Rona Bahreini, Leila Doshmangir and Ali Imani
Effective maintenance management of medical equipment is one of the major issues for quality of care and cost-effectiveness especially in modern hospitals. An effective medical…
Abstract
Purpose
Effective maintenance management of medical equipment is one of the major issues for quality of care and cost-effectiveness especially in modern hospitals. An effective medical equipment maintenance management (MEMM) consists of adequate planning, management and implementation. This is essential for providing good health services and saving scarce resources. Considering the importance of the subject, the purpose of this paper is to extract the influential factors on MEMM using a qualitative approach.
Design/methodology/approach
Documents review and interviews were main methods for data collection. Semi structured interviews were conducted with a purposive sample of 14 clinical engineers with different degree of education and job levels. Interviews were voice recorded and transcribed verbatim. Qualitative data were analyzed using a content analysis approach (inductive and deductive) to identify the underlying themes and sub-themes.
Findings
Factors influencing an effective and efficient MEMM system categorized in seven themes and 19 sub-themes emerged. The themes included: “resources,” “quality control,” “information bank,” “education,” “service,” “inspection and preventive maintenance” and “design and implementation.”
Originality/value
The proposed framework provides a basis for a comprehensive and accurate assessment of medical equipment maintenance. The findings of this study could be used to improve the profitability of healthcare facilities and the reliability of medical equipment.
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Ali Heidari, Din Mohammad Imani and Mohammad Khalilzadeh
This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic…
Abstract
Purpose
This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.
Design/methodology/approach
In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.
Findings
As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.
Practical implications
The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.
Originality/value
The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.
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Abhishek Shrivastava, Anand Kumar S. and Samrat Rao
This study used an indentation-based mechanical testing framework for the mechanical characterization of laser powder bed fusion (LPBF) processed Inconel 718 on a wrought Inconel…
Abstract
Purpose
This study used an indentation-based mechanical testing framework for the mechanical characterization of laser powder bed fusion (LPBF) processed Inconel 718 on a wrought Inconel 718 substrate. The purpose of the paper is to investigate the effectiveness of the indentation-based approach for localized mechanical evaluation.
Design/methodology/approach
The LPBF-processed wrought substrate was sectioned into three sections for microstructural and mechanical characterization. A 3D heat source model was used for the thermal analysis of the interface region. The developed interface region is probed using the Knoop hardness indenter in different orientations to determine the textural anisotropy and mechanical behavior of the region.
Findings
LPBF process develops a melted interface zone (MIZ) at the deposition-substrate interface. The MIZ exhibited a coarse grain structure region along with a larger primary dendritic arm spacing (PDAS), signifying a slower cooling rate. FE modeling of the LPBF process reveals heat accumulation in the substrate along with intrinsic heat treatment (IHT) induced due to layer-wise processing. The obtained yield locus shows strong anisotropy in the deposition region, whereas reduced anisotropy with a nearly uniform ellipse locus for the MIZ regions. This reduced anisotropy is attributable to IHT and heat accumulation in the substrate.
Originality/value
An alternative localized mechanical characterization tool has been investigated in this work. The approach proved sensitive to thermal variations during LPBF processing in an isolated region which extends its suitability to variable geometry parts. Moreover, the approach could serve as a screening tool for parts made from dissimilar metals.
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Muhammad Arif Mahmood, Marwan Khraisheh, Andrei C. Popescu and Frank Liou
This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing…
Abstract
Purpose
This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy.
Design/methodology/approach
Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects.
Findings
The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB.
Originality/value
The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.
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Lan Li, Tan Pan, Xinchang Zhang, Yitao Chen, Wenyuan Cui, Lei Yan and Frank Liou
During the powder bed fusion process, thermal distortion is one big problem owing to the thermal stress caused by the high cooling rate and temperature gradient. For the purpose…
Abstract
Purpose
During the powder bed fusion process, thermal distortion is one big problem owing to the thermal stress caused by the high cooling rate and temperature gradient. For the purpose of avoiding distortion caused by internal residual stresses, support structures are used in most selective laser melting (SLM) process especially for cantilever beams because they can assist the heat dissipation. Support structures can also help to hold the work piece in its place and reduce volume of the printing materials. The mitigation of high thermal gradients during the manufacturing process helps to reduce thermal distortion and thus alleviate cracking, curling, delamination and shrinkage. Therefore, this paper aims to study the displacement and residual stress evolution of SLMed parts.
Design/methodology/approach
The objective of this study was to examine and compare the distortion and residual stress properties of two cantilever structures, using both numerical and experimental methods. The part-scale finite element analysis modeling technique was applied to numerically analyze the overhang distortions, using the layer-by-layer model for predicting a part scale model. The validation experiments of these two samples were built in a SLM platform. Then average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model.
Findings
The validation experiments results of average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model. It was found that they matched well with each other. From displacement and residual stress standpoint, by introducing two different support structure, two samples with the same cantilever beam can be successfully printed. In terms of reducing wasted support materials, print time and high surface quality, sample with less support will need less post-processing and waste energy.
Originality/value
Numerical modeling in this work can be a very useful tool to parametrically study the feasibility of support structures of SLM parts in terms of residual stresses and deformations. It has the capability for fast prediction in the SLMed parts.
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Gabriella Arcese, Grazia Chiara Elmo, Fabio Fortuna, Maria Giovina Pasca and Mario Risso
The study investigates how consumers' food purchasing habits changed during the Covid-19 pandemic in Italy. The research aims to understand if traditional aspects, health…
Abstract
Purpose
The study investigates how consumers' food purchasing habits changed during the Covid-19 pandemic in Italy. The research aims to understand if traditional aspects, health consciousness and environmental concerns have influenced and changed the purchases of food products post-pandemic.
Design/methodology/approach
The authors developed a theoretical model to understand whether health consciousness, traditional aspects and environmental concerns affect consumers' purchasing intention. The study collects secondary data to analyse state of the art and investigate consumer behaviour in the agri-food system after the pandemic. Thereafter, a survey was conducted via a convenience random sampling procedure. The data (n = 622) were analysed using the formulated research framework and tested through the structural equation modelling procedure.
Findings
The findings reveal that health consciousness and traditional aspects (culinary traditions, ingredients usage from one's territory of origin, products' origin attention) are among the main reasons for purchasing agri-food goods after the pandemic. Instead, environmental concerns negatively affect consumers' purchase intentions.
Originality/value
The study identifies which aspects influenced consumers' purchasing intentions after the Covid-19 pandemic. It also provides insights for food companies and policymakers on the factors to be improved to optimize the agri-food sector following a sustainable perspective and in order to develop effective business strategies.