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The conventional strain gauge type pressure sensor suffers in static testing of engines due to the contact transduction method. This paper aims to focus on the concept of non-contact transduction-based pressure sensor using eddy current displacement sensing coil (ECDS) to overcome the temperature limitations of the strain gauge type pressure sensor. This paper includes the fabrication of prototypes of the proposed pressure sensor and its performance evaluation by static calibration. The fabricated pressure sensor is proposed to measure pressure in static test environment for a short period in the order of few seconds. The limitations of the fabricated pressure sensor related to temperature problems are highlighted and the suitable design changes are recommended to aid the future design.

The design of ECDS-based pressure sensor is aimed to provide non-contact transduction to overcome the limitations of the strain gauge type of pressure sensor. The ECDS is designed and fabricated with two configurations to measure deflection of the diaphragm corresponding to the applied pressure. The fabricated ECDS is calibrated using a standard micro meter to ensure transduction within limits. The fabricated prototypes of pressure sensors are calibrated using dead weight tester, and the calibration results are analyzed to select the best configuration. The proposed pressure sensor is tested at different temperatures, and the test results are analyzed to provide recommendations to overcome the shortcomings.

The performance of the different configurations of the pressure sensor using ECDS is evaluated using the calibration data. The analysis of the calibration results indicates that the pressure sensor using ECDS (coil-B) with the diaphragm as target is the best configuration. The accuracy of the fabricated pressure sensor with best configuration is ±2.8 per cent and the full scale (FS) output is 3.8 KHz. The designed non-contact transduction method extends the operating temperature of the pressure sensor up to 150°C with the specified accuracy for the short period.

Most studies of eddy current sensing coil focus on the displacement and position measurement but not on the pressure measurement. This paper is concerned with the design of the pressure sensor using ECDS to realize the non-contact transduction to overcome the limitations of strain gauge type pressure sensors and evaluation of the fabricated prototypes. It is shown that the accuracy of the proposed pressure sensor is not affected by the high temperature for the short period due to non-contact transduction using ECDS.

In Aerospace applications, the inlet tubes are used to mount strain gauge type pressure sensors on the engine under static test to measure engine chamber pressure. This paper aims to focus on the limitations of the inlet tube and its design aspects to serve better in the static test environment. The different sizes of the inlet tubes are designed to meet the static test and safety requirements. This paper presents the performance evaluation of the designed inlet tubes with calibration results and the selection criteria of the inlet tube to measure combustion chamber pressure with the specified accuracy during static testing of engines.

Two sensors, specifically, one cavity type pressure sensor with the inlet tube of range 0-6.89 MPa having natural frequency of the diaphragm 17 KHz and another flush diaphragm type pressure sensor of the same range having −3 dB frequency response, 5 KHz are mounted on the same pressure port of the engine under static test to study the shortcomings of the inlet tube. The limitations of the inlet tube have been analyzed to aid the tube design. The different sizes of inlet tubes are designed, fabricated and tested to study the effect of the inlet tube on the performance of the pressure sensor. The dynamic calibration is used for this purpose. The dynamic parameters of the sensor with the designed tubes are calculated and analyzed to meet the static test requirements. The diaphragm temperature test is conducted on the representative hardware of pressure sensor with and without inlet tube to analyze the effect of the inlet tube against the temperature error. The inlet tube design is validated through the static test to gain confidence on measurement.

The cavity type pressure sensor failed to capture the pressure peak, whereas the flush diaphragm type pressure sensor captured the pressure peak of the engine under a static test. From the static test data and dynamic calibration results, the bandwidth of cavity type sensor with tube is much lower than the required bandwidth (five times the bandwidth of the measurand), and hence, the cavity type sensor did not capture the pressure peak data. The dynamic calibration results of the pressure sensor with and without an inlet tube show that the reduction of the bandwidth of the pressure sensor is mainly due to the inlet tube. From the analysis of dynamic calibration results of the sensor with the designed inlet tubes of different sizes, it is shown that the bandwidth of the pressure sensor decreases as the tube length increases. The bandwidth of the pressure sensor with tube increases as the tube inner diameter increases. The tube with a larger diameter leads to a mounting problem. The inlet tube of dimensions 6 × 4 × 50 mm is selected as it helps to overcome the mounting problem with the required bandwidth. From the static test data acquired using the pressure sensor with the selected inlet tube, it is shown that the selected tube aids the sensor to measure the pressure peak accurately. The designed inlet tube limits the diaphragm temperature within the compensated temperature of the sensor for 5.2 s from the firing of the engine.

Most studies of pressure sensor focus on the design of a sensor to measure static and slow varying pressure, but not on the transient pressure measurement and the design of the inlet tube. This paper presents the limitations of the inlet tube against the bandwidth requirement and recommends dynamic calibration of the sensor to evaluate the bandwidth of the sensor with the inlet tube. In this paper, the design aspects of the inlet tube and its effect on the bandwidth of the pressure sensor and the temperature error of the measured pressure values are presented with experimental results. The calibration results of the inlet tubes with different configurations are analyzed to select the best geometry of the tube and the selected tube is validated in the static test environment.

The aim of the article is to ascertain the challenges, lessons learned and managerial implications in the deployment of Lean Six Sigma (LSS) competitiveness to micro, small and medium Enterprises (MSME) in India and to establish doctrines to strengthen the initiatives of the government.

The research adopts the Action Research methodology to develop a case study, which is carried out in the printing industry in a Tier III city using the LSS DMAIC (Define-Measure-Analyze-Improve-Control) approach. It utilizes LSS tools to deploy the strategy and to unearth the challenges and success factors in improving the printing process of a specific batch of a product.

The root cause for the critical to quality (CTQ) characteristic, turn-around-time (TAT) is determined and the solutions are deployed through the scientifically proven data-based approach. As a result of this study, the TAT reduced from an average of 1541.2–1303.36 min, which in turn, improved the sigma level from 0.55 to 2.96, a noteworthy triumph for this MSME. The company realizes an annual savings of USD 12,000 per year due to the success of this project. Top Management Leadership, Data-Based Validation, Technical Know-how and Industrial Engineering Knowledge Base are identified as critical success factors (CSFs), while profitability and on-time delivery are the key performance indicators (KPIs) for the MSME. Eventually, the lessons learned and implications indicate that LSS competitiveness can be treated as quality management standards (QMS) and quality tools and techniques (QTT) to ensure competitive advantage, sustainable green practices and growth.

Even though the findings and recommendations of this research are based on a single case study, it is worth noting that the case study is executed in a Tier III city along with novice users of LSS tools and techniques. This indicates the applicability of LSS in MSME and thus, the modality adopted can be further refined to suit the socio-cultural aspects of India.

This article illustrates the deployment of LSS from the perspective of novice users, to assist MSME and policymakers to reinforce competitiveness through LSS. Moreover, the government can initiate a scheme in line with LSS competitiveness to complement the existing schemes based on the findings of the case study.

The purpose of this study is to present a systematic literature review of academic peer-reviewed articles in English published between 2005 and 2021. The articles were reviewed based on the following features: research topic, conceptual and theoretical characterization, artificial intelligence (AI) methods and techniques.

This study examines the extent to which AI features within academic research in retail industry and aims to consolidate existing knowledge, analyse the development on this topic, clarify key trends and highlight gaps in the scientific literature concerning the role of AI in retail.

The findings of this study indicate an increase in AI literature within the field of retailing in the past five years. However, this research field is fairly fragmented in scope and limited in methodologies, and it has several gaps. On the basis of a structured topic allocation, a total of eight priority topics were identified and highlighted that (1) optimizing the retail value chain and (2) improving customer expectations with the help of AI are key topics in published research in this field.

This study is based on academic peer-reviewed articles published before July 2021; hence, scientific outputs published after the moment of writing have not been included.

This study contributes to the in-depth and systematic exploration of the extent to which retail scholars are aware of and working on AI. To the best of the author’s knowledge, this study is the first systematic literature review within retailing research dealing with AI technology.

The purpose of this study was to fabricate carbon nanotubes (CNT)-reinforced chromium oxide coatings and investigate mechanical and microstructural properties of these newly developed coatings on the boiler tube steel.

1 and 4 Wt.% CNT-reinforced Cr₂O₃ composite coatings were prepared and successfully deposited on ASTM-SA213-T22 (T22) boiler tube steel substrates using high-velocity oxy fuel (HVOF) thermal spraying method. Microhardness, porosity, metallography, X-ray diffraction (XRD), scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy, cross-sectional elemental analysis and X-ray mapping analysis have been used to examine the coated specimens.

The porosity of the CNT-Cr₂O₃ composite coatings was found to be decreasing with the increases in CNT content, and hardness has been found to be increasing with increase in percentage of CNT in the composite coatings. The CNT were able to increase hardness by approximately 17 per cent. It was found that the CNT were uniformly distributed throughout Cr₂O₃ matrix. The CNT were found to be chemically inert during the spraying process.

It must be mentioned here that studies related to fabrication of HVOF sprayed CNT reinforced Cr₂O₃ composite coatings on T22 boiler tube steel are not available in the literature. Hence, present investigation can provide valuable information related to fabrication and properties of CNT reinforced coatings on boiler steel.

Aerospace and defence industries use the materials having better properties at elevated temperatures, and Inconel 718 is one of that. The complexity in realizing complex and intricate shapes necessitate the product realization through additive manufacturing. This paper aims to investigate the wear behaviour of additive manufactured material.

The wear behaviour of additively manufactured Inconel 718 samples through direct metal laser sintering process at three different build orientations was experimentally investigated using a standard pin-on-disc wear tester.

Among the varied wear parameters, the load was identified as the most influencing parameter on the wear rate. In addition, the post-failure analysis of the worn surface of the pins under the scanning electron microscopy revealed the presence of various wear mechanisms.

Almost, the industries are now focussed on their production through additive manufacturing owing to its advantages. The present work displays the wear behaviour of the additive manufactured Inconel 718 and its associated wear mechanisms.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0322.

The purpose of this paper is to present and discuss the external factors of the solar dryer design that influenced the thermal efficiency of the solar dryer that contribute to the better quality of dried food products.

From the reviewed works of literature, the external factors including the drying temperature, airflow rate and relative humidity have significant effects to increase the rate of moisture diffusivity of the freshly harvested products during the drying process. The proper controls of airflow rate (Q), velocity (V), relative humidity (RH%) and drying temperature (°C) can influence the dried product quality. The dehydration ratio is the procedure to measure the quality of the dried food product.

The indirect solar dryer including the mixed-mode, hybrid and integrated was found shorter in drying time and energy-intensive compared to sun drying and direct drying. The recommended drying temperature is from 35.5°C to 70°C with 1–2 m/s velocity and 20%–60% relative humidity. The optimum thermal efficiency can be reached by additional devices, including solar collectors and solar accumulators. It gives a simultaneous effect and elongated the drying temperature 8%–10% higher than ambient temperature with 34%–40% energy saving. The recommended airflow rate for drying is 0.1204 to 0.0894 kg/s. Meanwhile, an airflow rate at 0.035–0.04 kg/m2 is recommended for an optimum drying kinetic performance.

This paper discusses the influence of the external factors of the solar dryer design on the thermal performance of the solar dryer and final dried food products quality. Therefore, the findings cannot serve as a statistical generalization but should instead be viewed as the quantitative validation subjected to fundamentals of the solar dryer design process and qualitative observation of the dried food product quality.

A well-designed of solar dryer with low operating and initial fabrication cost, which is simple to operate is useful for the farmers to preserve surplus harvested crops to an acceptable and marketable foods product. The optimization of the external and internal factors can contribute to solar dryer thermal performance that later provides an organoleptic drying condition that results in good quality of dried product and better drying process. The recommended drying temperature for a drying method is between 35°C up to 70°C. Drying at 65.56°C was effective to kill microorganisms. Meanwhile, drying at 50°C consider as average drying temperature. The recommended airflow rate for drying is 0.1204 to 0.0894 kg/s. Meanwhile, air flowrate at 0.035–0.04 kg/m² is recommended for optimum drying kinetic performance. The recommended value of aspect ratio and mass flow rate is 200 to 300 for an optimum evaporation rate. The good quality of dried products and good performance of solar dryers can be developed by proper control of airflow rate (Q), velocity (V), relative humidity (RH%) and drying temperature (°C).

The proper control of the drying temperature, relative humidity and airflow rate during the drying process will influence the final dried food products in terms of shape, color, aroma, texture, rupture and nutritious value. It is crucial to control the drying parameters because over-drying caused an increment of energy cost and reduces the dry matter. The quick-drying will disturb the chemical process during fermentation to be completed.

This study identifies the potential of the solar drying method for dehydrating agricultural produces for later use with the organoleptic drying process. The organoleptic drying process can reduce mold growth by promising an effective diffusion of moisture from freshly harvested products. The research paper gives useful understandings that well-designed solar drying technology gives a significant effect on dried product quality.

This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot.

In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R²) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function.

In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R² (97.5 per cent) and testing values R² (99 per cent). The R² for multiple regressions was obtained as 96.1 per cent.

The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.

For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.

In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.

To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.

The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.

Cloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.

The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.

This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.