Is Tussar Silk Inferior to Mulberry Silk ?

In a paper entitled  "Study of property and structural variants of mulberry and Tussar silk filaments" by professor Mohan Gulrajani, one can get several hints which may lead to the answer to the question.

"A glance at the typical tensile behaviour reveals that the stress-strain curve of these two varieties is distinctly different, in that tasar shows a clear yield point and very high elongation compared to the mulberry filament."

Conclusion 1:  Tussar silk can undergo significant stretching before permanently deforming.

The tusar silk stress-strain curve exhibits a clear yield point. A yield point is a point on the stress-strain curve where the material transitions from elastic deformation (where it returns to its original shape after the force is removed) to plastic deformation (where it retains some deformation even after the force is removed). This suggests that Tussar silk can undergo significant stretching before permanently deforming. 

Conclusion 2:  Tussar can stretch a lot before reaching its breaking point compared to mulberry silk.

The stress-strain curve of tussar silk also shows very high elongation compared to mulberry silk. Elongation refers to how much a material stretches before breaking. The fact that tussar silk exhibits high elongation means it can stretch a lot before reaching its breaking point compared to mulberry silk.

In contrast, mulberry silk does not show as pronounced a yield point and has lower elongation compared to tussar silk. This implies that mulberry silk is less flexible and may have a more limited ability to stretch before breaking compared to tasar silk.

Why there is a difference in their properties

One answer can  be density.  The density of mulberry is higher ( 1.35 g/cc) as compared to tussar ( 1.30 g/cc). This suggests a relatively poor degree of orientation and less order in Tussar, which gives to lower modulus and elongation behavior of tussar.

These values have their commercial and functional implications. 

Can Silk be Machine Washed

At least a study suggests so. 

A paper titled "Study of property and structural variants of mulberry and Tussar silk filaments" by professor Mohan Gulrajani has suggested this idea. 

Earlier research suggested that the wet strength of silk specially Mulberry reduces considerably when subjected to water during laundering. This happens because in an aqueous environment, the hydrogen bonds between the molecules break. These bonds are crucial for maintaining the structure and strength of the fibers.

However the paper suggests that " silks can be machine washed at 40-60ºC provided one uses appropriate washing procedures, such as the use of neutral detergents".

The results for both Tussar and Mulberry found that " the tenacity and elongation at break are not

significantly different in dry or wet state ". However there is slight decrease in modulus. The figure given below talks about the result. 

A reduction in modulus would make the fiber less stiff.

Modulus, specifically in the context of materials science, refers to the measure of a material's stiffness or rigidity. It indicates the ability of a material to resist deformation under an applied force. Modulus is typically expressed in terms of stress divided by strain, where stress is the force applied per unit area, and strain is the resulting deformation.

When the modulus of a material decreases, it means that the material becomes less resistant to deformation for a given stress. In other words, it becomes more flexible or less stiff. Conversely, an increase in modulus would indicate that the material becomes stiffer or more resistant to deformation.

Then why it is not advised not to launder Pure silk sarees ?

The answer lies in the properties of commercially available silk fabrics or sarees. The above study was done after fully degumming the yarn. However, in commercially available silk fabric, the yarn is not fully degummed, there is always a residual gum or sericin. In the study about 20% sericin was found in mulberry and 5% in tussar.  On wetting, the sericin weakens, and allows inter filament slippage, which in turn leads to a drastic reduction in mechanical properties. Hence the strength of the wet silk gets reduced. 

What is sericin, what is silk fiber composed of ?

Silk fiber is primarily composed of two main proteins: fibroin and sericin. These proteins are produced by specialized glands in the silk-producing organisms, such as silkworms (Bombyx mori). The composition of silk fiber can vary depending on factors such as the species of the silk-producing organism and the conditions under which the silk is produced.

Fibroin: Fibroin is the structural protein that forms the core of silk fibers. It constitutes the majority of the silk fiber's mass and is responsible for its strength and resilience. Fibroin is a fibrous protein composed mainly of amino acids such as glycine, alanine, and serine. The exact composition and arrangement of amino acids within fibroin contribute to its unique mechanical properties, including its tensile strength and elasticity.

Sericin: Sericin is a glue-like protein that surrounds and binds the fibroin filaments together within the silk cocoon. It serves to protect the fibroin and provide cohesion to the silk fiber structure. Sericin is composed of various proteins and amino acids, with its composition varying depending on factors such as the silk-producing species. Sericin is typically removed from silk fibers during processing to improve their texture and appearance, leaving behind only the fibroin core.

In addition to proteins, silk fiber may also contain small amounts of other substances such as lipids, sugars, and minerals. These minor components can influence the properties of silk fibers but are present in much smaller quantities compared to fibroin and sericin.

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Some Notes about Arani Sarees

 Source

1. Until 1995, only small motifs were created using 'Adai' or dobbies. Now bigger motifs with Jacquards are also in vogue.

2. Arani is located in the Tiruvannamalai district of Tamil Nadu.

3. In Tamil, Aru means river and Ani means adorning. Arani means a place made beautiful by rivers.

4. In Arani, still street sizing is practiced

5. These sarees are characterized by Korvai and Thazhampoo Rekku on the borders. 

In Hindi, "Thazhambu flower" is known as "केवड़ा फूल" (Kewda Phool). Kewda is a type of fragrant flower commonly used in perfumes, culinary preparations, and religious rituals in India. It is also known as Pandanus flower in English.

In the context of sarees, "ரேக்கு" (rekku) typically refers to the decorative borders or edges of the saree. These borders are often woven or embroidered onto the saree fabric and can vary in width and design. The term "rekku" is used to describe these intricate patterns or embellishments that adorn the edges of the saree, enhancing its beauty and elegance.

Thazhambu Flower

Thazhampoo Rekku

6. Both Frame looms and pit looms are used to weave the sarees. 

7. Arani weavers are mostly composed of Saurashtrians from Gujarat who came during the Vijayanagara Period. 

8. Arni Dobby sarees are lightweight and made with single color yarn using a fly shuttle. 

9. This region also produces Kumbakonam korvai Sarees

Kumbakonam Sarees

10. Arani Kottadi ( Checked pattern is very Popular)

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Automatic Fabric Defect Detection: New Approaches

The blog post is based on the this article:

In the ever-evolving landscape of textile manufacturing, maintaining the highest fabric quality is paramount. Traditionally, defect inspection has relied on human visual scrutiny, often employing semi-automated methods. However, this approach is labor-intensive and costly, prompting the need for more efficient and cost-effective solutions. Enter automatic inspection systems for defect detection, leveraging cutting-edge technologies like artificial neural networks, threshold segmentation, structural, statistical, and model-based approaches, as well as computer vision methods. This article explores the various methods employed in automatic fabric defect detection and their impact on revolutionizing fabric quality control.

The Need for Automation in Fabric Inspection

Fabric defects can range from irregularities in the weave to discolorations and tears. Detecting these imperfections manually is not only time-consuming but also prone to human error. Automatic fabric inspection systems aim to streamline this process, offering a more efficient and reliable solution. The primary goals include reducing time and cost wastage associated with defects, ensuring consistent quality, and meeting the ever-growing demands of the textile industry.

Methodologies in Automatic Defect Detection

1. Artificial Neural Networks (ANNs): Artificial Neural Networks have gained prominence in various fields, including fabric defect detection. ANNs mimic the human brain's structure, allowing them to learn and adapt to patterns. In fabric inspection, ANNs analyze large datasets of fabric images to identify and classify defects. The advantage lies in their ability to recognize complex patterns, making them effective in distinguishing subtle fabric irregularities.

2. Threshold Segmentation: Threshold segmentation involves setting a threshold value to distinguish between defective and non-defective areas of the fabric. This method relies on pixel intensity, where variations beyond a certain threshold are classified as defects. While threshold segmentation is simpler compared to neural networks, it proves effective in detecting visible defects and is computationally less intensive.

3. Structural and Statistical Approaches: Structural and statistical methods involve analyzing the fabric's structural features and statistical properties to identify defects. This may include analyzing the texture, thread density, and overall fabric composition. These methods offer a robust solution for defect detection, especially when combined with other approaches, providing a more comprehensive inspection.

4. Model-Based Approaches: Model-based approaches involve creating mathematical models of defect-free fabric, enabling the system to detect deviations from the established norm. This method is highly adaptable and effective in identifying both subtle and prominent defects. However, it requires precise modeling and may be more computationally demanding.

5. Computer Vision with Multi-Layer Neural Networks: Integrating computer vision with multi-layer neural networks represents a sophisticated approach to fabric defect detection. This method combines the strengths of computer vision for image processing and neural networks for pattern recognition. The result is a powerful system capable of accurately identifying and classifying various defects with high precision.

Empirical Outcomes and Benefits

Empirical evidence suggests that visualized approaches to fabric defect detection offer several key benefits. These include:

1. High Analyzing Speed: Automatic fabric inspection systems exhibit remarkable speed in analyzing fabric for defects. This accelerated pace enhances production efficiency and allows manufacturers to meet tight deadlines without compromising on quality.

2. Easy Utilization: The user-friendly nature of these systems ensures easy integration into existing manufacturing processes. Minimal training is required for operators to navigate and manage the automatic inspection systems effectively.

3. Noise Immunity: Automatic defect detection systems are less susceptible to noise and external factors that may affect manual inspections. This ensures a more reliable and consistent evaluation of fabric quality, leading to a reduction in false positives and negatives.

4. Meeting Requirements for Automatic Fabric Defect Inspection: Automatic fabric inspection systems effectively meet the stringent requirements of the textile industry. The combination of accuracy, speed, and ease of use positions these systems as essential tools for ensuring high-quality fabric production.

In conclusion, the integration of automatic fabric inspection systems represents a significant leap forward in fabric quality control. The diverse methodologies, ranging from artificial neural networks to model-based approaches, showcase the versatility of these systems in identifying defects with precision and efficiency. The empirical outcomes highlight the benefits of adopting such technology, including increased analyzing speed, ease of utilization, noise immunity, and meeting the industry's stringent requirements. As the textile industry continues to evolve, embracing these innovative solutions will undoubtedly play a pivotal role in enhancing overall fabric quality and production efficiency.

Case Studies

1. This study utilizes Fast Fourier Transform and Cross-correlation techniques for spatial domain analysis, followed by a thresholding operation to enhance defect detection accuracy. The approach is validated through simulations on plain fabric, optimizing parameters and considering noise. The proposed vision-based fabric inspection prototype aims for on-loom implementation, ensuring 100% coverage during fabric construction.

2. In this implementation to facilitate accurate inspection, a specialized LED system is employed to illuminate the fabric consistently and evenly. This lighting setup enhances visibility and aids in the precise detection of defects. Additionally, the system incorporates an encoder to measure fabric movement, ensuring synchronized data analysis.

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