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A numerical method for shape optimisation in forging is presented. The goal of the optimisation is to eliminate work‐piece defects that may arise during the forging process. A two‐dimensional finite element code has been developed for the simulation of the mechanical process. The material is incompressible and it follows the Norton‐Hoff law. To deal with contact constraint the velocity projection algorithm is used. The optimisation process is conducted using a genetic algorithm supported by an elitist strategy. A new genetic operator called adaptive mutation has been developed to increase the efficiency of the search. The developed scheme is used to design optimal preform shapes for several axisymmetric examples. Continuous and discrete design variables are considered. The objective function of the optimisation problem is associated with the quality of the final product. Comparing the obtained optimal results with the literature validates the proposed optimisation method.

The purpose of this paper is to evaluate the capabilities of the generalized finite element method (GFEM) under the context of the geometrically nonlinear analysis. The effect of large displacements and deformations, typical of such analysis, induces a significant distortion of the element mesh, penalizing the quality of the standard finite element method approximation. The main concern here is to identify how the enrichment strategy from GFEM, that usually makes this method less susceptible to the mesh distortion, may be used under the total and updated Lagrangian formulations.

An existing computational environment that allows linear and nonlinear analysis, has been used to implement the analysis with geometric nonlinearity by GFEM, using different polynomial enrichments.

The geometrically nonlinear analysis using total and updated Lagrangian formulations are considered in GFEM. Classical problems are numerically simulated and the accuracy and robustness of the GFEM are highlighted.

This study shows a novel study about GFEM analysis using a complete polynomial space to enrich the approximation of the geometrically nonlinear analysis adopting the total and updated Lagrangian formulations. This strategy guarantees the good precision of the analysis for higher level of mesh distortion in the case of the total Lagrangian formulation. On the other hand, in the updated Lagrangian approach, the need of updating the degrees of freedom during the incremental and iterative solution are for the first time identified and discussed here.

The purpose of this paper is to explore the possibility of combining additive manufacturing (AM) with topology optimization to generate support structures for addressing the challenging overhang problem. The overhang problem is considered as a constraint, and a novel algorithm based on continuum topology optimization is proposed.

A mathematical model is formulated, and the overhang constraint is embedded implicitly through a Heaviside function projection. The algorithm is based on the Solid Isotropic Material Penalization (SIMP) method, and the optimization problem is solved through sensitivity analysis.

The overhang problem of the support structures is fixed. The optimal topology of the support structures is developed from a mechanical perspective and remains stable as the material volume of support structures changes, which allows engineers to adjust the material volume to save cost and printing time and meanwhile ensure sufficient stiffness of the support structures. Three types of load conditions for practical application are considered. By discussing the uniform distributive load condition, a compromise result is achieved. By discussing the point load condition, the removal work of support structures after printing is alleviated. By discussing the most unfavorable load condition, the worst collapse situation of the printing model during printing process is sufficiently considered. Numerical examples show feasibility and effectiveness of the algorithm.

The proposed algorithm involves time-consuming finite element analysis and iterative solution, which increase the computation burden. Only the overhang constraint and the minimum compliance problem are discussed, while other constraints and objective functions may be of interest.

Compared with most of the existing heuristic or geometry-based support-generating algorithms, the proposed algorithm develops support structures for AM from a mechanical perspective, which is necessary for support structures particularly used in AM for mega-scale construction such as architectures and sculptures to ensure printing success and accuracy of the printed model.

With the rapid development of AM, complicated structures result from topology optimization are available for fabrication. The present paper demonstrates a combination of AM and topology optimization, which is the trend of fabricating manner in the future.

This paper remarks the first of attempts to use continuum topology optimization method to generate support structures for AM. The methodology used in this work is theoretically meaningful and conclusions drawn in this paper can be of important instruction value and practical significance.

Pores and shrink holes are unavoidable defects in the die-casting mass production process which may significantly influence the strength, fatigue and fracture behaviour as well as the life span of structures, especially if they are subjected to high static and dynamic loads. Such defects should be considered during the design process or after production, where the defects could be detected with the help of computed tomography (CT) measurements. However, this is usually not done in today's mass production environments. This paper deals with the stress analysis of die-cast structural parts with pores found from CT measurements or that are artificially placed within a structure.

In this paper the authors illustrate two general methodologies to take into account the porosity of die-cast components in the stress analysis. The detailed geometry of a die-cast part including all discontinuities such as pores and shrink holes can be included via STL data provided by CT measurements. The first approach is a combination of the finite element method (FEM) and the finite cell method (FCM), which extends the FEM if the real geometry cuts finite elements. The FCM is only applied in regions with pores. This procedure has the advantage that all simulations with different pore distributions, real or artificial, can be calculated without changing the base finite element mesh. The second approach includes the pore information as STL data into the original CAD model and creates a new adapted finite element mesh for the simulation. Both methods are compared and evaluated for an industrial problem.

The STL data of defects which the authors received from CT measurements could not be directly applied without repairing them. Therefore, for FEM applications an appropriate repair procedure is proposed. The first approach, which combines the FEM with the FCM, the authors have realized within the commercial software tool Abaqus. This combination performs well, which is demonstrated for test examples, and is also applied for a complex industrial project. The developed in-house code still has some limitations which restrict broader application in industry. The second pure FEM-based approach works well without limitations but requires increasing computational effort if many different pore distributions are to be investigated.

A new simulation approach which combines the FEM with the FCM has been developed and implemented into the commercial Abaqus FEM software. This approach the authors have applied to simulate a real engineering die-cast structure with pores. This approach could become a preferred way to consider pores in practical applications, where the porosity can be derived either from CT measurements or are artificially adopted for design purposes. The authors have also shown how pores can be considered in the standard FEM analysis as well.

This is an attempt to better bridge the gap between the mathematical and the engineering/physical aspects of the topic. The authors trace the different sources of non-convexification in the context of topology optimization problems starting from domain discretization, passing through penalization for discreteness and effects of filtering methods, and end with a note on continuation methods.

Starting from the global optimum of the compliance minimization problem, the authors employ analytical tools to investigate how intermediate density penalization affects the convexity of the problem, the potential penalization-like effects of various filtering techniques, how continuation methods can be used to approach the global optimum and how the initial guess has some weight in determining the final optimum.

The non-convexification effects of the penalization of intermediate density elements simply overshadows any other type of non-convexification introduced into the problem, mainly due to its severity and locality. Continuation methods are strongly recommended to overcome the problem of local minima, albeit its step and convergence criteria are left to the user depending on the type of application.

In this article, the authors present a comprehensive treatment of the sources of non-convexity in density-based topology optimization problems, with a focus on linear elastic compliance minimization. The authors put special emphasis on the potential penalization-like effects of various filtering techniques through a detailed mathematical treatment.

Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively.

The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and selection for input variables simultaneously.

It is found that the proposed risk-adjusted methodology via maximum drawdown significantly outperforms the benchmark and improves the previous model in the performance of stock selection.

Future work considers an extensive study for the risk-adjusted model using other risk measures such as Value at Risk, Block Maxima, etc. The authors also intend to use financial data from other countries, if available, in order to assess if the method is generally applicable and robust across different environments.

The authors expect this risk-adjusted model to advance the CI research for financial engineering and provide an promising solutions to stock selection in practice.

The originality of this work is that maximum drawdown is being successfully incorporated into the CI-based stock selection model in which the model's effectiveness is validated with strong statistical evidence.

The purpose of this paper is to quantify, evaluate and compare the implementation performance of a supplier development (SD) program using graph theoretic approach (GTA).

Broadly 13 critical success factors (CSFs) were identified and they were classified into four significant categories (SCs). Featuring these SCs and CSFs under each SC, GTA was proposed for evaluating the implementation performance of SD programs. The analysis was further extended to evaluate the performance of a SD program along the timeline to capture the other influences (if any), eventually compare the results with different performance situations and accordingly set the future targets.

In order to demonstrate the utility of the proposed approach it was applied to an Indian manufacturing company. The results obtained shown that there has been a significant growth achieved in the implementation performance of a SD program over a period of three years (i.e. 12 quarters) along the chosen SCs and CSFs under each SC. It was also found that still there was a performance gap and scope for improvement in the SD program of the case company.

The proposed approach is aimed at providing a procedure for evaluating the implementation performance of a SD program. This study is an attempt to assist a manufacturer to assess its SD program and accordingly define its course of actions.

Although many issues related to SD have been widely recognized and studied, there are no specific studies available in the literature to assess the implementation performance of SD programs along the timeline. The proposed model has the ability to capture the performance and interdependencies of SCs, CSFs under each SC and also to quantify the implementation performance of a SD program along the timeline.

The first objective of this paper is to identify the critical success factors (CSFs) and their corresponding key performance indicators (KPIs) for supplier development (SD) in a manufacturing environment. The second objective is to develop a methodology to analyze and evaluate the performance for SD using CSFs and their KPIs over the time.

In all, 13 CSFs and their corresponding KPIs for SD are established through an extensive literature review, discussion held with managers/engineers in different Indian manufacturing companies and conducting brainstorming sessions. A methodology is proposed using analytic hierarchy process (AHP) and performance value analysis to assess and evaluate the performance of SD over the time.

From an extensive analysis of the results, under the given circumstances, the growth of SD performance is positive at different progressive points along the time horizon.

This study has not been statistically validated in manufacturing supply chain for complete acceptability.

This study provides insights into the performance evaluation of SD using CSFs and their corresponding KPIs. The managers should capture their manufacturing environment which can reflect their own priority considerations to establish the impact of each KPI on its corresponding CSF.

Although many issues related to SD have been widely researched individually, few studies have been reported with a model to quantify, monitor, analyze, and evaluate the growth of SD performance in manufacturing environment in Indian context. The proposed model has the ability to capture the performance along KPIs and CSFs to draw fruitful conclusions regarding the success of the SD programme.

Agriculture, since time immemorial, has been a major sector in reducing poverty, accelerating economic activities and source of sustainability of a nation. As most of the youth in the 21st century are targeting the youth's career in the non-agricultural sector, attracting and involving the youths in agribusinesses is the need of the hour. In this regard, the research tries to understand the awareness and involvement of youth farmers in agripreneurship in Western Nepal.

Using a structured questionnaire, data were collected from both primary and secondary sources. On the basis of descriptive and inferential analysis, the ordered logit model is employed in the study. The research is based on explanatory research design through identifying farmers' awareness of agriculture entrepreneurship from 324 farmers of Bedkot Municipality, Kanchanpur, Nepal.

Study findings have shown that agricultural knowledge with overall awareness is statistically significant. Ordered logistic regression indicated that sex has somehow influenced technical and entrepreneur skills while agriculture information highly influenced education and training. The result shows that agricultural training is still the requirement of one-fourth of the farmers, whereas another quarter of farmers need subsidies.

The study suggests that in order to enhance awareness, training, subsidy, market management and development program are widely needed. In the backdrop of declining agro-products and food security challenges, the study targets to offer an understanding to policymakers and stakeholders on addressing the dire need of agripreneurship among the youths around the world.

The study summarizes the state of knowledge of agripreneurship of Nepalese farmers, identifies emerging issues and policy implications and priorities for future applied research.

Heat exchangers (HEs) which provide heat transfer and transfer energy through direct or indirect contact between fluids have an essential role in many processes as a part of various industries from pharmaceutical production to electronic devices. Using nanofluid as working fluid and integrating different types of turbulators could be used to upgrade the thermal effectiveness of HEs. Recently, to obtain more increment in thermal effectiveness, hybrid nanofluids are used that are prepared by mixing two or more various nanoparticles. The purpose of this experimental and numerical study is investigating different scenarios for improving the effectiveness of a concentric U-tube type HE.

In the numerical section of this study, different turbulator modifications, including circular and quarter circular rings, were modeled to determine the effect of adding turbulator on thermal performance. In addition, Al₂O₃/water and SiO₂/water single and Al₂O₃–SiO₂/water hybrid nanofluids were experimentally tested in an unmodified concentric U-tube HE in two different modes, including counter flow and parallel flow. Al₂O₃–SiO₂/water hybrid nanofluid was prepared at 2% (wt./wt.) particle ratio and compared with Al₂O₃/water and SiO₂/water single type nanofluids at same particle ratios and with distilled water.

Numerical modeling findings exhibited that integrating turbulators to the concentric tube type HE caused to raise in the effectiveness by improving heat transfer area. Also, experimental results indicated that using both hybrid and single type nanofluids notably upgraded the thermal performance of the concentric U-tube HE. Integrating turbulators cannot be an effective alternative in a concentric U-tube type HE with lower diameter because of raise in pressure drop. Numerically achieved findings exhibited that using quarter circular turbulators decreased pressure drop in comparison with circular turbulators. According to the experimental outcomes, using hybrid Al₂O₃–SiO₂/water nanofluid leads to obtain more thermal performance in comparison with single type nanofluids. The highest increment in overall heat transfer coefficient of HE by using Al₂O₃–SiO₂/water nanofluid achieved as 58.97% experimentally.

The overall outcomes of the current research exhibited the positive impacts of using hybrid nanofluid and integrating turbulators. In this empirical and numerical survey, numerical simulations were performed to specify the impact of applying different turbulators and hybrid nanofluid on the flow and thermal characteristics in a concentric U-tube HE. The achieved outcomes exhibited that using hybrid nanofluid can notably increase the thermal performance with negligible pressure drop in comparison with two different turbulator modifications.