​Artificial intelligence is being implemented within nearly every aspect of our lives and is slowly changing the face of manufacturing. We only need to look at Industry 4.0 to see its vital part within a manufacturer’s roadmap. Technological advancement is a prime candidate to optimise manufacturing processes, automate complex tasks and identify unknown patterns in workflows.
 
The manufacturing industry is leveraging this game-changing technology with a 60% adoption rate in 2020 (using in at least one business function). This means manufacturers are starting to realise the huge potential of utilising AI to increase their commercial advantage.
 
What is Holding Manufacturers Back?
 
AI is revolutionising almost all sectors, giving empowerment to companies across the globe to gain immense value from their data. Utilising AI can help manufacturers increase operational efficiency, reduce downtime, and deliver high-quality products. If this sophisticated technology is the epitome of Industry 4.0, why is the install rate relatively small?
 
Outdated Technological Infrastructure
 
Manufacturers often use a wide variety of machines, tools and systems that use different and competing technologies. Some of these may be outdated and not compatible with their other technological infrastructure. This combined with a lack of digitalisation is preventing manufacturers from capitalising on the potential.
 
Confidence, Uncertainty and Trust
 
A significant barrier to AI adoption is the trust and confidence in the capabilities of abstract algorithms. Personnel within manufacturing who don’t hold a data science background struggle to validate how predictive modelling and data science work together.
 
JetSoft’s Use of AI in Manufacturing
 
JetSoft employs AI techniques to automatically analyse inspection and manufacturing data, to detect anomalies in product output and associate them with the upstream processes that created them. Manufacturers gain deep insights to understand the correlations in inspection failures and deploy targeted process improvements, to reduce the likelihood of that anomaly occurring again.
 
Our OverSeer solution automatically manages inspection data and techniques from different sources. OverSeer reads and places the data automatically into a central database to perform thorough assessments. Data from new inspections can be tracked as it passes through each module that has an output. Working on legacy formats means our cutting-edge AI technology is able to analyse historic data from manufacturers to instantly spot anomalies and defects.
 
 
JetSoft deploys AI technology into a customer's site, which analyses images and inspection data to automatically detect anomalies and patterns within the data. The AI functionality pinpoints which processes have created the specific anomaly without having to stop the entire production to detect and fix the problem. Manufacturing personnel are then able to analyse the anomaly against a defect specification to gain a deeper insight into if the anomaly has become a defect.
 
Benefits of AI in Manufacturing
 
The applications of using AI are widespread and revolutionising, offering actionable insights into reducing waste and scrap. So, what are the overall benefits of using AI in manufacturing?
 
Predictive Analytics for Increased Production Output
 
AI systems are able to learn from machine learning algorithms and make use of predictive analytics. The manufacturing industry holds a large volume of data, which predictive analytics is powered from. Manufacturers can easily pinpoint anomalies without disrupting the production process.
 
Improved Process Quality
 
AI technology creates an efficient production process, resulting in higher product quality. The software ensures that products going through the inspection stage don’t just meet quality standards but are of higher quality and closer to nominal. 
 
Reduction of Scrap and Waste
 
Early detection of anomalies using AI techniques allows manufacturers to make quick industry-related decisions that won’t further impact production and create more waste. Reducing waste and scrap has a direct correlation with profit generation and the bottom line. When high-quality products are always created, manufacturers are maximising their output without having to scrap any materials. Utilising assets more effectively creates a reduction in the variable cost of each component, meaning lower prices can be introduced to become more competitive. Minimising component costs ultimately creates a more profitable company.
 
Implementing our solutions has allowed established clients to benefit from huge commercial gains. One JetSoft customer who using our solutions has reduced their scrap rates by over 60%, resulting in savings in excess of $2m a year.
 
AI solutions allow manufacturers to utilise their assets more effectively and take advantage of commercial gains. If you want to know more about how JetSoft can help you, get in touch.

Production loops can surface within any manufacturing sector, from pharmaceuticals to aerospace. The costs and implications of production loops can be detrimental to a business, meaning it is vital to acquire a stringent testing process to identify errors and meet those output expectations.

 
Production Loops   
 
In manufacturing, production loops generally materialise because of an error or an unknown, resulting in a good or component having to be remade or reworked. As high investments are spent in design, production and delivering a product, all components must match the specification, even the smallest defect or error could have damaging effects. Errors can happen due to defective equipment or material issues. For example, a batch of materials may unknowingly be of lower quality and therefore too weak for its intended purpose, leading to sub-standard products.

 
Production Errors
 
Production loops cost manufacturers unnecessary expense, the time spent re-manufacturing or fixing a product or component leads to additional costs and delayed output.
 
Remaking a component essentially doubles the cost and time to dispatch and delays other goods that were planned to be on the production line at that time. Brand reputation, customer relationships and trust are paramount to profit and growth. Delays to customers receiving their goods can drastically impact brand reputation and, in some circumstances, could even lead to clients going elsewhere.
 
Alongside this, defective products arising creates excessive consumption of materials and energy, significantly affecting profitability. These losses not only impact performance and production costs but also directly increase the carbon footprint with the additional CO2 emissions that are released.
 
 
Identifying the Root Cause
 
JetSoft’s solutions create visibility over the production line, which enables manufacturers to operate more reliable production and mitigate production loops. By centralising inspection data alongside vital production data, our OverSeer framework can derive further intelligence on your operations manufacturing process.
 
To receive detailed inspection data, our solution DigiOps allows manufacturers to digitise the inspection process, collating all the data formats into the OverSeer platform, where it will be combined with the array of production data you have available. Our specialised machine learning algorithms will analyse all the data inputted, in order to spot anomalies to identify common problems occurring.
 
  1. Reduce Extra Production Costs 
As an example, our solution may find that products going through equipment are experiencing higher inspection failure rates than those on other lines, indicating a problem with the equipment. Our solutions can help you identify exactly where a defect is originating by integrating inspection alongside other manufacturing data, so that maintenance of equipment can quickly take place to prevent another component facing the same failure and therefore the associated costs in re-make.
 
  2. Minimise Delayed Output 
Digitising paper-based processes through our solutions helps you to gain end-to-end visibility of all inspection data, to make quick and informative decisions. If a material defect rate is higher from a certain supplier, alternative materials may be sourced to ensure fewer defects and thereby fewer delays.
 
  3. Reach Net-Zero 
JetSoft can help you meet those looming carbon emission targets. Gain complete control and traceability over your inspection and manufacturing data, to identify the root cause of any production-related complications before they become a production nightmare. OverSeer can help you drive sustainability, by increasing quality control, spotting defects earlier on and where from, minimising the chance of a production loop. Fewer production loops mean the associated energy required is taken out of the equation and so are the carbon emissions.
 
  4. Meet Customer Expectations 
OverSeer curates an in-depth data process through our user-friendly analytics dashboard, allowing you to input all formats of data, from complex 3D images to the simplest of forms. Our solution can cross-compare real-time and historic data, to monitor trends and help you gain in-depth insight to produce high-quality products. Minimising production loops is integral to reaching quality controls to exceed customer expectations and stay ahead of competition.

 
A Framework for Success
 
Implementing a digital framework is critical to driving efficiency in your production line and paramount to reducing production loops.
 
By deploying JetSoft’s solutions, you are able to identify defects to improve future production processes, increasing your output and decreasing waste. High-quality and reliable output will create major opportunities to gain a competitive advantage in the manufacturing landscape, get in touch to find out more.

​​As we emerge from the climate conference in Glasgow, all manufacturers are facing the challenge of the journey to net-zero, and many are utilising digitalisation to reach these goals. The manufacturing industry is one of the largest contributors to greenhouse emissions globally. Manufacturing in the US accounts for 23% of the total direct carbon emissions, according to the Environmental Protection Agency. Most of the direct emissions produced are generated during the production process. 
 
The manufacturing industry must accept their heavy responsibility for contributing to greenhouse emissions, as they edge closer to the 2050 net-zero target. Drastic reductions in emissions need to be seen throughout the next several decades and the industry must adopt new and clean approaches by planning and investing in a comprehensive roadmap to commit to action.  
 
A New Identity 
 
When the manufacturing industry embraced digital technologies, it paved the way to a new identity of smart manufacturing. The adoption of artificial intelligence, the internet of things and 3D printing are among the technologies that are linking systems and processes, across areas of production, design, and services, to reduce costs and stay ahead of competitors. The innovation of digital technology is now vital to enable the transition to net-zero and can help reduce global emissions by 15%. The transition to digital enhances processes and redefines decision making and monitoring performance, whilst preventing the likelihood of defects, downtime and rework. Digitalisation has the potential to provide manufacturers with the architecture, technology, and concept to enable a circular economy and transition to a sustainable production system.  
 
Reducing the Footprint with Efficiency  
 
The manufacturing industry carbon footprint is largely due to its intensive energy processes. Although the industry cannot completely eradicate their carbon emissions, processes can be put into place to balance and reduce their output to reach carbon neutrality, whilst saving money. There are a number of ways manufacturers can achieve this, whether its integrating IoT sensors to reduce energy consumption, embracing automation for efficiency or digitising operations to spot efficiencies and areas of excess waste. 
 
Preventative Waste 
 
Waste is not only lost productivity but a wasted resource and therefore an unnecessary contributor to greenhouse gas emissions. Industry 4.0 is allowing manufacturers to tackle this huge problem, by giving manufacturers an overview of historic wastage data to change their processes in the future. Every time a component fails its inspection, the materials are either melted down to go back into production or scrapped and taken to landfill. Not only is this a financial burden, but the duplication of production results in an even higher emissions output.  
 
Visibility  
 
Leveraging digital technology, manufacturers can gain in-depth visibility over their facilities, to drive sustainability through waste management and energy efficiency. Pulling in data from the supply chain, production process and inspections means insightful analytics of failed inspections and wastage data can be gained to identify trends that can help to limit wastage going forward. Gaining overall visibility of production output provides insightful knowledge on where manufacturers can drive efficiency or even carbon offset the emissions they can't afford. 
 
The Next Generation of Inspection Technology 
 
JetSoft has the ability to redefine manufacturing with tools that provide traceability and visibility. JetSoft’s solutions aggregate inspection and production data into a central system that utilises AI to pinpoint the direct cause of the defect along the manufacturing process.  
 
Overseer enables users to gain complete control and clear insights into their inspection and manufacturing data. Overseer integrates a range of different data types from both inspections and production. Overseer supplies user-friendly dashboards, curating analytics in a visual manner in real-time and without the need for unsustainable paper documentation. Manufacturers cannot create a circular economy without in-depth data into their processes. 
 
If your scrap rate is not zero then you should be working on reducing it, not only to increase productivity, but to increase output quality, and our solutions have the capability to extensively reduce this figure. JetSoft’s unique solutions aid the manufacturing process, to understand the root cause of defects and help manufacturing managers to make better-informed decisions and reduce potential waste and energy consumption.  
 
If a component created from a material, that is being produced from a particular supplier has a higher inspection failure rate, this would demonstrate a critical problem within the supply chain that urgently needs to be rectified. JetSoft’s tools allow manufacturers to have complete traceability to identify production-related issues early before they become critical, not only increasing quality control but implementing circular economy principles. 
 
JetSoft can help manufacturers stay ahead of the competitor curve and increase profits, whilst reaping the rewards to cut cost, carbon, and reduce environmental impact. Our solutions accelerate the manufacturing industry towards a circular economy by driving sustainability, contact us to find out more. 

​This month (August 2020) marks the second anniversary of our first AI, Automatic Defect Recognition (ADR) system being installed in a production environment.
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To date we haven’t promoted our ADR system publicly, deciding to concentrate on working with select companies and individuals to fully develop it. However it has formed a natural addition to OverSeer’s framework. OverSeer collects inspection data and performs validation and calculations upon it, notifying stakeholders of results and storing data for further analysis. ADR is just another calculation.

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This flagship installation at an aerospace metal production facility was installed in August 2018 and utilises many of JetSoft’s software features. We are gathering positive feedback from the site about the quality and productivity benefits it is delivering, including:
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• Automatic defect and characteristic recognition
• Combining factory wide data with inspection information
• Driving process change
• Generating new insight and enabling predictive maintenance
• Reducing the requirements on downstream processes

In a series of upcoming posts, I will discuss these areas in more detail to explain how our processes deliver these benefits, starting with an insight into our ADR software.
 

A note on ADR
Firstly, this article uses the term ADR, which is an industrial term to mean the automatic detection of a problem within a component. ADR isn’t clearly defined yet within the industry, as explained in the previous post ADR - what’s expected? 

​The installation discussed in this article is an Automatic Indication Detection (AID) system, where inspections are still signed off by a qualified human operator.
 

How it works
JetSoft’s core mission is to provide systems that automatically aggregate and catalogue inspection data into a single database. We understand inspection data, and we gather it into a convenient format to take advantage of it. Our solutions combine quality control information with data from other manufacturing systems. These include details about the processing conditions that the part has gone through, details of the component structure and the customer specifications. Combining this information, and making it available for all, has the huge benefit of keeping quality at the forefront of everyone’s mind. I will talk about this in more detail in a future article.

This collated data is passed through an extensible list of models which are trained to look for particular faults or certain characteristics. Each model outputs a confidence level based on the likelihood that the inspection contains a positive match. Any disagreement between the system and inspector is highlighted to the responsible level 3 or automatically sent for a second opinion. This drastically minimises the potential for misdiagnosis. Additionally, the output of these models is stored in the system and is made available for analysis. 

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Thresholding of output confidence enables the user to select the desired level of Probability of Detection (POD).

Model training is in depth and employs a hybrid approach using a combination of pre-processing, AI machine learning techniques using historic data and deterministic algorithms.

One of the key advantages of this system is its ability to subcategorise defects. For example, this system can differentiate between a lamination crack caused by rolling and a quench crack caused by heat treatment. Detecting this distinction is non-trivial for a human inspector, requiring further analysis

​Similar defects which are caused by different process may look very similar to inspectors, but it is important to differentiate them to target improvements towards the appropriate processes. For inspectors to make this distinction may require additional training and further analysis, whereas the systematic solution is automatic, quick and consistent.

​I mentioned earlier that the system can be configured to identify any characteristics, not just defects. This is important as there are many things we may want to observe and track that could indicate future problems which in time could lead to defects, or just a general reduction in quality. The time and cost investment of training inspectors to manually identify such trends or patterns would not be justified, however by recognising these warning signs early, there is time for mitigating action to be taken to reduce scrap rates in the long run.

 
Key benefits summary
An independent assessment – this system provides an independent assessment alongside the inspector. If there is any difference between the system and the inspector, the responsible level 3 can be alerted or it can be automatically sent for a second opinion. This improves quality and reduces the chance of mistakes or misdiagnosis, without increasing operating costs.

Sub categorisation of defects – this is the big commercial driver behind the software. It is the application of this feature that has seen scrap rates plummet. An inspector’s job is not necessarily to interpret the cause of a defect, but to highlight its existence. By training models to examine for defects with a known cause, we can target our time and efforts on improving process where we will see the most commercial benefit. This creates a feedback loop of high-quality, useful, data from inspection to all other departments.
 
Next time
Quality should be everyone’s responsibility, but this is only achievable if everyone has a clear vision of quality, and their impact upon it. JetSoft is providing the tools that deliver real time statistics which demonstrate and communicate quality to all.

​Next time we’ll look in more detail at how we’re achieving this and how JetSoft’s is helping everyone to take ownership of quality.
 

​​Automatic Defect Recognition (ADR) is a term that is gaining traction within the NDT world. However, neither ADR or the functions such a system should deliver are clearly defined.

​At JetSoft we prefer to split ADR into two stages, using the terminology Automatic Indication Detection (AID) and Automatic Part Sentencing (APS).
 
The problem with ADR
The scope of ADR isn’t clear. It seems, depending upon your source, ADR can range from a system which highlights abnormalities to an inspector, to one which can perform full sentencing (go/no go), eliminating the need for an inspector at all. In practice, most end users anticipate a solution resembling the latter, but this may not be true of the products promoted by equipment vendors.
 
AID and APS
At JetSoft we don’t use the term ADR, due to the lack of clarity on what this means, instead we use the terms:

• Automatic Indication Detection (AID)
• Automatic Part Sentencing (APS)

Defect detection is a two step process. The first step is the discovery of an indication or discontinuity, the second step is evaluating that indication against a specification to determine whether it is a defect. ADR may (or may not – depending upon who you talk to) be considered to be the automation of this two-step process. However, in JetSoft’s experience each stage can be automated separately, indeed it would be difficult not to.

JetSoft refers to the automation of the first step as Automatic Indication Detection (AID) with the results highlighted to an inspector or used as an independent assessment and compared with the manual outcome.
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The second step, qualifying the indications against defect specifications, JetSoft calls Automatic Part Sentencing (APS). Because once a system is confidently automatically qualifying defects it must logically be able to decide if the part passes or failed.

​Is ADR overhyped?
News about ADR (go/no go) using deep learning and machine vision is rife and in my view racing towards the peak of inflated expectations on the Hype Cycle. 

The Hype Cycle, developed by the technology firm Gartner, details the phases a new technology goes through to reach maturity and widespread adoption. 

The scale of the peak and trough of this graph is exacerbated by the lack of clear definition of ADR. It is not difficult to imagine a situation where customers and suppliers differ on the expected functionality, resulting in a negative view of ADR and its possibilities.

However, ADR will happen. There is a large commercial appetite to reduce the cost of inspection and new advances in computation technologies prove that it is possible technically. But it may take a while to get there and require more investment (time and money) by users than anticipated. The market should be prepared not to expect a single turnkey algorithm that performs ADR on any component for any defect, but, should rather, anticipate solutions that require dedicated configuration, development and training based on what the users is looking for and the component/material type.

We hear a lot about the benefits involved with ADR, but perhaps less so of the realism of implementing and delivering them.
 
What to expect
JetSoft is amongst several companies utilising these new technologies, with working systems, using deep learning and machine learning techniques in place in production environments.
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It is JetSoft’s experience that performing accurate automatic detection of defects takes dedicated time, development, and extensive testing. Furthermore, for an AID system to be effective, additional information beyond inspection data is required. Including component details and upstream process data, both of which are critical.

​It is important to note that in every case the preceding step to achieving an APS must be a fully working, and empirically proven AID system.
 
Contact us
As always, we would love to hear your thoughts on this, and if you have any questions, or would like to know more about what we do and the systems we have developed please get in contact.

What is a PACS?

Picture Archiving and Communication Systems (PACS) provide central storage and access to images from multiple sources across multiple techniques. The Picture in the PACS acronym is a bit of a misnomer as PACS don’t just deal in pictures. They also contain all the data around that picture, including when/by who/how that picture was taken. It contains that critical, contextual information that is written on the back of an old photograph.

 
Where are they used?

PACS are used extensively and ubiquitously in the medical world, enabling a plethora of medical practitioners to offer a smooth patient centric service. PACS act as a hub in the network of collection equipment and viewing terminals, allowing the collectors of data and the evaluators to work at arms length. Medical images can be transferred electronically and so instantaneously to wherever the patient requires them. Medical PACS revolve around the DICOM standard, and are very mature, having been first developed in the 1970s.
 
What are the benefits?

This is great for the medical industry, but why do we need them for industrial NDT? Well, we do but for different reasons, reasons that are becoming more critical and useful.

Unlike the medical industry manufacturing is production, not component centric. In the DICONDE format – the NDT equivalent of DICOM standard – component is analogous for patient, except the component is not normally a repeat customer and the suite of tests in manufacturing are generally prescribed and linear, and diagnosis is normally subject to quantifiable standards.

However, industry can benefit from PACS technology in other ways. As projects become more global (such the countries participating in the F35 project), and companies become more likely to have an off site level 3, stakeholders or critical NDT decision makers are not in the same location as the inspection. In this case a PACS helps to make the flow of data and communication more efficient, reducing the time needed to make a decision thus making manufacturing more efficient.

NDT image data quantity is expanding and becoming more complex, both in terms of volume and size, and quite often management of that data will incorrectly fall on to the NDT department. They have the impossible task of managing continuous data generation across many different proprietary formats from multiple locations and techniques. A good PACS will order this data automatically, make this data manageable, and provide quick and easy search and view functions, reducing the time demands on key NDT personnel allowing them to work more effectively.
 
Where can I get one?

JetSoft’s OverSeer is a PACS solution that specialises in NDT information. OverSeer can automatically read and understand many different formats from multiple suppliers and techniques, reducing resources absorbed with data management. OverSeer is DICONDE compliant and so can both input and output DICONDE information and can act as a DICONDE converter for your existing data.

OverSeer’s database with web interface makes it easy to search and find inspections by their contextual information, for example all the scans done on a particular piece of equipment, by a certain operator or with a particular defect.

OverSeer goes further than a standard PACS and offers Business Intelligence functionality, allowing performance of Statistical Process Control (SPC), trend analysis, and full health check of the NDT department.

By using OverSeer and having NDT information in a central database allows us to perform some new types of analysis, including data fusion, automatic defect recognition, or dataset analysis. An example of this was a defect heatmap that was recently performed for a JetSoft customer, which provided new insight and enlightening results

For more information or to arrange a demonstration please get in contact with JetSoft.

http://www.jetsoft.co.uk/contact.html