Reduced Human Intervention For Additive Manufacturing at Large-Scale - Manufacturing the future - Manufacturing Technologies
Lead Research Organisation:
University of Warwick
Department Name: Sch of Engineering
Abstract
The three key objectives are as follows:
1. To create a novel large-scale, sensor-laden thermoplastic composite extrusion system, together with appropriate simulation and modelling tools, focussing on their suitability for direct production. - An automated robotic AM process will first be virtualised and used to explore the integration of different process monitoring strategies, variation of process parameters and their impact on process efficiencies, cost reductions and production rates. The model will unlock a robust and deep process understanding as well as enabling trialing of new strategies and methods virtually. The simulation will be written to be process agnostic, such that it can be applied to any robotic deposition process (e.g. Wire + Arc Additive Manufacturing) with the open nature greatly enhancing the academic impact of the work.[2]
2. To quantitatively demonstrate the improvements resulting from such development, in terms of material properties, part quality, cost and delivery lead-time. - An extruder will be built as an end effector for the robot. The extruder will incorporate in-process monitoring features and comprise of sensors to monitor variables such as extrusion rate, extruder offset, extrudate track width and temperature, track separation, inter-layer temperature differential and material properties.[3] The extruder package will monitor these parameters in real-time and communicate with the robot controller over ethernet enabling it to make a decision to alter the print strategy and or modify its extrusion parameters. The architecture of the entire system will comprise of sensors to monitor variables such as part presence check, part failure analysis using machine vision and part temperature profile.
3. To further demonstrate the suitability of this enhanced system for the production of selected demonstrator components such as those for aerospace and automotive.
Within the context of AM activity within the UK, the project seeks to overcome several barriers identified in the UK National strategy for AM, such as requirements for deeper understanding of processes, materials and machines as well as lack of appropriate skills.[4] A 2015 positioning paper identified opportunities in the field of AM such as the need for bigger build platforms, modelling and simulation for AM, surface finish improvements, speed/productivity, reliability, material property information, data management, waste reduction and education and training.[5] It also identified that there are currently gaps in the market for the development of a new production tools rather systems suited for prototyping. It is estimated that by addressing these needs, the UK can gain £5bn of the global market for AM, which is forecast to reach £69bn by 2025.[6]
References
1. Malte, B., et al. "How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective." International Journal of Mechanical, Industrial Science and Engineering 8, 1 (2014): 37-44.
2. Williams, S. W., et al." Materials Science and Technology 32, no. 7 (2016): 641-647.
3. Abinesh Kurapatti, R., et al. "An in-process laser localized pre-deposition heating approach to inter-layer bond strengthening in extrusion based polymer additive manufacturing." Journal of Manufacturing Processes 24 (2016): 179-185.
1. To create a novel large-scale, sensor-laden thermoplastic composite extrusion system, together with appropriate simulation and modelling tools, focussing on their suitability for direct production. - An automated robotic AM process will first be virtualised and used to explore the integration of different process monitoring strategies, variation of process parameters and their impact on process efficiencies, cost reductions and production rates. The model will unlock a robust and deep process understanding as well as enabling trialing of new strategies and methods virtually. The simulation will be written to be process agnostic, such that it can be applied to any robotic deposition process (e.g. Wire + Arc Additive Manufacturing) with the open nature greatly enhancing the academic impact of the work.[2]
2. To quantitatively demonstrate the improvements resulting from such development, in terms of material properties, part quality, cost and delivery lead-time. - An extruder will be built as an end effector for the robot. The extruder will incorporate in-process monitoring features and comprise of sensors to monitor variables such as extrusion rate, extruder offset, extrudate track width and temperature, track separation, inter-layer temperature differential and material properties.[3] The extruder package will monitor these parameters in real-time and communicate with the robot controller over ethernet enabling it to make a decision to alter the print strategy and or modify its extrusion parameters. The architecture of the entire system will comprise of sensors to monitor variables such as part presence check, part failure analysis using machine vision and part temperature profile.
3. To further demonstrate the suitability of this enhanced system for the production of selected demonstrator components such as those for aerospace and automotive.
Within the context of AM activity within the UK, the project seeks to overcome several barriers identified in the UK National strategy for AM, such as requirements for deeper understanding of processes, materials and machines as well as lack of appropriate skills.[4] A 2015 positioning paper identified opportunities in the field of AM such as the need for bigger build platforms, modelling and simulation for AM, surface finish improvements, speed/productivity, reliability, material property information, data management, waste reduction and education and training.[5] It also identified that there are currently gaps in the market for the development of a new production tools rather systems suited for prototyping. It is estimated that by addressing these needs, the UK can gain £5bn of the global market for AM, which is forecast to reach £69bn by 2025.[6]
References
1. Malte, B., et al. "How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective." International Journal of Mechanical, Industrial Science and Engineering 8, 1 (2014): 37-44.
2. Williams, S. W., et al." Materials Science and Technology 32, no. 7 (2016): 641-647.
3. Abinesh Kurapatti, R., et al. "An in-process laser localized pre-deposition heating approach to inter-layer bond strengthening in extrusion based polymer additive manufacturing." Journal of Manufacturing Processes 24 (2016): 179-185.
Organisations
People |
ORCID iD |
| Simon James Leigh (Primary Supervisor) | |
| Elizabeth BISHOP (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509796/1 | 01/10/2016 | 30/09/2021 | |||
| 1884718 | Studentship | EP/N509796/1 | 02/10/2017 | 30/09/2021 | Elizabeth BISHOP |
| Description | This award has been used to develop Additive Manufacturing (AM) (more commonly referred to as 3D printing) technology to improve large-scale 3D printing through various approaches. This project focuses on a technology called Fused Deposition Modelling (FDM) whereby a thermoplastic polymer is extruded through heated nozzle, typically in planar layers, onto a build platform to build up a physical 3-dimensional part. Typically, 3D printers produce parts up to about 250 mm, but systems can be scaled to much larger sizes where they are able to produce large-scale parts up to several metres in width, that can be used to manufacture large structural parts for vehicles and infrastructure. Printing at such a large size though, presents a whole new set of challenges that need to be addressed. This research has sought to understand the printing process more thoroughly and devise new ways to improve the printing systems themselves, making them more reliable and able to operate autonomously and adapt to changes during the printing process to manufacture a successful part every time. Specifically, this work has led to the creation of a large-scale AM demonstration cell that incorporates a custom-built extrusion system and a set of sensors able to monitor the extrusion system, printer and part being produced to gather data about the process and inform autonomous changes to the process so it can operate with minimal human interaction. The system is now being used to explore how new approaches to large-scale AM can yield parts with improved mechanical properties and surface finish which is of importance to the automotive and aerospace industries. |
| Exploitation Route | The outcomes from this research are have led to the preparation of several academic papers (which are currently being submitted) and through collaboration with industry, potential IP that can be commercialised by a UK SME placing them at the forefront of the technology and boost their international competitiveness. |
| Sectors | Aerospace, Defence and Marine,Education,Manufacturing, including Industrial Biotechology |
| Description | The findings of this project have also contributed to the RHIFALS, an Innovate UK funded project. The project focused on improving two main areas of the digital workflow involved in operating the equipment -thereby improving the efficiency of the system, reducing both process time and cost. Additionally, the project focused on the procedures necessary for mass-production and demonstrated the suitability of such a manufacturing approach for the future direct production of components for automotive manufacturing. |
| First Year Of Impact | 2019 |
| Sector | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Education,Manufacturing, including Industrial Biotechology |
| Impact Types | Economic |