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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.

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.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

It has been suggested that to be successful in the current global economy with increased competition and ever changing markets, especially in the post-crisis context, firms need to focus more on innovation in exploring new ideas and designing new products to develop new markets than on cost-cutting strategies to maintain cost leadership in old markets. However, because of the lack of micro data, this conjecture has not been systematically evaluated. This paper aims to fill this important void by studying the economic performance associated with these two different business strategies using Canadian micro data.

The main data for our analysis are from the Survey of Innovation and Business Strategy (2009 and 2012) which is a sample-based survey of Canadian government. The authors used in this research regression models for the econometric analysis of the underlying factors for undertaking certain business strategies and how business strategies link to economic performance. They also used propensity score matching to ensure the group of firms with innovation strategy being comparable to that with cost-cutting.

The research shows that firms focusing on product innovation are indeed more productive than firms focusing on cost-cutting, although there is no evidence that these two different strategies make a difference in profitability. The first indication from the research has been that certain characteristics of Canadian firms are very useful predictors for firms to undertake product innovation. They are, among other things, the age of the firms, the single-establishment structure of the business and being multinationals.

This empirical research opens up many interesting avenues for future research. Some other variables could be integrated into the models to increase the rate of explained variance. Moreover, because this research is based only on the case of Canadian firms and for a relatively short period of four years after the 2008 crisis, an extension to other context and to a longer period of time should be interesting.

The research has confirmed that Canadian firms adopting long-term business strategies based on product innovation are more productive.

The results truly concur with the vision of the Government of Canada, like some other developed countries, on the importance of innovation and its policies in encouraging business innovation in driving the growth of the Canadian economy and improving the standard of living of country.

Mainly because of the lack of micro data, the existing researches have not provided solid evidence on why firms are choosing different business strategies when they are operating in the same business conditions and how the financial crisis has affected the undertaking of business strategies. They have not established a clear linkage between economic performance and different business strategies, although there has been some anecdotal evidence about their association. This study aims to bridge the knowledge gaps with theoretical and practical contributions.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

Molecular dynamics is an emerging simulation technique in the field of machining in recent years. Many researchers have tried to simulate different processing methods of various materials with the theory of molecular dynamics (MD), and some preliminary conclusions have been obtained. However, the application of MD simulation is more limited compared with traditional finite element model (FEM) simulation technique due to the complex modeling approach and long computation time. Therefore, more studies on the MD simulations are required to provide a reliable theoretical basis for the nanoscale interpretation of grinding process. This study investigates the crystal structures, dislocations, force, temperature and subsurface damage (SSD) in the grinding of iron-nickel alloy using MD analysis.

In this study the simulation model is established on the basis of the workpiece and single cubic boron nitride (CBN) grit with embedded atom method and Morse potentials describing the forces and energies between different atoms. The effects of grinding parameters on the material microstructure are studied based on the simulation results.

When CBN grit goes through one of the grains, the arrangement of atoms within the grain will be disordered, but other grains will not be easily deformed due to the protection of the grain boundaries. Higher grinding speed and larger cutting depth can cause greater impact of grit on the atoms, and more body-centered cubic (BCC) structures will be destroyed. The dislocations will appear in grain boundaries due to the rearrangement of atoms in grinding. The increase of grinding speed results in the more transformation from BCC to amorphous structures.

This study is aimed to study the grinding of Fe-Ni alloy (maraging steel) with single grit through MD simulation method, and to reveal the microstructure evolution within the affected range of SSD layer in the workpiece. The simulation model of polycrystalline structure of Fe-Ni maraging steel and grinding process of single CBN grit is constructed based on the Voronoi algorithm. The atomic accumulation, transformation of crystal structures, evolution of dislocations as well as the generation of SSD are discussed according to the simulation results.

The purpose of this paper is to provide insights for flower retailers, horticultural practitioners and marketing managers into the prioritisation of cut flower attributes by German residents.

Applying a best–worst scaling approach, this analysis identified the relative ranking of importance amongst product attributes relevant to German consumers when buying fresh cut flowers. A latent class analysis determined four flower consumer segments for further study. The study builds on a sample of 978 consumers and is consistent with the most recent German census in terms of age, gender, income and federal state.

The best-worst analysis showed that intrinsic flower attributes, in particular appearance, freshness and scent were found to be more important to German consumers than the extrinsic attributes studied, namely, price, country of origin and a certification indicating fair trade. The latent class analysis determined four consumer segments that desire either budget, luxury or ethical flowers or more information about flowers. For all identified consumer segments, appearance was the attribute of greatest importance. The segments that desired luxury or ethical flowers, as well as the segment that desires more information were interested in appearance, but also had relatively large shares of preferences dedicated to flower freshness guarantees. The preference for freshness guarantees in addition to appearance may be interpreted jointly as a desire for not only beautiful and aesthetically pleasing flowers, but for sustained beauty.

Internationally, the study fills a research gap by exploring consumer’s relative preference for cut flower attributes. In contrast to existing studies on consumer preferences for flowers in Germany, the present study builds on a sample that was targeted in terms of age, gender, net household income and federal state to the most recent German census.

In manufacturing, dedicated machine tools and flexible machine tools are failing to satisfy the ever-changing manufacturing demands of short life cycles and dynamic nature of products. These machines are limited when new product designs are introduced. The solution lies in developing responsive machines that can be adjusted or be changed functionally when these change requirements arise. These machines are reconfigurable machines which are becoming the new focus, as they rapidly respond to product variety and volume changes. A sheet metal working machine known as a reconfigurable guillotine shear and bending press machine (RGS&BPM) has been developed. The purpose of this paper is to present a methodology, function-oriented design approach (FODA), which was developed for the design of the RGS&BPM.

The design of the machine is based on the six principles of reconfigurable manufacturing systems (RMSs), namely, modularity, scalability integrability, convertibility, diagnosability and customisability. The methodology seeks to optimise the design process of the RGS&BPM through a design of modules that make up the machine, enable its conversion and reconfiguration. The FODA is focussed on function identification to select the operational function required. Two main functions are recognised for the machine, these being cutting and bending; hence, the design revolves around these two and reconfigurability.

The developed design methodology was tested in the design of a prototype for the reconfigurable guillotine shear and bending press machine. The prototype is currently being manufactured and will be subjected to functional tests once completed. This paper is being presented not only to present the methodology by to show and highlight its practical applicability, as the prototype manufacturers have been enthusiastic about this new approach.

The research was limited to the design methodology for the RGS&BPM, the machine which has been designed to completion using this methodology, with prototype being manufactured.

This study presents critical steps and considerations in the development of reconfigurable machines. The main thrust being to explore the best possibility of developing the machines with dual functionality that will assist in availing the technology to manufacturer. As the machine has been development, the success of the design can be directly attributed to the FODA methodology, among other contributing factors. It also highlights the significance of the principles of RMS in reconfigurable machine design.

The RGS&BM machine is an answer for the small-to-medium enterprises (SMEs), as the machine replaces two machines with one, and the methodology ensures its affordable design. It contributes immensely to the machine availability by eliminating trial and error approaches.

This study presents a new approach to the design of reconfigurable dual machines using principles of RMS. As the targeted market is the SME, it is not limited to that as any entrepreneur may use the machine to their advantage. The design methodology presented contributes to the body of knowledge in dual reconfigurable machine tool design.

This paper has two purposes. First, it aims to explore how Australian universities used calculative rhetoric and practices through accounting numbers to persuade employees and legitimize their financial recovery plans to alleviate the financial hardship caused by the COVID-19 pandemic. Second, it aims to analyze how the accounting-based solutions were legitimized through a well-blended pathos, logos and ethos rhetoric.

Building on a rhetorical theory of diffusion, we employed a qualitative research design within all 37 Australian public universities involving Internet-based documentary analysis.

This study finds that in an urgent crisis like the fiscal crisis caused by COVID-19, universities again found rescue in accounting tools, in particular budgets, as a rhetorical device to justify their operational and strategic choices such as job-cuts, programs closures and staff pay-cuts. However, in this crisis, the same old accounting-based solutions were even more quickly to be accepted by being delivered in management’s colorful blending of pathos–logos–ethos rhetoric.

While this study is constrained to Australian public universities’ financial responses, its findings have implications for university decision-makers and higher education policymakers across the globe when it comes to university management using calculative devices in persuading employees to work their way through financial hardship caused by an extreme health crisis-like COVID-19 pandemic.

This study adds more evidence that the use of budgets as a calculative tool continues to play a key role in organizations in the construction, mobilization and preservation of certain strategic and operational choices during volatilities. Especially, the same way of creating calculative-based solutions can be communicated via the colorful blending of different rhetoric to make it acceptable.

The cardiomegaly can be determined by the cardiothoracic ratio (CTR) which can be measured in a chest x-ray image. It is calculated based on a relationship between a size of heart and a transverse dimension of chest. The cardiomegaly is identified when the ratio is larger than a cut-off threshold. This paper aims to propose a solution to calculate the ratio for classifying the cardiomegaly in chest x-ray images.

The proposed method begins with constructing lung and heart segmentation models based on U-Net architecture using the publicly available datasets with the groundtruth of heart and lung masks. The ratio is then calculated using the sizes of segmented lung and heart areas. In addition, Progressive Growing of GANs (PGAN) is adopted here for constructing the new dataset containing chest x-ray images of three classes including male normal, female normal and cardiomegaly classes. This dataset is then used for evaluating the proposed solution. Also, the proposed solution is used to evaluate the quality of chest x-ray images generated from PGAN.

In the experiments, the trained models are applied to segment regions of heart and lung in chest x-ray images on the self-collected dataset. The calculated CTR values are compared with the values that are manually measured by human experts. The average error is 3.08%. Then, the models are also applied to segment regions of heart and lung for the CTR calculation, on the dataset computed by PGAN. Then, the cardiomegaly is determined using various attempts of different cut-off threshold values. With the standard cut-off at 0.50, the proposed method achieves 94.61% accuracy, 88.31% sensitivity and 94.20% specificity.

The proposed solution is demonstrated to be robust across unseen datasets for the segmentation, CTR calculation and cardiomegaly classification, including the dataset generated from PGAN. The cut-off value can be adjusted to be lower than 0.50 for increasing the sensitivity. For example, the sensitivity of 97.04% can be achieved at the cut-off of 0.45. However, the specificity is decreased from 94.20% to 79.78%.