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Automation

PRESENTATION BY _ ALINA HASAN RIZVI DEVDATHAN PV NITIN PRANESH

in Sewing Technology

Artificial Intelligence

The application of machinery to the purposes

of sewing is of recent date, yet it has quietly

worked its way into a position of great

importance, not only in the relief that it brings

to thousands of needle-women, but in a

commercial view. The progress has not been

marked with the noise and tumult

characteristic of the application of steam to

machinery, nor so frequently been made the

theme for eloquence with statesmen and

orators, as the steamship, the iron horse and

the steam printing-press, yet it has quietly

stitched its way onward as an agent of

domestic economy, until it now claims a place

side by side with the most important of the

labor-saving inventions.

INTRODUCTION

An additional step for the development of sewing technology was a machine that was invented by Walter Hunter in the year 1832. This worked with a needle with an eye point, as well as a shuttle that guides a second needle. A stitch was created using the needle and the shuttle, a stitch that shares similarities with today's lockstitch. An additional "lockstitch machine" that could, however only create straight seams, was invented by Elias Howe in 1845. This was followed by the invention of the first truly functional sewing machine in 1851, developed by Isaac Merritt Singer. With the increased quantity and diversity of clothing needed today, sewing technology has, of course continued to develop. Sewing machines that create up to 10,000 stitches per minute, or automatic sewing machines that stitch on a pattern according to a predefined computer program, are already part of everyday work.

Latest Innovations Sewing Line Management Systems 3D Printing of Garments/ Fabrics Sewing Robot Automation to the Current Sewing machines

Robotic 3D Sewing Technology

The use of robotic 3D sewing technology can explore new dimensions in sewing as it can create high quality garments. Philipp Moll GmbH & Co. invented a 3D Sewing Technology, which could automatically create 3D Seam. Also, a 3D sewing robotic arm developed in China, the robotic arm can quickly scan the fabric pieceswith a laser scanner and sew them together supported programmed patterns and cut the threads, the whole process takes just a few minutes to complete. The 3D robotic arms are currently applied to the stitching of automotive interiors. 3D sewing technology can make clothing (trousers, jackets, shirts) and car seat covers, airbag fabrics. This 3D technology can help achieve better quality of high-efficiency sewing products. 3D sewing technology also helps reduce labor costs and increase productivity.

Advantages :

Improved Efficieny and cost effective

Improved quality

Ease of Production in Various countries.

Improved Time Management

Disadvantages : Fabrics are Flexible so its hard for full automation Still under development Need Reprogramming for every change in size or style Increase in unemployment

3D Printing of

Garments/

Fabrics

The manufacturing process of 3D knitted fabrics is quite

similar to other 3D objects. Both begin on a computer: CAD

software is used to create the design and ultimately to

obtain a programming language. The digital codes are then

forwarded to the machine, which then begins the

manufacturing process. However, the machine used is where

knitwear and traditional 3D printing diverge.

The primary difference from additive manufacturing is that

the machine is not a 3D printer that processes filament or

powder through extruders, rather, a knitting machine that can

produce a three-dimensional garment in a single pass by

uniting it thread by thread. Consequently, it should be noted

that the principle is essentially the same both are based on

software and are additively manufactured, but the material

creates a big difference between the two methods.

Automated Binding, Button & Button- hole Sewing Machine

A selecting/picking pad was displayed at ITMA 2019 through an associate project by

Spain-based AB Industries. The system uses an easy gripping component and might

simply scoop up work pieces with a 360 degree robotic arm. The technology is

currently under development at AB Industries and isn’t yet commercialized. Sewing is

the most vital textile attaching & joining technology. Various semi-automatic sewing

units are commercially available from several suppliers as well as Japan based

mostly Juki Corporation, Italy based mostly Rimac and Germany based mostly

Duerkopp Adler AG. Juki displayed some automatic sewing machine for button &

buttonholes, whereas Rimac displayed an automatic binding machine for finishing

spherical corners of bedding, floor carpets and several things. Also, the Duerkopp

Adler sewing machine used to sew double welt pocket.

The human eye is a remarkable instrument, but it is fallible. One area of apparel manufacturing where AI improves quality control (QC) is grading yarn and other base materials. Applying artificial intelligence to this area results in cost savings and more precise gradings of the fundamental materials used in apparel manufacturing. In other words, AI can uphold a higher and more consistent standard for materials than humans can alone, thereby raising the average quality of finished garments. subheading 1) improving material grading Machine learning and computer vision have reached a point where they can even discern whether a piece of fruit is bruised beneath its skin. Applications in textile and apparel manufacturing are equally inspiring. Algorithms paired with specialty illumination systems can appraise the condition and salability of newly made and previously worn garments. Measuring the level of transmitted and reflected light lets AI see in a single glance whether the density of a piece of fabric or a finished garment meets current quality standards. 2) Reducing errors in final product inspection

Current inventory Historical and real-time demand Workforce trends and future needs Raw material availability and prices Distribution centres (DCs) are primary sources and beneficiaries of operational data. DC managers have many information sources that can help them optimise their current task load, from historical data on consumer and vendor trends to real-time insights into market fluctuations. Artificial intelligence can turn distribution centres into a nexus of data concerning: 3) Automating data-gathering and asset management