Search results

This paper presents the influence of beam incidence angle on austenitic stainless steel sheet subjected to a high density laser beam having Gaussian power density distribution. Bead‐on trials are conducted on 3.15 mm thick commercial AISI 304 austenitic stainless steel sheet using a Nd:YAG laser source with a maximum output of 2kW in the continuous wave mode. The effects of beam incident angle on the weld bead geometry are studied using finite element analysis. Experiments are conducted with 600, 1000 and 1400W laser power and 800, 1400 and 2000mm/min welding speed. A three dimensional finite element model is developed for the simulation of non‐linear transient thermal analysis of the weld bead geometry for different beam incident angles using the finite element code ANSYS. The result reveals that by increasing the beam incident angle with constant beam power and welding speed, there is a considerable reduction in the depth of penetration‐to‐width ratio (d/w). Further, it is noticed that the process enters into conduction mode of welding from the keyhole mode of welding as the beam angle is increased beyond 10o. The comparison of the simulation results and the experimental data for weld bead geometry with different beam incident angles show good agreement.

The purpose of this study is to develop an elasto-plastic multi-material shell model by which finite element analysis of laser welded joints is carried out at the interface of the heat-affected zone and base material.

The multi-material shell model is implemented on the simple cantilever and double cantilever welded plates to examine the efficiency of the developed model.

By reducing the computational time approximately 20 times with the developed model, the results obtained in the form of von Mises stress and equivalent plastic strain are found in good agreement as compared with the reference solid model.

The accurate and fast prediction of the stresses and strains in the laser welded joints, and the developed multi-material model is helpful to simulate complex industrial welded structures.

This study aims to determine the near optimal welding process parameters (beam power (BP), travel speed (TS) and focal position (FP)) using grey relational analysis by simultaneously considering multiple output parameters (depth of penetration and bead width). Further, the optimized parameters were evaluated through the microstructural characterization and hardness measurements across the weld zone.

It is appropriate to apply Taguchi's technique to a complex system like welding process. Therefore, this study is made to determine the near optimal welding process parameters (BP, TS and FP) using grey relational analysis by simultaneously considering multiple output parameters (depth of penetration and bead width).

Taguchi experimental design for determining welding parameters was successful. The hardness of the Argon shielded weld metal was comparatively lesser than the Helium shielded weld metal. The Helium shielded weld metal microstructure comprises of finer grains and higher amounts of equiaxed grains. Argon and Helium shielded weld metal microstructure was endowed with a higher amount of secondary interdendritic austenite phase.

The optimal welding conditions were identified in order to increase the productivity and minimize the total operating cost. The process input parameters effect was determined under the optimal welding combinations.

The purpose of this paper is to develop mathematical relationships to calculate the loop length to knit compact plain knitted fabrics and to validate the model using the fabric parameters of commercial fabrics.

Ellipse defines the shape of the head of a knitted loop and straight lines define the arms of a knitted loop. The mathematical relationships developed relate the yarn count to the loop length of compact knitted fabrics. The experimental data and the data from previous similar research validate the accuracy of the mathematical model.

The model can calculate loop lengths to knit compact plain knitted fabrics in terms of thickness of the yarn and the coefficient defined to express the ratio of minor axis to major axis of the ellipse that defines the shape of the head of the loop. The mathematical model can deliver several loop lengths to produce compact plain knitted fabrics for different values of this coefficient. For commercial fabrics the error of the model was 0.53%.

The present model defines the head of the loop as an ellipse. The uniqueness of the present model is that several ellipses can exist for any given yarn thickness for a range of values assigned to the minor axis of the ellipse. The accuracy of the model against experimental data ascertains that the model is closer to the reality for commercial fabrics and proves the uniqueness of the model. Further, this model is an ideal and a simple model to introduce knitted loop configurations in teaching knitted fabric geometry.

This work highlights the optimization of the consumed amount of sewing thread required to make up a pair of jeans using three different metaheuristic methods; particular swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) techniques. Indeed, using metaheuristic optimization techniques enable industrialists to reach the lowest sewing thread quantities in terms of bobbins per garments. Besides, the compared results of this research can obviously prove the impact of each input parameter on the optimization of the sewing thread consumption per pair of jeans.

To assess objectively the sewing thread consumption, the optimized sewing conditions such as thread composition, needle size and fabric composition are investigated and discussed. Hence, a Taguchi design was elaborated to evaluate and optimize objectively the linear model consumption. Thanks to its principal characteristics and popularity, denim fabric is selected to analyze objectively the effects of studied input parameters. In addition, having workers with same skills and qualifications to repeat each time the same sewing process will involve having the same sewing thread consumption values. This can occur in some levels such as end of sewing, the number of machine failures, the kind of failure and its complexity, the competency of the mechanic and his way to repair failure, the loss of thread caused by threading and its frequency. Seam repetition due to operator lack of skill will obviously affect clothing appearance and hence quality decision. Interesting findings and significant relationship between input parameters and the amount of sewing thread consumption are established.

According to the comparative results obtained using metaheuristic methods, the PSO and ACO technique gives the lowest values of the consumption within the best combination of input parameters. The results show the accuracy of the applied metaheuristic methods to optimize the consumed amount needed to sew a pair of jeans with a notable superiority of both PSO and ACO methods compared to experimental ones. However, compared to GA method, ACO and PSO algorithms remained the most accurate techniques allowing industrials to minimize the consumed thread used to sew jeans. They can also widely optimize and predict the consumed thread in the investigated experimental design of interest. Consequently, compared to experimental results and regarding the low error values obtained, it may be concluded that the metaheuristic methods can optimize and evaluate both studied input and output parameters accurately.

This study is most useful for denim industrial applications, which makes it possible to anticipate, calculate and minimize the high consumption of sewing threads. This paper has not only practical implications for clothing appearance and quality but also for reduction in thread wastage occurring during shop floor conditions like machine running, thread breakage, repairs, etc. (Kawabata and Niwa, 1991). Unless the used sewing machine is equipped within a thread trimmer improvement in garment seam appearance cannot be achieved. By comparing and analyzing the operating activities of the regular lock stitch 301 machine with and without a thread trimmer, a difference in time processing can be grasped (Magazine JUKI Corporation, 2008). Time consumed in trimming by a lockstitch machine without a thread trimmer equals 3.1 s compared to 2.6 s by a thread trimming one. Hence, the reduction rate in the time processing equals 16.30%. This paper aimed to implement the optimal consumption (thread waste outstanding number of trials). Unless highly skilled workers are selected and well-motivated, the previous recommended changes will not be applied. The saved cost of the sewing thread reduction can be used to buy a better quality of fabric and/or thread. However, these factors are not always the same as they can vary according to customer's requirements because thread consumption is never a standard for sewn product categories such as trousers, shirts and footwear (Khedher and Jaouachi, 2015).

Until now, there is no work dealing with the investigation of the metaheuristic optimization of the consumed thread per pair of jeans to minimize accurately the amount of sewing thread as well as the sewing thread wastage. Even though these techniques of optimization are currently in full development due to some advantages such as generality and possible application to a large class of combinatorial and constrained assignment problems, efficiency for many problems in providing good quality approximate solutions for a large number of classical optimization problems and large-scale real applications, etc., are not applied yet to decrease sewing thread consumption. Some recent published works used statistical techniques (Taguchi, factorial, etc.), to evaluate approximate consumptions; conversely, other geometrical and mathematical approaches, considering some assumptions, used stitch geometry and remained insufficient to give the industrialists an implemented application generating the exact value of the consumed amount of sewing thread. Generally, in the clothing field 10–15% of sewing thread wastage should be added to the experimental approximate consumption value. Moreover, all investigations are focused on the approximative evaluations and theoretical modeling of sewing thread consumption as function of some input parameters. Practically, the obtained results are successfully applied and the ACO method gives the most accurate results. On the other hand, in the point of view of industrialists the applied metaheuristic methods (based on algorithms) used to decrease the amount of consumed thread remained an easy and fruitful solution that can allow them to control the number of sewing thread bobbin per garments.

Cement industry for both developed and developing countries is important from the economic point of view. It is playing a vital role in economic development of a developing country like Pakistan. However, these industries are posing threat to the environment, human health and plant species. The purpose of this paper is to identify the most critical factors of cement industry that have a negative impact on the environment, human health and plant species in the context of Pakistan.

The factors are categorized into air pollution, noise pollution, soil pollution, human health and plant species. These factors are categorized on the basis of previous literature and environmental safety reports. Air pollution is caused by iron and sulphur while noise pollution is mainly caused by crusher room and rotatory kiln end. The soil is being polluted by zinc and lead while human health and plant species are being damaged by sulphur dioxide and nitrogen dioxide. For the analysis purpose, a multi-criteria decision-making (MCDM) technique, i.e., decision-making trial and evaluation laboratory (DEMATEL) is used.

The result shows that the major cause of air pollution is “sulphur” while “crusher room and rotatory kiln end” are responsible for noise pollution. On the other hand, “mercury” is responsible for causing soil pollution while human health and plant species are influenced by the toxic effect of “nitrogen dioxide.”

The results obtained are specific to cement manufacturing industry of Pakistan and cannot be generalized for any other manufacturing sector.

The proposed methodology shows the most critical factors toward which concertation should be given for mitigating their impact. This study will help the government and the cement industry to focus on all those elements that are the most responsible for causing different types of pollution.

No such work is reported in previous research that proposes a framework using DEMATEL technique for analysis of critical factors of cement industries that have a dangerous impact on the environment and human health, especially in a developing country, like Pakistan.

The purpose of this paper is to compare the bursting strength, bursting distension, air permeability and wale wise wicking rate properties of recycled polyester (r-PET) and virgin polyester (v-PET) raw materials from which single jersey knitted fabric samples are manufactured. Meanwhile, numerical optimization method was used in predetermined parameters to determine the optimum r-PET and v-PET blend ratio and yarn manufacturing technology. In the optimization analysis, the average values of the important yarn and fabric properties inspected were taken as a target according to the 50 percent proportion of r-PET and v-PET fiber for both compact and ring yarn manufacturing technology.

To encourage the use of value-added textile products produced from recycling PET bottle with the focus of social responsibility is a condition that should be evaluated within the scope of waste management. The recycling of PET bottles and finding new opportunities for the uses in different field are crucial for both contributing environmental economy and conserving natural energy resources. The most important alternative ways is to use the r-PET fiber from recycling PET bottle in textile industry. In this study, 19.7 tex r-PET/cotton and v-PET/cotton-blended compact and ring spun yarns were produced at different blending ratios at the same production parameters.

Results showed that blend type, blend ratio and yarn manufacturing technology have statistical significance effect on bursting strength and air permeability. Besides, it was found that blend type has no significance on wale wise wicking rate unlike other parameters. Optimization analysis indicated that single jersey knitted fabric with v-PET/CO 58.62/41.38 percent compact yarn had higher desirability with the value of 0.72.

At the present time, r-PET fiber is blended in small amount (approximately 5–15 percent blend ratio) with both cotton and polyester together. In addition, it is possible using different fiber blend types instead of cotton and polyester according to the usage area. The most important question is to determine the amount of r-PET proportion. In other words, both optimum yarn/fabric quality parameters should be ensured and at the same time life cycle of the apparels should not be short when the optimum r-PET proportion is taken into consideration.

This study investigates the effect of ring and compact spun yarns such as Sussen Elite and Com4 spun yarn on the physical and comfort characteristics of single jersey, rib and plain interlock knitted fabrics. The physical characteristics such as fabric aerial density, tightness factor, spirality and pilling behaviour were studied and statistically analyzed using Multivariable ANOVA analysis. The comfort characteristics such as thermal insulation behaviour (TIV), water vapour permeability, wicking and air permeability were studied and reported. The test results showed that compact spun yarn knitted fabrics such as Sussen Elite and Com4 yarn fabrics demonstrated higher thermal insulation behaviour in all the knitted structures when compared to ring spun yarn knitted fabrics. The low stress mechanical characteristics such as shear and compressional behaviour of ring and compact spun yarn knitted fabrics were also reported.

The purpose of the paper is to examine the trade structure of India’s pharmaceutical sector with a focus on intra-industry trade (IIT).

This paper starts with analysing export destinations and import sources using significant trade shares; the study calculates IIT between India and its consistent trade partners at an aggregate level and considers the problem of categorical aggregation at a disaggregate level. To determine the determinants of IIT at different levels, the Vector Error Correction model used production-related data to identify the drivers of IIT. Also, the Granger causality test was used for short-run causality.

This study examining India’s consistent trade partners from 1993 to 2023, finds long-run association and short-run causality. The results show a significant long-run association between total IIT and factors like unskilled labour share, invested capital, fuel consumption, total input and net value added. The key low-vertical IIT (LVIIT) drivers are invested capital, unskilled labour, fixed capital and total inputs. The negative long-run association between the total input and LVIIT obtained implies a rising level of total input cost, leading to a fall in IIT and LVIIT. Also, a negative association is obtained for unskilled labour and total IIT, while a positive association is obtained for LVIIT. In the short run, causality indicates that total IIT is influenced by invested capital and fuel consumption, while unskilled labour shares and total inputs drive LVIIT. Both IIT types impact invested capital, highlighting the need for policy intervention in input markets. It provides insights for improving quality trade expansion and correcting production-related factors.

Unlike other studies on the pharmaceutical trade in India, this study analyses India’s pharmaceutical trade for a longer time period, focusing on destination-wise analysis and calculating the intra-industry trade index while taking care of the problem of categorical aggregation. Further, the study attempted to find the long-run association with production-related drivers.

Electricity consumption around the world and in India is continuously increasing over the years. Presently, there is a huge diversity in electricity tariffs across states in India. This paper aims to focus on development of new tariff design method using K-means clustering and gap statistic.

Numbers of tariff plans are selected using gap-statistic for K-means clustering and regression analysis is used to deduce new tariffs from existing tariffs. The study has been carried on nearly 27,000 residential consumers from Sangli city, Maharashtra State, India.

These tariff plans are proposed with two objectives: first, possibility to shift consumer’s from existing to lower tariff plan for saving electricity and, second, to increase revenue by increasing tariff charges using Pay-by-Use policy.

The study can be performed on hourly or daily data using automatic meter reading and to introduce Time of Use or demand based tariff.

The proposed study focuses on use of data mining techniques for tariff planning based on consumer’s electricity usage pattern. It will be helpful to detect abnormalities in consumption pattern as well as forecasting electricity usage.

Consumers will be able to decide own monthly electricity consumption and related tariff leading to electricity savings, as well as high electricity consumption consumers have to pay more tariff charges for extra electricity usage.

To remove the disparity in various tariff plans across states and country, proposed method will help to provide a platform for designing uniform tariff for entire country based on consumer’s electricity consumption data.