Artificial Intelligence for Production Automation (AIPA) - Development of an Machine Learning Platform for Additive Manufacturing
Lead Participant:
AUTONOMOUS MANUFACTURING LTD
Abstract
The crisis caused by COVID-19 has significant economic impact on the UK economy. Over 25% of manufacturing staff have been furloughed and especially R&D Departments face a 17% reduction in R&D budget (IHS Markit, COVID-19 Automotive R&D Impact Survey). Over 50% of respondents postpone deployment of deployment of innovative technologies.
Additive Manufacturing (AM) faces a threat due to impact of COVID-19, as innovative technologies are being delayed. As a direct response, AMFG needs to offer the market a solution that offers:
1. Process automation for AM \[AMFG offers a solution for this\]
2. Rapid deployment of the AMFG solution within a customers environment \[Focus of AIPA, **this innovation project**\]
**Role of AMFG:**
AM has been in the news, as companies like Dyson and JLR use AM capacity to build face masks and ventilators. AM will also support global supply and value chains, as global companies evaluate reshoring of manufacturing activities to the UK (NatWest, Manufacturing insights: the pros and cons of reshoring).
AMFG is a world leader in Process Flow Automation for AM and we have successfully deployed our software into large customers such as ArcelorMittal, Henkel or BMW.
Why AMFG needs this:
AMFG sees an increase in interest in Additive Manufacturing from global companies. However, our potential customers suffer from significant cuts to R&D budgets and a backlog of work. In order to capture the current trend in the market, AMFG will develop a solution that allows to rapidly deploy our software within a customers environment.
The current process is to go through a Proof Of Concept (POC) with potential customers, which typically takes 1 year to complete and costs around £50000\. This is due to an iterative process, where AMFG and customers engage in frequent meetings to elicit requirements, understand our customers environment and develop bespoke concepts. A POC is also a risk for AMFG and our customers, as it depends hugely on the experience of the Project Manager (on customer side). This is a significant drain on resources and results in sub-optimal utilisation of production lines long-term due to potential to miss important requirements early on in the development process (when these issues are easy to fix).
**Why AIPA is innovative:**
AMFG suggests to take this to the next level and completely automate the initial stage of the POC. AMFG will achieve this by using novel Machine Learning algorithms to determine the best solution for a customer. This will replace an iterative and risky process by capturing best practice in the market from our customers.
As a result, AIPA will be an interactive questionnaire that automatically suggests requirements based on our customers self-evaluation, which will be used to develop the required features. Additionally, AIPA can be used as an ongoing self-assessment to confirm the development is on track and according to our customers requirements.
AMFG expects that we will be able to triple the number of successfully completed POCs from currentyl 12 to 36 per year.
Additive Manufacturing (AM) faces a threat due to impact of COVID-19, as innovative technologies are being delayed. As a direct response, AMFG needs to offer the market a solution that offers:
1. Process automation for AM \[AMFG offers a solution for this\]
2. Rapid deployment of the AMFG solution within a customers environment \[Focus of AIPA, **this innovation project**\]
**Role of AMFG:**
AM has been in the news, as companies like Dyson and JLR use AM capacity to build face masks and ventilators. AM will also support global supply and value chains, as global companies evaluate reshoring of manufacturing activities to the UK (NatWest, Manufacturing insights: the pros and cons of reshoring).
AMFG is a world leader in Process Flow Automation for AM and we have successfully deployed our software into large customers such as ArcelorMittal, Henkel or BMW.
Why AMFG needs this:
AMFG sees an increase in interest in Additive Manufacturing from global companies. However, our potential customers suffer from significant cuts to R&D budgets and a backlog of work. In order to capture the current trend in the market, AMFG will develop a solution that allows to rapidly deploy our software within a customers environment.
The current process is to go through a Proof Of Concept (POC) with potential customers, which typically takes 1 year to complete and costs around £50000\. This is due to an iterative process, where AMFG and customers engage in frequent meetings to elicit requirements, understand our customers environment and develop bespoke concepts. A POC is also a risk for AMFG and our customers, as it depends hugely on the experience of the Project Manager (on customer side). This is a significant drain on resources and results in sub-optimal utilisation of production lines long-term due to potential to miss important requirements early on in the development process (when these issues are easy to fix).
**Why AIPA is innovative:**
AMFG suggests to take this to the next level and completely automate the initial stage of the POC. AMFG will achieve this by using novel Machine Learning algorithms to determine the best solution for a customer. This will replace an iterative and risky process by capturing best practice in the market from our customers.
As a result, AIPA will be an interactive questionnaire that automatically suggests requirements based on our customers self-evaluation, which will be used to develop the required features. Additionally, AIPA can be used as an ongoing self-assessment to confirm the development is on track and according to our customers requirements.
AMFG expects that we will be able to triple the number of successfully completed POCs from currentyl 12 to 36 per year.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| AUTONOMOUS MANUFACTURING LTD | £175,489 | £ 140,391 |
People |
ORCID iD |
| Sven Hinrichs (Project Manager) |