An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem

This paper focuses on improvements to the Bees Algorithm (BA) with slope angle computation and Hill Climbing Algorithm (SACHCA) during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised to guide the algorithm to converge to the local optima. This enabled the global optimum of the given problem to be found faster and more precisely by focusing on finding the available local optima first before turning the attention on the global optimum. The proposed enhancements to the BA have been tested on continuous-type benchmark functions and compared with other optimisation techniques. The results show that the proposed algorithm performed better than other algorithms on most of the benchmark functions. The enhanced BA performs better than the basic BA, in particular on higher dimensional and complex optimisation problems. Finally, the proposed algorithm has been used to solve the single machine scheduling problem and the results show that the proposed SAC and HCA-BA outperformed the basic BA in almost all the considered instances, in particular when the complexity of the problem increases

Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

Nowadays, ensuring high quality can be considered the main strength for a company’s success. Especially, in a period of economic recession, quality control is crucial from the operational and strategic point of view. There are different quality control methods and it has been proven that on the whole companies using a continuous improvement approach, eliminating waste and maximizing productive flow, are more efficient and produce more with lower costs. This paper presents a method to optimize the quality control stage for a wood manufacturing firm. The method is based on the employment of the principal component analysis in order to reduce the number of critical variables to be given as input for an artificial neural network (ANN) to identify wood veneer defects. The proposed method allows the ANN classifier to identify defects in real time and increase the response speed during the quality control stage so that veneers with defects do not pass through the whole production cycle but are rejected at the beginning

Cost-effective solutions and tools for medical image processing and design of personalised cranioplasty implants

Cranial defects which are caused by bone tumors or traffic accidents are treated by cranioplasty techniques. Cranioplasty implants are required to protect the underlying brain, correct major aesthetic deformities, or both. With the rapid develop-ment of computer graphics, medical image processing (MIP) and manufacturing technologies in recent decades, nowadays, personalised cranioplasty implants can be designed and made to improve the quality of cranial defect treatments. However, software tools for MIP and 3D modelling of implants are ex-pensive; and they normally require high technical skills. Espe-cially, the process of design and development of personalised cranioplasty implants normally requires a multidisciplinary team, including experts in MIP, 3D design and modelling, and Biomedical Engineering; this leads to challenges and difficulties for technology transfers and implementations in hospitals. This research is aimed at developing, in particular, cost-effective solutions and tools for design and modeling of per-sonalised cranioplasty implants, and to simplify the design and modelling of implants, as well as to reduce the design and modeling time. In this way, surgeons and engineers can con-veniently and easily design personalised cranioplasty implants, without the need of using complex MIP and CAD tools; and as a result the cost of implants will be minimised

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

A core subfeld of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specifc. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifcally for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

The finite element analysis of melt flow behaviour in micro-injection moulding

Micro-injection moulding as a replication method is one of the key technologies for micromanufacture. The understanding of process constraints for a selected production route is essential both at the design stage and during mass production. In this research, an existing finite element analysis system is used to verify the effects of four process parameters: the melt and mould temperatures, injection speed, and part thickness. Special attention is paid to the melt flow sensitivity when filling microchannels, particularly the factors affecting the shear rate, pressure, and temperature. In particular, the simulation model was used to investigate the flow behaviour of two polymer materials, polypropylene and acrylonitrile butadiene styrene, by varying the process parameters. Then, the results of this investigation were compared with those reported in an experimental study. Conclusions are made about the accuracy and sensitivity of the proposed simulation model

Selection of Rapid Prototyping Techniques Using Reasoning and Ranking

Rapid prototyping (RP) is a method in which the part is created by a layer-additive process. Using specialised software, a 3-D CAD model is cut into very thin layers or cross-sections. Then, depending on the specific method used, the RP machine constructs the part layer by layer until a solid replica of the CAD model is generated. With respect to RP there is a need to select the most appropriate RP technique according to a specific application. This task is hard to achieve because of the number of available RP techniques and material/process combinations capable of creating prototypes directly from CAD models. This paper presents a method for choosing the most suitable RP technique for a given application. Here, using the proposed approach this task is simplified by dividing the selection problem into two related subproblems, which are ‘determining feasibility’ through ‘reasoning’ and ‘ordering the options’ through ‘ranking’ in order to choose the most appropriate RP technique for a given application in a quick and inexpensive way without compromising the end-user requirements