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The purpose of this paper is to predict the bagging recovery velocity of bagged denim fabric samples. Hence, the authors attempt to carry out a model highlighting and explaining the impact of some considered frictional parameters such as yarn-to-yarn friction expressed as weft yarn rigidity parameter and metal-to-fabric friction expressed by mean frictional coefficient parameter.

The statistical analysis steps were implemented using experimental design type Taguchi and thanks to Minitab 14 software. The modeling methodology analyzed in this paper deals with the linear regression method application and analysis. The predictive power of the obtained model is evaluated by comparing the estimated recovery velocity (theoretical) with the actual values. These comparative values are measured after the bagging test and during the relaxation time of the denim fabric samples. The regression coefficient (R2) values as well as the statistical tests (p-values, analysis of variance results) were investigated, discussed and analyzed to improve the findings.

According to the statistical results given by Taguchi analysis findings, the regression model is very significant (p-regression=0.04 and R2=97 percent) which explains widely the possibility of bagging behavior prediction in the studied experimental field of interest. Indeed the variation (the increase or the decrease) of the frictional input parameters values caused, as a result, the variation of the whole appearance and the shape of the bagged zone expressed by the residual bagging height variations. In spite of their similar compositions and characteristics, the woven bagged fabrics presented differently behaviors in terms of the bagging recovery and kinetic velocity values. After relaxation times which are not the same and relative to different fabric samples, it may be concluded that bagging behavior remained function of the internal frictional stresses, especially yarn-to-yarn and metal-to-fabric ones.

This study is interesting for denim consumers and industrial applications during long and repetitive uses. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. In fact, in terms of the importance to the industrial producers of the materials it helps to provide a first step in an attempt for a better understanding of the stresses involved in bagging of woven fabrics in general and denim fabrics particularly due to important frictional input contributions. They provide the basis for the development of fabrics that can withstand bagging problems. This research may also put forward improved methods of measuring bagginess as function of frictional parameters in order to optimize (minimize) their effects on the bagging behaviors before and after repetitive uses. These experimental, statistical and theoretical findings may be used to predict bagginess of fabrics based on their properties and prevent industrial from the most significant and influential inputs which should be adjusted accurately. This work allows industrial, also, to make more attention, in case of a high-quality level to ensure, to optimize and review yarn behaviors used to produce fabrics against drastic solicitations and minimize frictions forms during experimental spinning and weaving processes.

Until now, there is no sufficient information to evaluate and predict the effect of the yarn-to-yarn friction as well as metal-to-yarn one on the residual bagging behavior. Besides, there is no work that deals with the kinetic recovery evolution as function of frictional inputs to explain accurately the bagging behavior evolution during relaxation time. Therefore, this present work is to investigate and model the residual bagging recovery velocity after bagging test as function of the frictional input parameters of both denim yarn and fabric samples (expressed by the friction caused due to contact from conformator to fabric).

To investigate the relation of in‐plane fabric tensile properties with woven fabrics bagging behavior, a new test method was developed and a real time data acquisition and strain gauge technique were used. The bagging procedure was carried out while the woven fabric tensile deformations along warp and weft directions were measured. The fabric bagging behavior was characterized by bagging resistance, bagging fatigue, residual bagging height and residual bagging hysteresis. The experimental results show that the bagging load, work, hysteresis, residual hysteresis and fatigue are highly linearly correlated with corresponding parameters in warp and weft directions. An empirical relationship obtained between residual bagging height and bagging fatigue and resistance (R²=0.83) suggests that the proposed new test method is able to evaluate bagging behavior of fabrics.

The purpose of this paper is to optimize the frictional input parameters related to the yarn and woven fabric samples. Indeed, using metaheuristic techniques for optimization, it helps to attempt the best quality appearance of garment, by analysing their effects and relationships with the bagging behaviour of tested fabrics before and after bagging test. Using metaheuristic techniques allows us to select widely the minimal residual bagging properties and the optimized inputs to adjust them for this goal.

The metaheuristic methods were applied and discussed. Hence, the genetic algorithms (GA) and ant colony optimization (ACO) technique results are compared to select the best residual bagging behaviour and their correspondent parameters. The statistical analysis steps were implemented using Taguchi experimental design thanks to Minitab 14 software. The modelling methodology analysed in this paper deals with the linear regression method application and analysis to prepare to the optimization steps.

The regression results are essential for evaluate the effectiveness of the relationships founded between inputs and outputs parameters and for their optimizations in the design of interest.

This study is interesting for denim consumers and industrial applications during long and repetitive uses. Undoubtedly, the denim garments remained the largely used and consumed, hence, this particularity proves the necessity to study it in order to optimize the bagging phenomenon which occurs as function of number of uses. Although it is fashionable to have bagging, the denim fabric remains, in contrast with the worsted ones, the most popular fabric to produce garments. Moreover, regarding this characteristic, the large uses and the acceptable value of denim fabrics, their aesthetic appearance behaviour due to bagging phenomenon can be analysed and optimized accurately because compared to worsted fabrics, they have a high value and the repetitive tests to investigate widely bagged zones can fall the industrial. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. This can help to understand why residual bagging behaviour remained after garment uses due to the internal stress and excessive extensions.

Until now, there is no work dealing with the optimization of bagging behaviour using metaheuristic techniques. Indeed, all investigations are focused on the evaluation and theoretical modelling based on the multi linear regression analysis. It is notable that the metaheuristic techniques such as ACO and GA are used to optimize some difficult problems but not yet in the textile field excepting some studies using the GA. Besides, there is no sufficiently information to evaluate, predict and optimize the effect of the yarn-to-yarn friction as well as metal-to-yarn one on the residual bagging behaviour. Several and different denim fabrics within their different characteristics are investigated to widen the experimental analysis and thus to generalize the results in the experimental design of interest.

The purpose of this paper is to predict the appearance of denim fabric after repetitive uses judging the denim cloth behavior and performance in viewpoint of bagging ability. Hence, it attempts to carry out the significant inputs and outputs that have an influence on the bagging behaviors using the Principal Component Analysis (PCA) technique. In this study, the Kawabata Evaluation System parameters such as the frictional characteristics, the bending, compression, tensile and shear parameters are investigated to propose a model highlighting and explaining their impacts on the different bagging properties. To improve the obtained results, the selected significant inputs are also analyzed within their bagging properties using Taguchi experimental design. The linear regressive models prove the effectiveness of the PCA method and the obtained findings.

To investigate the mechanical properties and their contributions on the bagging characteristics, some denim fabrics were collected and measured thanks to the Kawabata evaluation systems (KES-FB1, KES-FB2, KES-FB3 and KES-FB4). These bagging properties were further analyzed applying the method of PCA to acquire factor patterns that indicate the most important fabric properties for characterizing the bagging behaviors of different studied denim fabric samples. An experimental design type Taguchi was, hence, applied to improve the results. Regarding the obtained results, it may be concluded that the PCA method remained a powerful and flawless technique to select the main influential inputs and significant outputs, able to define objectively the bagging phenomenon and which should be considered from the next researches.

According to the results, there are good relationships between the Kawabata input parameters and the analyzed bagging properties of studied denim fabrics. Indeed, thanks to the PCA, it is probably easy to reduce the number of the influent parameters for three reasons. First, applying this technique of selection can help to select objectively the most influential inputs which affect enormously the bagged fabrics. Second, knowing these significant parameters, the prediction of denim fabric bagging seems fruitful and can undoubtedly help researchers explain widely this complex phenomenon. Third, regarding the findings mentioned, it seems that the prevention of this aesthetic phenomenon appearing in some specific zones of denim fabrics will be more and more accurate.

This study is interesting for denim consumers and industrial applications during long and repetitive uses. Undoubtedly, the denim garments remained the largely used and consumed, hence, this particularity proves the necessity to study it in order to evaluate the bagging phenomenon which occurs as function of number of uses. Although it is fashionable to have bagging, the denim fabric remains, in contrast with the worsted ones, the most popular fabric to produce garments. Moreover, regarding this characteristic, the large uses and the acceptable value of denim fabrics, their aesthetic appearance behavior due to bagging phenomenon can be analyzed accurately because compared to worsted fabrics, they have a high value and the repetitive tests to investigate widely bagged zones may fall the industrial. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. This can help understanding why residual bagging behavior remained after garment uses due to the internal stress and excessive extensions. Regarding the selected influential inputs and outputs relative to bagging behaviors, there are some practical implications that have an impact on the industrial and researchers to study objectively the occurrence of this aesthetic phenomenon. Indeed, this study discusses the significance of the overall inputs; their contributions on the denim fabric bagged zones aims to prevent their ability to appear after uses. Moreover, the results obtained regarding the fabric mechanical properties can be useful to fabric and garment producers, designers and consumers in specifying and categorizing denim fabric products, insuring more denim cloth use and controlling fabric value. For applications where the subjective view of the consumer is of primary importance, the KES-FB system yields data that can be used for evaluating fabric properties objectively and prejudge the consumer satisfaction in viewpoint of the bagging ability. Therefore, this study shows that by measuring shear, tensile and frictional parameters of KES-FB, it may be possible to evaluate bagging properties. However, it highlights the importance and the significance of some inputs considered influential or the contrast (non-significant) in other researches.

This work presents the first study analyzing the bagged denim fabric applying the PCA technique to remove the all input parameters which are not significant. Besides, it deals with the relationship developed between the mechanical fabric properties (tensile, shear and frictional stresses) and the bagging properties behavior. To improve these obtained relationships, for the first time, the regression technique and experimental design type Taguchi analysis were both applied. Moreover, it is notable to mention that the originality of this study is to let researchers and industrials investigate the most influential inputs only which have a bearing on the bagging phenomenon.

This paper aims to develop a new slicing method for fused deposition modelling (FDM), the curved layer adaptive slicing (CLAS), combining adaptive flat layer and curved layer slicing together.

This research begins with a review of current curved layer and adaptive slicing algorithms employed in the FDM and further improvement of the same, where possible. The two approaches are then integrated to develop the adaptive curved layer slicing based on the three-plane intersection method for curved layer offsetting and consideration of facet angles together with the residual heights for adaptive slicing. A practical implementation showed that curved layer adaptive layers respond in similar lines to the flat layer counterparts in terms of the mechanical behaviour of FDM parts.

CLAS is effective in capturing sharply varying surface profiles and other finer part details, apart from imparting fibre continuity. Three-point bending tests on light curved parts made of curved layers of varying thicknesses prove thicker curved layers to result in better mechanical properties.

The algorithms developed in this research can handle relatively simple shapes to develop adaptive curved slices, but further developments are necessary for more complex shapes. The test facilities also need further improvements, to be able to programmatically implement adaptive curved layer slicing over a wide range of thicknesses.

When fully developed and implemented, CLAS will allow for better FDM part construction with lesser build times.

This research fills a gap in terms of integrating both curved layer and adaptive slicing techniques to better slice and build a part of given geometry using FDM.

This paper aims to present a multi-material additive manufacturing (AM) process with a newly developed curing-on-demand method to fabricate a three-dimensional (3D) object with multiple material compositions.

Unlike the deposition-on-demand printing method, the proposed curing-on-demand printheads use a digital light processing (DLP) projector to selectively cure a thin layer of liquid photocurable resin and then clean the residual uncured material effectively using a vacuuming and post-curing device. Each printhead can individually fabricate one type of material using digitally controlled mask image patterns. The proposed AM process can accurately deposit multiple materials in each layer by combining multiple curing-on-demand printheads together. Consequently, a three-dimensional object can be fabricated layer-by-layer using the developed curing-on-demand printing method.

Effective cleaning of uncured resin is realized with reduced coated resin whose height is in the sub-millimeter level and improved vacuum cleaning performance with the uncleaned resin less than 10 µm thick. Also, fast material swapping is achieved using the compact design of multiple printheads.

The proposed multi-material stereolithography (SL) process enables 3D printing components using more viscous materials and can achieve desired manufacturing characteristics, including high feature resolution, fast fabrication speed and low machine cost.

The purpose of this paper is to develop a simple analytical model for predicting the through-thickness distribution of residual stresses in a cold spray (CS) deposit-substrate assembly.

Layer-by-layer build-up of residual stresses induced by both the peening dominant and thermal mismatch dominant CS processes, taking into account the force and moment equilibrium requirements. The proposed model has been validated with the neutron diffraction measurements, taken from the published literature for different combinations of deposit-substrate assemblies comprising Cu, Mg, Ti, Al and Al alloys.

Through a parametric study, the influence of geometrical variables (number of layers, substrate height and individual layer height) on the through-thickness residual stress distribution and magnitude are elucidated. Both the number of deposited layers and substrate height affect residual stress magnitude, whereas the individual layer height has little effect. A good agreement has been achieved between the experimentally measured stress distributions and predictions by the proposed model.

The proposed model provides a more thorough explanation of residual stress development mechanisms by the CS process along with mathematical representation. Comparing to existing analytical and finite element methods, it provides a quicker estimation of the residual stress distribution and magnitude. This paper provides comparisons and contrast of the two different residual stress mechanisms: the peening dominant and the thermal mismatch dominant. The proposed model allows parametric studies of geometric variables, and can potentially contribute to CS process optimisation aiming at residual stress control.

This work deals with the evolution of the residual bagging height of knitted samples. In comparing the results after a fabric bagging test, it may be concluded that the behaviour of the sample length is an influential parameter which widely reflects the anisotropy of knitted structures. Hence, it is clear that the sample length does not exhibit the same behaviour in each knitted fabric zone which generally explains the impartial response after stress is applied. With regards to the different height values that the sample length presents in each measured part of the fabric, it may be concluded that there are several types of behaviours in the areas of bagging along the sample length. Moreover, it appears that there is a non uniform distribution of deformation after removing the stress. Therefore, internal stresses and deformations that cause different residual heights in the same sample accurately reflect and explain the anisotropic structure of the investigated knitted fabrics. In knowing that there is this non-uniform distribution of deformation, the input parameters also have considerable effects on the bending behaviour of the residual bagging. Indeed, when the yarn structure is changed, the residual bagging height changes too. Furthermore, our findings prove that elastic knitted fabrics accurately show a more minimal residual bagging height as opposed to non elastic fabrics in spite of the other input parameter values.

Since columns are critical structural elements, they shall withstand hazards without any considerable damage. In the case of a fire, although concrete has low thermal conductivity compared to other construction materials, its properties are changed at elevated temperatures. Most critically, the residual compressive strengths of reinforced concrete columns are significantly reduced after fire exposure. Validation of the worthiness of rehabilitating concrete structures after fire exposure is highly dependent on accurately determining the residual strengths of fire-damaged essential structural elements such as columns.

In this study, eight reinforced-concrete columns (200 × 200 × 1,500 mm) that were experimentally examined in a prior related study have been numerically modelled using ABAQUS software to investigate their residual compressive strengths after exposure to different durations of standard fire (i.e. one and two hours) while subjected to different applied load ratios (i.e. 20 and 40% of the compressive resistance of the column). Outcomes of the numerical simulations were verified against the prior study's experimental results.

In a subsequent phase, the results of a parametric study that has been completed as part of the current study to investigate the effects of the applied load ratios show that the application of axial load up to 80% of the compressive resistance of the column did not considerably influence the residual compressive strength of the shorter columns (i.e. 1,500 and 2,000-mm high). However, increasing the height of the column to 2,500 or 3,000 mm considerably reduced the residual compressive strength when the load ratio applied on the columns exceeded 60 and 40%, respectively. Also, when the different columns were simulated under two-hour standard fire exposure, the dominant failure was buckling rather than concrete crushing which was the typical failure mode in most columns.

The outcomes of the numerical study presented in this paper reflect the residual compressive strength of RC columns subjected to various applied load ratios and standard fire durations. Also, the parametric study conducted as part of this research on the effects of higher load ratios and greater column heights on the residual compressive strength of the fire-damaged columns is practical and efficient. The developed computer models can be beneficial to assist engineers in assessing the validity of rehabilitating concrete structures after being exposed to fire.

This paper presents an investigation into residual stresses in selective laser sintering (SLS) and selective laser melting (SLM), aiming at a better understanding of this phenomenon.

First, the origin of residual stresses is explored and a simple theoretical model is developed to predict residual stress distributions. Next, experimental methods are used to measure the residual stress profiles in a set of test samples produced with different process parameters.

Residual stresses are found to be very large in SLM parts. In general, the residual stress profile consists of two zones of large tensile stresses at the top and bottom of the part, and a large zone of intermediate compressive stress in between. The most important parameters determining the magnitude and shape of the residual stress profiles are the material properties, the sample and substrate height, the laser scanning strategy and the heating conditions.

All experiments were conducted on parts produced from stainless steel powder (316L) and quantitative results cannot be simply extrapolated to other materials. However, most qualitative results can still be generalized.

This paper can serve as an aid in understanding the importance of residual stresses in SLS/SLM and other additive manufacturing processes involving a localized heat input. Some of the conclusions can be used to avoid problems associated with residual stresses.