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Photo: Calderdale College
02.08.2023

BTMA: Apprenticeship Training Course for Textile Engineering Technicians in UK

West Yorkshire is to have a first-of-its-kind apprenticeship training course for textile engineering technicians, reflecting a resurgence in the industry locally, and more generally in the UK.

Calderdale College has partnered with the Textile Centre of Excellence (TCoE) and the British Textile Machinery Association (BTMA) to develop the bespoke Level 3 apprenticeship course which will start in September 2023.

Engineering Technician apprentices at Calderdale College will receive training from the TCoE, helping them to develop the engineering maintenance skills required to close the skills gap in West Yorkshire’s textile industry.

While the region has been a flourishing hub for textile excellence since the 19th century and is being revitalised through digitalization and the localisation of supply chains, its success is currently being hindered by an ageing workforce and high staff turnover.

West Yorkshire is to have a first-of-its-kind apprenticeship training course for textile engineering technicians, reflecting a resurgence in the industry locally, and more generally in the UK.

Calderdale College has partnered with the Textile Centre of Excellence (TCoE) and the British Textile Machinery Association (BTMA) to develop the bespoke Level 3 apprenticeship course which will start in September 2023.

Engineering Technician apprentices at Calderdale College will receive training from the TCoE, helping them to develop the engineering maintenance skills required to close the skills gap in West Yorkshire’s textile industry.

While the region has been a flourishing hub for textile excellence since the 19th century and is being revitalised through digitalization and the localisation of supply chains, its success is currently being hindered by an ageing workforce and high staff turnover.

Through adapting the engineering training at Calderdale College to address the current requirements of the textile industry, the unique new course will ensure the passing on of vital know-how and good practice aligned with the new skills demanded by Industry 4.0 and automation.

Collaborative Apprenticeships
Calderdale College has developed the programme over a two-year period through close collaboration with the TCoE and the BTMA, as well as through consultation with British heritage weaver AW Hainsworth and a number of other local textile companies.

The course launch follows on closely from the success of the Collaborative Apprenticeships project launched in 2022 at Calderdale College. To date, this has seen the college engage with over 100 local employers on the benefits of increasing the quantity and improving the quality of the apprenticeships that they offer, as well as encouraging others to introduce apprenticeships for the first time.

“Over the years, we’ve seen how beneficial apprenticeships can be for several sectors, particularly in terms of helping businesses to retain staff and ensuring that they have a steady flow of skilled workers coming in,” said Claire Williams, head of employer engagement at Calderdale College. “Having identified that employers in the textile manufacturing industry were struggling to find apprenticeship training that was designed around their needs, we knew that alongside employers and our partners, we needed to satisfy this critical gap in the market. We hope that this programme will act as a leading example for the rest of the industry to follow.”

Source:

British Textile Machinery Association

(c) BTMA
22.03.2023

BTMA welcomes digital dyeing and finishing company Alchemie

Alchemie Technology is the latest company to join the British Textile Machinery Association (BTMA), as all of the organisation’s members gear up to showcase an array of new innovations at ITMA 2023 in Milan from June 8-14 this year.

Cambridge-headquartered Alchemie is the inventor of two technologies – EndeavourTM and NovaraTM.

The Endeavour digital dyeing system produces no wastewater and reduces water consumption by up to 95% compared to traditional dyeing. The virtually waterless process delivers dyed fabric with high colour consistency and colour fastness and does not require post dyeing washing steps which leads to an energy reduction up to 85%. It can deliver any colour shade required and enables on-demand digital colour changeovers in any run length, from a few metres to several kilometres.

Alchemie Technology is the latest company to join the British Textile Machinery Association (BTMA), as all of the organisation’s members gear up to showcase an array of new innovations at ITMA 2023 in Milan from June 8-14 this year.

Cambridge-headquartered Alchemie is the inventor of two technologies – EndeavourTM and NovaraTM.

The Endeavour digital dyeing system produces no wastewater and reduces water consumption by up to 95% compared to traditional dyeing. The virtually waterless process delivers dyed fabric with high colour consistency and colour fastness and does not require post dyeing washing steps which leads to an energy reduction up to 85%. It can deliver any colour shade required and enables on-demand digital colour changeovers in any run length, from a few metres to several kilometres.

Similar energy savings can be achieved with the Novara precision finishing system which utilises a nozzle array to deliver finishing chemistry with millimetre resolution. Finishing chemistries penetrate deeply into the fabric due to the combination of high velocity liquid jetting and precisely-controlled vacuum and textile finishes are applied only where needed, reducing chemistry usage and enabling multi-functionality.

In the past year, Alchemie, backed by Swedish fashion giant H&M, has established a first demonstration hub at customer JSRTEX in Taiwan. It is now progressing plans to set up further centres at customer sites around the world.

Source:

BTMA / AWOL Media

09.01.2023

Shelton Vision AI: Tailored machine learning solutions for the textiles industry

Over the past three years, a dedicated AI development team at BTMA member Shelton Vision has been developing tailored machine learning solutions for the textiles industry.

The aim has been to elevate the detection process and the accuracy of naming and grading subtle defects in textiles, in real time within production environments.

“Big Data ‘off-the-shelf’ systems such as those behind technolgies like facial recognition and Google Maps involve reading many thousands of single images each second and simply take too long to accumulate sufficient data for what’s required in this specific case,” says Shelton Vision CEO and Managing Director Mark Shelton. “A feature of the textile industry is that in many sectors, the product range changes several times within a year and it is not uncommon to have to inspect hundreds, if not thousands of different styles in a year based on precise settings.”

In terms of defect types, he adds, there may typically be over 100 that need to be accurately detected, classified (named) and graded in real time.

Over the past three years, a dedicated AI development team at BTMA member Shelton Vision has been developing tailored machine learning solutions for the textiles industry.

The aim has been to elevate the detection process and the accuracy of naming and grading subtle defects in textiles, in real time within production environments.

“Big Data ‘off-the-shelf’ systems such as those behind technolgies like facial recognition and Google Maps involve reading many thousands of single images each second and simply take too long to accumulate sufficient data for what’s required in this specific case,” says Shelton Vision CEO and Managing Director Mark Shelton. “A feature of the textile industry is that in many sectors, the product range changes several times within a year and it is not uncommon to have to inspect hundreds, if not thousands of different styles in a year based on precise settings.”

In terms of defect types, he adds, there may typically be over 100 that need to be accurately detected, classified (named) and graded in real time.

“Added to this is the need to ‘filter out’ the random occurrence of ‘non defects’, such as loose threads, lint and dust on the surface – the number of which can be higher than actual defects – and it is clear that a bespoke system is required.”
The development team has consequently established metadata for identifying defect properties, enabling the successful identification of faults from a much smaller number of images.

“The system employs a unique combination of machine learning for automated style training and novel algorithms for defect detection, to provide high quality images for the AI real time defect classification and grading software,” Shelton explains. “Due to the inherent variation in fabric features – raw materials, construction, texture, colour and finishes, as well as the differing product quality standards in value chains and the regional variations in what defects are called – our AI engine uses models built for each individual company or group of companies, or product value chain.”

The AI models are constructed so that the user operatives can populate them with their own data produced by the vision system or by obtaining defect images from another imaging source (eg a mobile phone camera).  

The occurrence of defects is sporadic and many defect types occur infrequently, although when they do, they can have severe consequences. These scenarios re-enforce the need for the AI engine to be quickly set up and able to operate accurately with limited data sets of typically between 30 and 50 good quality images per defect type.

A further feature is a tool enabling the user to periodically ‘clean up’ the AI data during the set up phase. This is used to resolve conflicting data and to correct mis-named images.

Generally, the highest cost component of fabric production is the raw material and in addition to finished product inspection, a cost effective use for vision systems is in process operation.

Generally, the highest cost component of fabric production is the raw material and in addition to finished product inspection, a cost effective use for vision systems is in process operation.

“There is a need for the real time detection of defects that are being created in separate processes, such as printing or coating and for real time automated systems that can accurately determine the defects and their severity and provide a reliable signal for an operative to rectify the issue, This can result in considerable savings.

Prior to Shelton introducing powerful customised machine vision and real time defect classification, the only systems available were those that required manual sifting through vast numbers of images, which included both real defects and ‘non defect’ images. The task was very often overwhelming and did not provide much benefit beyond manual fabric inspection.

More information:
Shelton Vision fabric inspection
Source:

AWOL for British Textile Machinery Association (BTMA)

(c) BTMA
The James Heal AirPro air permeability tester
11.10.2022

BTMA: Testing equipment manufacturer James Heal celebrates 150th anniversary

Long-standing BTMA member James Heal is celebrating its 150th anniversary this year, the company’s formation dating back to 1872, when it was listed as an oil and tallow merchant and mill furnisher in Yorkshire, UK.

Today, as a testing equipment manufacturer for textiles and nonwovens, James Heal continues to expand its range, with a focus on making testing simple – most notably with the introduction of its Performance Testing collection of instruments, most recently the AirPro and HydroView systems.

AirPro
The new James Heal AirPro air permeability tester is used to test the resistance of the flow of air through woven, knitted and nonwoven textiles. Its software offers flexibility with standards and  comprehensive reporting options and different test head sizes are available, making it suitable for a range of applications and standards

Long-standing BTMA member James Heal is celebrating its 150th anniversary this year, the company’s formation dating back to 1872, when it was listed as an oil and tallow merchant and mill furnisher in Yorkshire, UK.

Today, as a testing equipment manufacturer for textiles and nonwovens, James Heal continues to expand its range, with a focus on making testing simple – most notably with the introduction of its Performance Testing collection of instruments, most recently the AirPro and HydroView systems.

AirPro
The new James Heal AirPro air permeability tester is used to test the resistance of the flow of air through woven, knitted and nonwoven textiles. Its software offers flexibility with standards and  comprehensive reporting options and different test head sizes are available, making it suitable for a range of applications and standards

HydroView
The HydroView hydrostatic head tester is meanwhile designed to measure the penetration of water in materials which have an end use that requires water resistance, such as those in the medical, geotextiles and nonwovens sectors. It is also proving essential in the testing of end-use applications for technical textiles, such as in protective gloves, diving dry suits and winter sports apparel, to fishing waders, roofing, tenting, ground sheets and more.

(c) BTMA by AWOL Media
08.09.2022

Shelton Vision presents new fabric inspection technique

A new fabric inspection technique for accurately detecting the most subtle of defects on patterned fabrics during high speed production has been developed by BTMA member Shelton Vision, of Leicester, UK.

The patent-pending system has been integrated into the company’s WebSpector platform and validated through factory trials on a purpose-built full scale in-house demonstration system with sophisticated fabric transport capabilities. As a result, a first system has already been ordered by a manufacturer of both plain and patterned fabrics, including camouflage, in Colombia. This follows the successful conclusion of a 21-month Innovate UK project in which techniques for the resolution of complex pattern deformations were developed by machine vision and computer scientists in the company, backed up by the machine vision and robotics department at Loughborough University.

A new fabric inspection technique for accurately detecting the most subtle of defects on patterned fabrics during high speed production has been developed by BTMA member Shelton Vision, of Leicester, UK.

The patent-pending system has been integrated into the company’s WebSpector platform and validated through factory trials on a purpose-built full scale in-house demonstration system with sophisticated fabric transport capabilities. As a result, a first system has already been ordered by a manufacturer of both plain and patterned fabrics, including camouflage, in Colombia. This follows the successful conclusion of a 21-month Innovate UK project in which techniques for the resolution of complex pattern deformations were developed by machine vision and computer scientists in the company, backed up by the machine vision and robotics department at Loughborough University.

Restrictions
Traditional methods for defect detection rely on human inspection which is ineffective, with detection rates under 65%, while the Shelton WebSpector machine vision system offers a sophisticated platform for automated defect detection of over 97%, but until now has been restricted to plain textiles.

While pattern matching and neural network approaches have previously been tried for patterned textiles, they have failed to provide a practical solution due to the extreme complexity associated with pattern matching on deformable substrates like textiles, as well as the time required to train a neural network for each pattern type.

Challenges
The challenge is that fabrics are not rigid and can be creased or stretched and are also subject to local distortion,” says Shelton Vision Managing Director and CEO Mark Shelton. “As a result, inspection without the technique we have developed, would lead to thousands of false positives. Our sophisticated pattern inspection software techniques ensure a clean image, allowing the detection of faults on fabrics running at speeds of up to a hundred metres a minute.”

The full system consists of:

  • A camera and lighting system for optimum image capture at high speed and associated image processing hardware.
  • Self-training software utilising statistical analysis to automate the system configuration for new textile products.
  • An advanced suite of defect detection algorithms for the detection of all textile defect types.
  • An AI-driven defect classification system which learns and automates defect naming in real time, as well as a real time defect grading capability based on client decision rules.
  • A system for recording and retrieving complete roll map images for subsequent review and quality control.

The generation of textile roll maps with complete defect data allows for an optimised textile cut plan, improved downstream processing and quality assurance.

Source:

BTMA by AWOL Media