Seam efficiency of woven linen shirting fabric: process parameter optimisation

2017 ◽  
Vol 21 (4) ◽  
pp. 293-306
Author(s):  
Mallika Datta ◽  
Devarun Nath ◽  
Asif Javed ◽  
Nabab Hossain
Keyword(s):  
Regression Model ◽  
Response Surface ◽  
Process Parameters ◽  
Classical Method ◽  
Statistical Technique ◽  
Process Parameter ◽  
Content Type ◽  
Commercial Grade ◽  
Sewing Thread ◽  
Stitch Density

Purpose The focus of this research is to identify the optimum commercial grade sewing thread and stitch density to be used with woven linen shirting fabric used in making men’s formal shirt. Maximum seam efficiency and interaction between the process parameters were assessed. Design/methodology/approach The classical method of optimisation involves varying one variable at a time and keeping the others constant. This is often useful, but it does not explain the effect of interaction between the variables under consideration. In this study, the response surface methodology was used for securing a more accurate optimisation of seam quality (seam efficiency) of woven linen shirting fabric. The response surface method is an empirical statistical technique used for multiple regression analysis of quantitative data obtained from statistically designed experiments by solving the multivariate equations simultaneously. Through this system, the input level of each process parameter, i.e. variable and the level of the selected response (seam efficiency), can be quantified. The central composite, Box–Behnken, is the common design used here. Findings The maximum seam efficiency is 79.62 per cent and 83.13 per cent in warp and weft direction, respectively, with optimum areal density (G) of 110 g/m2 of woven linen shirting fabric. The most suitable stitch density and ticket number of commercial grade sewing thread for woven linen shirting fabric are 13-13.5 and 40, respectively. Practical implications This study could help apparel manufacturers to evaluate seam quality, i.e. seam efficiency of woven linen fabric for men’s shirting, more effectively from the proposed regression model. The optimisation of the commercial grade sewing thread size and stitch density used in this study for woven linen shirting fabric within the range of 110-150 g/m2 will facilitate apparel engineers in production planning and quality control. Originality/value There is dearth of research on seam quality for woven linen shirting fabric using commercial grade sewing thread and engineering of prediction regression model for the estimation of seam efficiency by using process parameters, namely, fabric G, thread size and thread density and their interaction.

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Vaseem Chavhan ◽  
M. Ramesh Naidu ◽  
Hayavadana Jamakhandi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Regression Models ◽  
Network Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Content Type ◽  
Sewing Thread ◽  
Artificial Neural

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

Study on fabric compression under seam vis a vis quantitative measurement of seam boldness and thread consumption for lockstitch 301

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Md Vaseem Chavhan ◽  
Mandapati Ramesh Naidu
Keyword(s):  
Quantitative Measurement ◽  
Multilinear Regression ◽  
New Approach ◽  
Content Type ◽  
Single View ◽  
Sewing Thread ◽  
Stitch Density ◽  
Proposed Model ◽  
Fabric Weight ◽  
Single View Metrology

Purpose This paper aims to develop at sewing thread during the seam formation may lead to the compression of fabric under seam. In the present study, the model has been proposed to predict the seam compression and calculation of seam boldness, as well as thread consumption by considering seam compression. Design/methodology/approach The effect of sewing parameters on the fabric compression at the seam (Cf) for fabrics of varying bulk density was studied by the Taguchi method and also the multilinear regression equation is obtained to predict seam compression by considering these parameters. The framework has been set as per the single view metrology approach to measuring structural seam boldness (Bs). One of the basic geometrical models (Ghosh and Chavhan, 2014) for the prediction of thread consumption at lock stitch has been modified by considering fabric compression at the seam (Cf). Findings The multilinear regression model has been proposed which can predict the compression under seam using easily measurable fabric parameters and stitch density. The seam boldness is successfully calculated quantitatively using the proposed formula with a good correlation with the seam boldness rated subjectively. The thread consumption estimation from the proposed approach was found to be more accurate. Originality/value The compression under seam is found out using easily measurable parameters; fabric thickness, fabric weight and stitch density from the proposed model. The attempt has been made to calculate seam boldness quantitatively and the new approach to find out thread consumption by considering the seam compression has been proposed.

scholarly journals Optimisation of selective laser melting for a high temperature Ni-superalloy

2015 ◽  
Vol 21 (4) ◽  
pp. 423-432 ◽  
Author(s):  
Luke N. Carter ◽  
Khamis Essa ◽  
Moataz M Attallah
Keyword(s):  
High Temperature ◽  
Response Surface ◽  
Selective Laser Melting ◽  
Process Parameters ◽  
Laser Power ◽  
Surface Model ◽  
Good Prediction ◽  
Laser Melting ◽  
Content Type ◽  
Scan Speed

Purpose – The purpose of this paper is to optimise the selective laser melting (SLM) process parameters for CMSX486 to produce a “void free” (fully consolidated) material, whilst reducing the cracking density to a minimum providing the best possible fabricated material for further post-processing. SLM of high temperature nickel base superalloys has had limited success due to the susceptibly of the material to solidification and reheat cracking. Design/methodology/approach – Samples of CMSX486 were fabricated by SLM. Statistical design of experiments (DOE) using the response surface method was used to generate an experimental design and investigate the influence of the key process parameters (laser power, scan speed, scan spacing and island size). A stereological technique was used to quantify the internal defects within the material, providing two measured responses: cracking density and void per cent. Findings – The analysis of variance (ANOVA) was used to determine the most significant process parameters and showed that laser power, scan speed and the interaction between the two are significant parameters when considering the cracking density. Laser power, scan speed, scan spacing and the interaction between power and speed, and speed and spacing were the significant factors when considering void per cent. The optimum setting of the process parameters that lead to minimum cracking density and void per cent was obtained. It was shown that the nominal energy density can be used to identify a threshold for the elimination of large voids; however, it does not correlate well to the formation of cracks within the material. To validate the statistical approach, samples were produced using the predicted optimum parameters in an attempt to validate the response surface model. The model showed good prediction of the void per cent; however, the cracking results showed a greater deviation from the predicted value. Originality/value – This is the first ever study on SLM of CMSX486. The paper shows that provided that the process parameters are optimised, SLM has the potential to provide a low-cost route for the small batch production of high temperature aerospace components.

scholarly journals Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi-Based Response Surface Methodology

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Madhanagopal Manoharan ◽  
Arul Kulandaivel ◽  
Adinarayanan Arunagiri ◽  
Mohamad Reda A. Refaai ◽  
Simon Yishak ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Process Parameter ◽  
Depth Of Cut ◽  
Input Process ◽  
Coated Tools ◽  
Grey Taguchi

Milling is the surface machining process by removing material from the raw stock using revolving cutters. This process accounts for a major stake in most of the Original Equipment Manufacturing (OEM) industries. This paper discusses optimizing process parameters for machining the AA 2014 T 651 using a vertical milling machine with coated cutting tools. The process parameters such as cutting speed, depth of cut, and type of the cutting tool with all its levels are identified from the previous literature study and several trial experiments. The Taguchi L9 Orthogonal Array (OA) is used for the experimental order with the chosen input parameters. The commonly used cutting tools in the machining industry, such as High-Speed Steel (HSS) and its coated tools, are considered in this study. These tools are coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN) by Physical Vapor Deposition (PVD) technique. The output responses such as cutting forces along the three-axis are measured using a milling tool dynamometer for the corresponding input factors. The input process parameters are optimized by considering the output responses such as MRR, machining torque, and thrust force. Grey Taguchi-based Response Surface Methodology (GTRSM) is used for multiobjective multiresponse optimization problems to find the optimum input process parameter combination for the desired response. Polynomial regression equations are generated to understand the mathematical relation between the input factor and output responses as well as Grey Relational Grade (GRG) values. The optimum process parameter combination from the desirability analysis is the HSS tool coated with TiAlN at a cutting speed of 270 rpm and a depth of cut value of 0.2 mm.

Micro-hardness prediction of friction stir processed magnesium alloy via response surface methodology

2017 ◽  
Vol 13 (3) ◽  
pp. 377-390 ◽  
Author(s):  
Ahmed Naser ◽  
Basil Darras
Keyword(s):  
Response Surface Methodology ◽  
Magnesium Alloy ◽  
Response Surface ◽  
Process Parameters ◽  
Rotational Speed ◽  
Micro Hardness ◽  
Content Type ◽  
Friction Stir ◽  
Effective Use ◽  
Hardness Values

Purpose The purpose of this paper is to present a model to predict the micro-hardness of friction stir processed (FSPed) AZ31B magnesium alloy using response surface methodology (RSM). Another objective is to identify process parameters and through-thickness position which will give higher micro-hardness values. Moreover, the study aims at defining the factor that exhibits the most effect on the micro-hardness. Friction stir processing (FSP) machine can then be fed with the optimized parameters to achieve desirable properties. Design/methodology/approach An experimental setup was designed to conduct FSP. Several AZ31B magnesium samples were FSPed at different combinations of rotational and translational speeds. The micro-hardness of all the combinations of process parameters was measured at different through-thickness positions. This was followed by an investigation of the three factors on the resulting micro-hardness. RSM was then used to develop a model with three factors and three levels to predict the micro-hardness of FSPed AZ31 magnesium alloy within the covered range. The analyses of variance in addition to experimental verification were both used to validate the model. This was followed by an optimization of the response. Findings The model showed excellent capability of predicting the micro-hardness values as well as the optimum values of the three factors that would result in better micro-hardness. The model was able to capture the effects of rotational speed, translational speed, and through-thickness position. Results suggest that micro-hardness values were mostly sensitive to changes in tool rotational speed. Originality/value FSP is considered to be one of the advanced microstructural modification techniques which is capable of enhancing the mechanical properties of light-weight alloys. However, the lack of accurate models which are capable of predicting the resulted properties from process parameters hinders the widespread utilization of this technique. At the same time, RSM is considered as a vital branch of experimental design due to its ability to develop new processes and optimize their performance. Hence, the developed model is very beneficial and is meant to save time and experimental effort toward effective use of FSP to get the desired/optimum micro-hardness distribution.

Optimization of discharge printing of indigo denim using potassium permanganate via response surface regression

2018 ◽  
Vol 47 (3) ◽  
pp. 228-235 ◽  
Author(s):  
Amna Siddique ◽  
Tanveer Hussain ◽  
Waseem Ibrahim ◽  
Zulfiqar Ali Raza ◽  
Sharjeel Abid
Keyword(s):  
Reaction Time ◽  
Response Surface ◽  
Potassium Permanganate ◽  
Practical Implication ◽  
Statistical Technique ◽  
Significant Loss ◽  
Content Type ◽  
Wet Processing ◽  
Key Variables ◽  
Response Surface Regression

Purpose This paper aims to investigate the feasibility of potassium permanganate (KMnO4) as an efficient discharging agent for indigo-dyed denim fabrics and identification of key variables for its cost-efficient implication. Design/methodology/approach Response surface methodology, which is a statistical technique for the optimization of process variables, was used to study the effect of three key variables, i.e. KMnO4 concentration, printing paste pH and reaction time on whiteness and strength of discharged printed fabric. Regression models were developed to predict response variables, i.e whiteness, tensile strength and tear strength of discharge printed denim. Findings It was found that some captivating discharge printing effects could be produced using appropriate KMnO4 concentration, printing paste pH and reaction time without any significant loss in the fabric strength. Practical implications This study highlights the practical implication of KMnO4 to be used as a safe and effective discharging agent under different conditions and to optimize the parameters using statistical analysis to ensure minimum loss in textile properties. The use of denim has evolved over the decades from a rough and tough workwear to highly fashionable apparel. Various dry and wet processing techniques have been introduced in recent years for the value-addition of denim – discharge printing is one of them. As lab to bulk reproducibility requires some sort of experience and adjustments in main parameters, the practical feasibility on the bulk scale should be adjusted in advance by means of the lab scale experimentation. Originality/value The KMnO4 oxidation process is considered eco-friendly because manganese dioxide, which is formed when permanganate is reduced, can be recycled. Thus, the use of KMnO4 can be considered as an eco-friendly safe process for the discharging of indigo dyes.

scholarly journals Multivariate Quadratic Nonlinear Regression Model of the Ultimate Pull-Out Load of Electrohydraulic Expansion Joints Based on Response Surface Methodology

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 689
Author(s):  
Da Cai ◽  
Chenyu Jin ◽  
Jie Liang ◽  
Guangyao Li ◽  
Junjia Cui
Keyword(s):  
Response Surface Methodology ◽  
Regression Model ◽  
Response Surface ◽  
Process Parameters ◽  
Nonlinear Regression ◽  
Discharge Voltage ◽  
Wire Diameter ◽  
Nonlinear Regression Model ◽  
Wire Length ◽  
Pull Out

Electrohydraulic expansion joining has great potential for joining the light weight and high strength thin-walled pipes due to its high strain rate. Based on the central composite design (CCD) of response surface methodology, multiple experiments of electrohydraulic expansion joining process were performed. The multivariate quadratic nonlinear regression model between process parameters (discharge voltage, wire length, and wire diameter) and the ultimate pull-out load of the joints was established. The results revealed that discharge voltage, wire length and wire diameter all had a significant effect on the ultimate pull-out load. The discharge voltage had the most significant effect. The interaction between the discharge voltage and the wire diameter had a significant effect on the ultimate pull-out load. The optimal parameter combination (discharge voltage = 6 kV, wire length = 10 mm, wire diameter = 0.833 mm) was obtained and verified through the experiments. This study would provide guidance for the choice of the process parameters in real applications.

Predicting sewing thread consumption for lockstitch using regression model

2016 ◽  
Vol 20 (3) ◽  
pp. 155-163 ◽  
Author(s):  
VIinay Kumar Midha ◽  
Shailja Sharma ◽  
Vaibhav Gupta
Keyword(s):  
Regression Model ◽  
Regression Equation ◽  
Coefficient Of Determination ◽  
Stock Management ◽  
Content Type ◽  
Sewing Thread ◽  
Different Types ◽  
Sewing Threads ◽  
Cotton Threads ◽  
Single Regression

Purpose This paper aims to develop a single regression model (instead of developing models separately for each thread type) to predict the sewing thread consumption for cotton and polyester staple spun threads. Design/methodology/approach A single regression model is developed for predicting sewing thread consumption for cotton and polyester threads. The polyester sewing threads have lower sewing thread consumption as compared to cotton threads because of their higher elongation behaviour. The model differentiates between the cotton and polyester sewing threads using their elongation values at peak levels of tensions experienced by the sewing threads during stitch tightening. By comparing the estimated thread consumption values with actual values, the effectiveness of model is evaluated with root mean square error and coefficient of determination (R2). Findings During the sewing process, by understanding the behaviour of different types of sewing threads, it is possible to develop a single regression model for all types of threads. Practical implications The sewing thread consumption can be easily calculated for cotton and polyester sewing threads using a single regression equation using the sewing assembly thickness, stitch density and elongation of thread at peak tension. The garment manufacturers need not depend on different charts for sewing thread consumption for stock management. Originality/value The sewing thread consumption is different for different types of threads, and garment manufacturers have to depend on different charts given by sewing thread manufacturers or use different equations for each type of threads. Using this single regression equation, sewing thread consumption for cotton and polyester sewing thread can be estimated accurately.

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