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Three-dimensional printing (3DP) is an established process to print structural parts of metals, ceramic and polymers. Further, multi-material 3DP has the potentials to be a milestone in rapid manufacturing (RM), customized design and structural applications. Being compatible as functionally graded materials in a single structural form, multi-material-based 3D printed parts can be applied in structural applications to get the benefit of modified properties.

The fused deposition modelling (FDM) is one of the established low cost 3DP techniques which can be used for printing functional/ non-functional prototypes in civil engineering applications.

The present study is focused on multi-material printing of primary recycled acrylonitrile butadiene styrene (ABS), polylactic acid (PLA) and high impact polystyrene (HIPS) in composite form. Thermal (glass transition temperature and heat capacity) and mechanical properties (break load, break strength, break elongation, percentage elongation at break and Young’s modulus) have been analysed to observe the behaviour of multi-material composites prepared by 3DP. This study also highlights the process parameters optimization of FDM supported with photomicrographs.

The present study is focused on multi-material printing of primary recycled ABS, PLA and HIPS in composite form.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The wide application of metal material extrusion (MEX) has been hampered by the practicalities associated with the resulting shrinkage of the final parts when commercial three-dimensional (3D) printing equipment is used. The shrinkage behaviour of MEX metal parts is a very important aspect of the MEX metal production process, as the parts must be accurately oversized to compensate for shrinkage. This paper aims to investigate the influence of primary 3D printing parameters, namely, print speed, layer height and print angle, on the shrinkage behaviour of MEX Steel 316L parts.

Two groups of dog-bone and rectangular-shape specimens were produced with the BASF Ultrafuse Steel 316L metal filament. The length, width and thickness of the specimens were measured pre- and post-debinding and sintering to calculate the percentile shrinkage rates. Analysis of variance (ANOVA) was used to evaluate and rank the significance of each manufacturing parameter on shrinkage. Typical main print quality issues experienced in this analysis are also reported.

The shrinkage rates of the tested specimens ranged from 15.5 to 20.4% along the length and width axis and 18.5% to 23.1% along the thickness axis of the specimens. Layer height and raster angle were the most statistically significant parameters influencing shrinkage, while print speed had very little influence. Three types of defects were observed, including surface roughness, surface deformation (warping and distortion) and balling defects.

This paper bridges an existing gap in MEX Steel 316L literature, with a focus on the relationship between MEX manufacturing parameters and subsequent shrinkage behaviour. This study provides an in-depth analysis of the relationship between manufacturing parameters – layer height, raster angle and print speed and subsequent shrinkage behaviour, thereby providing further information on the relationship between the former and the latter.

This study aims to computational numerical simulations to clarify and explore the influences of periodic cellular lattice (PCL) morphological parameters – such as lattice structure topology (simple cubic, body-centered cubic, z-reinforced body-centered cubic [BCCZ], face-centered cubic and z-reinforced face-centered cubic [FCCZ] lattice structures) and porosity value ( ) – on the thermal-hydraulic characteristics of the novel trussed fin-and-elliptical tube heat exchanger (FETHX), which has led to a deeper understanding of the superior heat transfer enhancement ability of the PCL structure.

A three-dimensional computational fluid dynamics (CFD) model is proposed in this paper to provide better understanding of the fluid flow and heat transfer behavior of the PCL structures in the trussed FETHXs associated with different structure topologies and high-porosities. The flow governing equations of the trussed FETHX are solved by the CFD software ANSYS CFX® and use the Menter SST turbulence model to accurately predict flow characteristics in the fluid flow region.

The thermal-hydraulic performance benchmarks analysis – such as field synergy performance and performance evaluation criteria – conducted during this research successfully identified demonstrates that if the high porosity of all PCL structures decrease to 92%, the best thermal-hydraulic performance is provided. Overall, according to the obtained outcomes, the trussed FETHX with the advantages of using BCCZ lattice structure at 92% porosity presents good thermal-hydraulic performance enhancement among all the investigated PCL structures.

To the best of the authors’ knowledge, this paper is one of the first in the literature that provides thorough thermal-hydraulic characteristics of a novel trussed FETHX with high-porosity PCL structures.

The purpose of this paper is to present the properties of thick-film resistors made of novel pastes prepared from glass and graphite.

Graphite-based resistors were made of thick-film pastes with different graphite-to-glass mass fraction were prepared and examined. Sheet resistance, temperature coefficient of resistance, impact of humidity and short-term overload were investigated. The properties of the layers fired in atmospheres of air at 550°C and nitrogen at 875°C were compared.

Graphite-based resistors with various graphite-to-glass ratios made possible to obtain a wide range of sheet resistance from single O/square to few kO/square. These values were dependent on firing atmosphere, paste composition and the number of screen-printed layers. The samples made of paste with 1:1 graphite-to-glass ratio exhibited the temperature coefficient of resistance of about −1,000 ppm/°C, almost independently on the firing atmosphere and presence of a top coating. The resistors fired in the air after coating with overglaze, exhibited significantly lower sheet resistance, reduced impact of humidity and improved power capabilities.

In this paper, graphite-based resistors for applications in typical high-temperature cermet thick-film circuits were presented, whereas typical graphite-based resistors were fabricated in polymer thick-film technology. Owing to very low cost of the graphite, the material is suitable for low-power passive circuits, where components are not subjected into high temperature, above the typical temperature of operation of standard electronic components.

Because of the significant differences in the features and requirements of specific products and the capabilities of various additive manufacturing (AM) solutions, selecting the most appropriate AM technology can be challenging. This study aims to propose a method to solve the complex process selection in 3D printing applications, especially by creating a new multicriteria decision-making tool that takes the direct certainty of each comparison to reflect the decision-maker’s desire effectively.

The methodology proposed includes five steps: defining the AM technology selection decision criteria and constraints, extracting available AM parameters from the database, evaluating the selected AM technology parameters based on the proposed decision-making methodology, improving the accuracy of the decision by adopting newly proposed weighting scheme and selecting optimal AM technologies by integrating information gathered from the whole decision-making process.

To demonstrate the feasibility and reliability of the proposed methodology, this case study describes a detailed industrial application in rapid investment casting that applies the weightings to a tailored AM technologies and materials database to determine the most suitable AM process. The results showed that the proposed methodology could solve complicated AM process selection problems at both the design and manufacturing stages.

This research proposes a unique multicriteria decision-making solution, which employs an exclusive weightings calculation algorithm that converts the decision-maker's subjective priority of the involved criteria into comparable values. The proposed framework can reduce decision-maker's comparison duty and potentially reduce errors in the pairwise comparisons used in other decision-making methodologies.

This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions.

The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations.

A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works.

The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity.

The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.

There are many academic contributions dealing with the impact of additive manufacturing on supply chains (Ben-Ner and Siemsen, 2017; Durach, 2017; Gravier and Roethlein, 2018; Brown, 2018; Rogers et al., 2016; Sasson and Johnson, 2016; Nyman and Sarlin, 2014). But how future supply chain design may differ from today is still vague. In this article, possible scenarios are discussed and decision support is provided for the management, which is responsible for long-term strategic decisions.

This papers introduces the general characteristics of additive manufacturing and its next steps of development. Based on these technological assumptions various scenarios are systematically derived applying the standardized nomenclature of SCOR-model. Resulting threats and chances will be discussed and finally brought to a conclusion.

With the spread of additive manufacturing, the industry has the opportunity to pursue completely new approaches in terms of product development, design and product properties. This not only leads to new competitive models and the possibility of customer individualization of the products down to volume “1”. In addition, there are new models for supply chain management that can be used to react quickly and flexibly to customer requests. Already today new approaches for the cooperation between partners play an essential role.For start-ups, market entry should be simplified by using the resulting opportunities.

Future developments and especially the development speed of additive manufacturing are not predictable. Therefore, the expected scenarios may differ from reality and lead to a different supply chain design. There will also be industries that can use additive manufacturing much more intensively than others – not least because of the technological restrictions of the manufacturing process. Corporate culture and the overcoming of technical challenges are a decisive factor.

This paper gives supply chain management an outlook on future development opportunities. This enables management to set the right course for a future-oriented position today.

The changes in the supply chain will open up new business models while existing models will disappear. This leads to a change in the field of logistics but also for many technology providers. As a consequence, there will be serious changes (opportunities and risks) for the employees involved and their working environment.

This paper enables management to understand the scope and impact of upcoming changes. In this way, it significantly promotes awareness-raising and contributes to the future-oriented proceeding of companies.

This study aims to, first, propose a valid and reliable scale to document the COVID-19 Pandemic Shopping Experience (CPSE) and, second, determine the impact of its variables on the postpurchase shopping experience (PPSE).

For scale development, published studies were scanned and the variables were shortlisted. These shortlisted variables were validated by 52 faculties from four universities in Saudi Arabia. Data were collected from 318 respondents to purify the CPSE Scale. In Study 2, a path analysis was performed on a sample of 354 respondents to determine the individual impact of each variable on PPSE.

A total of 14 items were found to be aligned under four variables, social distance (SD), shop hygiene, operational time and entertainment venues. SD was found to have the greatest influence on PPSE, followed by operational time and shop hygiene.

This research has important implications for retailers to initiate changes in store layout so that they can implement social distancing by physically marking stickers on the floors and by placing barricading on billing counters. Store hygiene can be ensured by making sanitizers and hand gloves available at the entry points, periodically cleaning the floor and sanitizing the premises. Rationing the operating time proved to be an effective tool to minimize the exposure time, thereby limiting consumers' time inside the store.

To the best of the authors’ knowledge, this is the first study to propose a full-scale measure of the customer shopping experience (SE) during a pandemic. This scale can be generalized to measure SE in similar situations.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.