IoT ACES (Internet of Things applied to curved engineered surfaces)


< Project overview >


There is increasing demand for manufacturers to customise products at a late stage, independent of the size or shape of these products. The system chosen needs to ensure minimal on-cost to the manufacturer. For example printing conductive tracks, electronics or 3D textures onto the parts of products sold in the aerospace or energy industries, thus enabling the products to connect to, and benefit from, the internet of things (IoT).

This project looked at the flow of information required and likely challenges in delivering late-stage customisation to large curved engineered surfaces, postproduction using a standard approach of robotic systems to deliver materials directly onto the surfaces.

In this particular case a demonstrator was built in the Distributed Information and Automation Lab in the Institute for Manufacturing, with support from capabilities delivered (but not detailed in this report) with Pilkington NSG, as reported in a recent article of The Manufacturer [1]. A curved glass windscreen was used as an example of an industrially relevant component.

Project aims

The project aimed to create a demonstrator at a pilot scale that showed how IoT can bring together the information regarding part geometry, materials, surface chemistry, and the key parameters of a chosen coating or printing technology (eg stand-off distance, speed). The demonstrator would not only show the potential for such an enabled part, but would also provide clear communication on how industry could replicate this.

The project first looked at the virtual layout and process required to identify the robotics required to address non-flat surfaces. A key step was to identify the flow of data to enable such a process. Next the aim was to deliver a simple functional device to the surface of a non-flat product using pilot-scale technologies. The aim was to then present the findings at the largest industrial inkjet conference, and submit a proposal to follow on the work with a company.

What was done?

Robotic systems were deployed in pilot-scale facility to test the flow of data required and how it could be integrated in order to print customised products which would have IoT sensing capabilities. This data flow was mapped out with each function, key input and output noted. A route to giving a virtual manufacturing setup was defined and demonstrated. Simple demonstrations were carried out to demonstrate the importance of IoT in delivering late stage customisation. Three different deposition technologies were anticipated to be attempted in this work.

Due to COVID-19 restrictions, there was a significant reduction in access to the lab and extrusion deposition was analysed in most detail. Inkjet capabilities were started and not delivered in the pilot-scale. Initial components were also sourced and prepared for fused deposition modelling, and will be trialled over the summer.


A map has been developed of the required process, information inputs and outputs, and example software/controllers. This is a useful starting point for any industry to tackle late stage automated customisation. This also highlights the important information to capture within an IoT system that will enable late stage customisation.

Secondly, late stage customisation of products is being discussed to enable IoT to develop through the integration of additional sensing and communication technologies.

We specifically show the software and hardware required to deliver digitally patterned materials to a surface, as a first step towards printed electronics on large components.

The software for offline programming robot was tested, with different output files produced, in some cases requiring additional compilation tools for specific robot brands. These will be useful learnings for further studies and are also included.

The main barriers up to this stage were identified to be mainly:

  1. The translation of vectorised images precisely to a real product, rather than to a CAD representing the product.

  2. The need for consistent positioning and alignment.

  3. Significant further analysis will be needed (or a case-by-case analysis initially) to enable a digital coating/printing output to be integrated into a robotic system that can take into account the patterns and curvatures to then provide a automated printing strategy.

  4. Additional data that will be needed for multi-nozzle inkjet printers, with results showing some of these challenges. Printing will require excellent control over the ink-substrate chemistry, and integration of pre- and post-processing equipment. Some key parameters are highlighted in the report.

Attendance at the conference (IJC 2020) was cancelled with very late notice due to COVID-19 restrictions. The conference organisers decided not to hold a virtual alternative.

A proposal was submitted with Pilkington NSG to EPSRC and progressed to the final stage, but was not successful at the final panel. We were recommended to try with a Programme Grant or Innovate UK grant, which will be the next step.

Deliverables and other tangible outputs

During the project we produced:

  • Written report detailing the resources and commands used within this work, along with the key functions to avoid limiting to particular software/hardware. This includes the data flow of the process, key challenges identified and potential solutions for future consideration.

  • Video demonstration of how to generate a printed path from an initial design to implementation, highlighting challenges of file type, tool and reference frame information.

  • Project overview presentation.


The project should help companies understand the potential of integrating this printing process, and late-stage product customisation, within their production operation, especially if they already have robotic systems in place. The detailed information we have created from the project will show them how to print IoT sensors on to complex curved products, indicating the resources needed and how to overcome the barriers to adoption of this process.

In addition, the project enabled a Research Associate (RA) to be trained in the use of industrial robots and vinculated software tools as well as safeguarding their job. The project will have an impact on the research group’s other projects which look at the field of deposition on complex surfaces.

Next steps

  1. A specific example is being explored with Pilkington NSG through a masters level project.

  2. Challenges due to access prevented integration of other printing techniques and this will be explored further (including fused deposition modelling printhead).

  3. A more detailed experimental link will be explored between the collection of information regarding sample and the use of this to enable late-stage customisation.

  4. Additive manufacturing will be used to print devices onto products to show potential IoT devices.

  5. The research team is working on integrating tools for pre-treatment and post-treatment processes for any functional materials deposited on surfaces.

  6. A second research proposal is being prepared based on the feedback of the first, unsuccessful proposal.

Lessons learned

Working with this pilot-scale manufacturing process has helped the RA to conduct research which encompasses the vision of de-risking the adoption of an IoT enabled technology, by addressing ways to overcome barriers to adoption. The research team also benefited from learning new concepts on business decision making, providing a better understanding on how industry operates.

It would have been extremely beneficial if industrial collaborators could have visited the lab, and we could have visited external companies, but COVID-19 regulations made this impossible. This would lead to an exchange of more practical skills during the implementation of the project. COVID-19 restrictions led to a significantly reduced access to the lab facilities, and so less progress could be made with the physical demonstrator, although this is still planned to go ahead.

The project would have run more quickly if we had collaborated with a team with expertise in CAD/CAM modelling. Also, if we had had access to a variety of differently shaped parts, as well as their associated CAD files, we could have made more replications for analysis and sped up the testing process by reducing the need for surface treatment and complex alignment issues.

What has Pitch-In done for you?

As the RA was directly involved in this project, I have found great value in having acquired skills for the use of robotic hardware systems at a pilot scale and the linked software control. This has made me realise of the challenges for generating a clear flow of information to optimise testing and operation and enable progress towards IoT capabilities.

I have also experienced the limitations of scaling up processes from the laboratory scale, in terms of resolution, object handling or energy consumption associated to these scales, which has helped me in becoming more aware on the struggles for adoption of innovative technologies.

This project has also allowed me to connect with other researchers working in fields of digital manufacturing and distributed information.

The work reported here was part-sponsored by Research England’s Connecting Capability Fund award CCF18-7157 – Promoting the Internet of Things via Collaboration between HEIs and Industry (Pitch-In). The authors thank this programme for their support.

Project lead

Dr Ronan Daly – University of Cambridge

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