Hybrid intelligent model for software maintenance prediction

Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used for evolving the neural network topology until an optimal topology is reached. The model was applied on a popular open source program, namely, Android. The results are very promising, where the correlation between actual and predicted points reaches 0.9

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities

Advances in application of machine learning to life cycle assessment: a literature review

Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm. &nbsp

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm. &nbsp

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Feasibility analysis of using special purpose machines for drilling-related operations

This work focuses on special purpose machine tools (SPMs), providing a modular platform for performing drilling-related operations. One of the main challenges in using SPMs is selecting the most appropriate machine tool among many alternatives. This thesis introduces a feasibility analysis procedure developed to support decision-making through the assessment of the strengths and limitations of SPMs. To achieve this, technical and economic feasibility analyses, a sensitivity analysis, and an optimisation model were developed and a case study was provided for each analysis. The results indicated that although technical feasibility analysis leads decision-makers to select a feasible machine tool, complementary analyses are required for making an informed decision and improving profitability. Accordingly, a mathematical cost model was developed to perform economic and sensitivity analyses and investigate the profitability of any selected SPM configuration. In addition, an optimisation procedure was applied to the cost model in order to investigate the effect of process parameters and the SPM configuration on the decision-making. Finally, the developed analyses were then integrated into a model in a proper sequence that can evaluate whether the SPM is appropriate for producing the given part and achieving higher productivity. To validate this integrated model three different case studies were presented and results were discussed. The results showed that the developed model is a very useful tool in assisting manufacturers to evaluate the performance of SPMs in comparison with other alternatives considered from different perspectives