these problems with those in manufacturing operations as found in the literature for effective maintenance.

To identify the main problems in maintenance operations and to compare them with those in manufacturing operations, a large amount of published literature was studied. The paper systematically categorizes the published literature and then analyzes and reviews it theoretically.

Lack of top management support, lack of measurement of overall equipment effectiveness (OEE), lack of strategic planning and implementation and many more problems are biggest problems in the maintenance operations as well as manufacturing operations. These have emerged as top problems in the implementation of effective maintenance strategies in industries.

From the findings, the authors can conclude that for good maintenance, top management is supposed to be supportive in taking different initiatives. Industrial organizations should focus on improving overall performance of machines identified as OEE rather than only productivity of machines. This paper will be extremely useful for the researchers, maintenance professionals and others concerned with maintenance to understand the significance of maintenance problems in industries.

These findings will be highly useful for professionals from the manufacturing sector in implementing effective maintenance strategy in the maintenance management system.

The purpose of this paper is to review the literature on Total Productive Maintenance (TPM) and to present an overview of TPM implementation practices adopted by the manufacturing organizations. It also seeks to highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementation.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper reveals the important issues in Total Productive Maintenance ranging from maintenance techniques, framework of TPM, overall equipment effectiveness (OEE), TPM implementation practices, barriers and success factors in TPM implementation, etc. The contributions of strategic TPM programmes towards improving manufacturing competencies of the organizations have also been highlighted here.

The literature on classification of Total Productive Maintenance has so far been very limited. The paper reviews a large number of papers in this field and presents the overview of various TPM implementation practices demonstrated by manufacturing organizations globally. It also highlights the approaches suggested by various researchers and practitioners and critically evaluates the reasons behind failure of TPM programmes in the organizations. Further, the enablers and success factors for TPM implementation have also been highlighted for ensuring smooth and effective TPM implementation in the organizations.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. It will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the significance of TPM.

The purpose of this paper is to examine the multidimensionality of total productive maintenance (TPM) and its relationship with manufacturing performance improvement in the Malaysian manufacturing sector. Specifically, this study evaluates the contribution of each TPM success factors in improving manufacturing performance.

Data from 89 employees who participated in the survey were used to test the proposed research framework. A structured questionnaire adopted from Ahuja and Khamba (2006) was used to assess the Malaysian context.

The analytical results reveal that traditional maintenance initiatives and TPM implementation initiatives significantly affect manufacturing performance, but not top management leadership and maintenance organisation. Top management roles and commitment are critical in the early stage to determine the master plan and initiate the implementation of the whole programme. However, traditional maintenance and TPM implementation initiatives gradually enable engagement, proper planning, right execution and continuous improvement, ultimately improving the manufacturing performance indicators significantly. The findings further unveil that TPM is not sustainable in Malaysia’s manufacturing organisations in the long run.

This analysis is vital for senior managers of manufacturing organisations that have implemented TPM or are considering introducing TPM in their organisations.

This study contributes to the literature by examining beyond the introduction and stabilisation phase of TPM to provide an insight of whether TPM is sustainable in the long run.

Seeks to present a methodology for working with bottle‐neck reduction by using a combination of automatic data collection and discrete‐event simulation (DES) for a manufacturing system.

In the DES model, the bottle‐neck was identified by studying the simulation runs based on the collected automatic data from the different machines in the manufacturing system.

A case study showed an improvement of the availability in one machine from 58.5 to 60.2 percent. This single alteration with a minimum of investment resulted in a 3 percent increase of the overall output in the manufacturing system consisting of 11 numerically controlled machines and six other stations. A new simulation run was performed one year after the first study in order to see how the improvement work has progressed with the suggested method. The method resulted in an increase of 6 percent in overall output.

It could be assumed that machines in future manufacturing systems will provide automatic data. The data can then be used for DES models when identifying bottle‐necks in a manufacturing system.

The purpose of this paper is to design, investigate and optimize a horizontal axis tidal turbine (HATT) using computer-aided numerical simulation and computational fluid dynamics (CFD). This is the first step of research and development (R&D) for implementation in the Persian Gulf condition. To do so, suitable locations are reviewed. Then, the optimization is focused on determining the optimum fixed pitch angle (β) of a three-bladed HATT based on the widespread multiple reference frame (MRF) technique to calculate power and thrust coefficients at different operational rotating speeds.

To simplify the problem and reducing the computational costs due to cyclic symmetry only one blade, accordingly one-third of the whole computational domain is considered in the modeling. Due to flow’s nature involving rotating, separation and recirculation, a realizable κ-ε turbulence model with standard wall function is selected to capture flow characteristics influenced by the rotor and near the wall region. Simulations are conducted for two free-stream velocities, then compared with their dependencies through the dimensionless tip speed ratio (TSR) parameter.

The validation process of the simulations is carried out by the use of AeroDyn BEM code, which has been evaluated by comparing with two experimental data. As results, the highest coefficient of power is achieved at ß = 19.3° at TSR = 4 with the value around 0.41 and 0.816 for thrust coefficient. Furthermore, to comprehend the rotor’s performance and simulation method, flow characteristics due to the rise in angular velocity is discussed in detail. Moreover, the major phenomenon, cavitation occurrence, is also checked at the critical situation where it is found to be safe.

By comparing and evaluating the results to other HATTs, it implies that the proposed rotor of this study is feasible and proved by CFD evaluation at this step. However, the current rotor is awaiting a justification through experimental assessment.

Recent cybersecurity education literature has focused on developments in cybersecurity curricula, qualifications and accreditation, pedagogy and practice to increase the number of cybersecurity professionals, in both the UK and internationally. There has been little research published to date on the online learning, teaching and assessment environment as a cyber target in its own right. This chapter appraised and discussed the dangers in, and emerging threats to, using online environments. It proposes a set of steps and mitigation measures that can be taken to make it more difficult for cybercriminals to attack educational institutions.