# An Approach to Digitalising the Manufacturing Steps of Large-Scale Components by Using an Industrial Pilot Case

Kay Burow, Patrick Klein, Klaus-Dieter Thoben

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## To cite this version:

Kay Burow, Patrick Klein, Klaus-Dieter Thoben. An Approach to Digitalising the Manufacturing Steps of Large-Scale Components by Using an Industrial Pilot Case. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.183-189, 10.1007/978-3-031-16407-1_22. hal-05386072

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Submitted on 27 Nov 2025

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# An Approach to Digitalising the Manufacturing Steps of Large-Scale Components by Using an Industrial Pilot Case

Kay Burow<fnref n="1" />[✉]®, Patrick Klein<fnref n="1" />[✉], and Klaus-Dieter Thoben<fnref n="2" />[✉]

[bow, klp]@biba.uni-bremen.de

tho@biba.uni-bremen.de

**Abstract.** Although Industry 4.0 is a widespread word and is expected to be found everywhere, a deeper look shows that, depending on the application, its entire implementation throughout manufacturing is finite and has enormous potential for higher levels of integration.

Many complex parts and components are still fabricated and checked manually by workers, especially in large-scale components and vessels; these processes are far beyond digitalised input. With a specific pilot case in mind, the authors work on an approach to include data and implement this knowledge to digitalise the manufacturing process. Therefore, input data (e.g., requirements from the manufacturer, technical constraints, CAD files) are collected and allocated, and the fabrication status is checked.

This paper focuses on a digital mapping for the manufacturing of large-scale components to receive a digital thread, particularly an oil and gas piping vessel (industrial pilot case). The goal is to visualise a target-performance comparison for quality checking to locate missing auxiliary components and fabrication deviations. The individual steps shall be listed, connected and cross-linked to have a digital thread of the until now still manual process.

**Keywords:** Digitalisation · Digital thread · Large-scale components

## 1 Introduction

During the past last years, the terms Industry 4.0 and digitalisation have become more and more common. In sectors like automation, digital mapping, digital threads, and digital twins are typical tools [1]. Applications in logistics like the delivery service of Amazon are in a highly digital and flexible state. Individual orders and access to the supply chain up to intervening running orders are possible [2]. Large Enterprises (LE), especially in logistics, have a well-developed implementation towards digitalisation using interconnected services, data and information transfer and monitoring.

<footnote n="1">¹ BIBA – Bremer Institut für Produktion und Logistik GmbH, Hochschulring 20, 28359 Bremen, Germany</footnote>

<footnote n="2">² Institute for Integrated Product Development, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany</footnote>
For Industry 4.0, it is clear that for a high level of digitalisation, the processes must be transparent and connected (e.g. network, cloud-based); data and information exchange also must be done digitally.

Where digital twins picture a digital counterpart of physical objects or processes with all its connections and dependencies, a digital thread focuses on the traceability of a single set of related data between digital and physical worlds. Figure 1 shows the concept of a digital thread through an Engineering model throughout the entire lifecycle [3] and links the different steps together. Depending on the required information and its limited complexity, a digital thread can help better understand a product's related processes and data flows.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Engineering design with a Digital Thread [3]</figcaption></figure>

Nevertheless, for small and midsize enterprises (SME) and manufacturing chains with unique or single-part fabrication, the production processes are often only at the beginning of digitalisation. In addition, SMEs lack knowledge in converting their existing manual processes into a digital way. Converting is also a matter of efficiency and price.

Other sectors like Aerospace have their difficulties with the fabrication in only small numbers and extremely hard requirements in manufacturing, quality and documentation [4]. Also, for the manufacture of large-scale components, similar difficulties exist. Here, we must differentiate between the concept and transformation of a smart factory for mass production (e.g., large numbers of items) and the individual implementation of concrete customised components.

Handling and manufacturing large-scale components are still very challenging and lack digitalised processes, especially in combination with SMEs and their difficulties towards digitalisation [5].

In this paper, we consider the manufacturing processes of vessels for oil and gas piping as an example and later pilot case. These vessels (see Fig. 2) are massive in size and are partly manufactured by hand. In addition, the later quality check is also to be done manually, which means workers check every detail and attachment if they are in the proper position, angle and connected by measuring the fabricated model and checking/comparing it with the design model.

One difficulty is the small number of fabricated components, which are mostly unique and designed for one purpose on customer's demand. Also, different stakeholders work together, from the concept over the design phase to the manufactured model, and have to
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Large-scale component (vessel) for oil and gas piping [6]</figcaption></figure>

exchange data and information. To optimise this manual process, a digital thread that will map the different fabrication steps could help to receive a digital approach. Moreover, the data flows can be integrated (e.g., input, output), which can be transformed into a closed-loop for manufacturing.

## 2 Approach and Methodology

As stated in the previous section, depending on the field of application, manufacturing processes are partly still on the level of Industry 3.0, meaning a lot of work is done by paper and manually with digitalised processes just at the start [7].

Having a particular product in hand with the general focus on manufacturing of large-scale components, the idea is to develop a solution with a general approach. This is adopted for a particular pilot case on the one hand, and on the other hand, the general approach shall serve for transmission to other applications in large-scale manufacturing.

The first step is to analyse the existing manufacturing process and map it, including the flows of information (e.g. input/output data) and technical requirements from the manufacturer.

The general mapping is done by AutomationML, a neutral and XML-based data format that supports data storage and exchange. It further offers the possibility of integrating all kinds of information (e.g., values, files) [8]. Depending on the used software, APIs (application programming interface) allow to import and export data between AutomationML. For example, values in CAD files can be exported and changed in AutomationML.
Furthermore, for the pilot case, the focus lies in evaluating the fabricated component. This means that we need different information from different stakeholders (e.g., CAD model, 3D scan/point cloud, technical requirements). The explicit mapping concerns the pilot case; here, the quality check of a fabricated vessel. The main idea is to compare two models and analyse their deviations. Figure 3 shows the general idea, which can be divided into three parts:

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Simplified concept of the digital thread for the process chain as the specification for AutomationML</figcaption></figure>

* Design includes CAD files, technical drawings, models and technical requirements as well as manufacturing constraints

* Manufacturing includes the fabrication of components and a digital image of the model as a point cloud. Therefore, a 3D scan was made of the fabricated model and provided to us in a point cloud format; manufacturing can consist of multiple fabrication steps, depending on the complexity of the model and is done in iteration loops

* Quality control - the input data from the design phase are compared with the fabricated model, e.g., a point cloud model made by a scan with the help of the requirements

Therefore, two models are necessary. The first is the design model, the CAD model, and the second is the fabricated model. A manufacturer visualisation shall be achieved, which lists and highlights all deviations between the CAD model and point cloud (target-performance comparison). The list can be used for selected targets (deviations) and will show necessary examination as digital content.

## 3 Pilot Case: Visualisation for Quality Checking

In this section, a deeper look at details is done to understand our idea better. Connecting processes do not make them “smart”. It also depends on the correct information in the right place.

For the quality check, the first step is checking for eccentricity and whether the vessel is within tolerances. The next step during manufacturing is to attach all auxiliary components such as flanges, valves, mounts, etc. Every vessel is divided into sections; before two sections are connected/welded, a quality check is necessary. Workers do these
checks. They check each detail for position, angle, determent and proper placement by
measuring from a reference edge to every single component, also double-checking with
the bill of materials. An approach that is very time-consuming and expensive gives
freedom for failure due to tired workers of monotonous work and lack of concentration.

Our approach offers a solution to reduce the manual work to specific items. For
comparison and visualisation, we use open-source software. Here, we work with Cloud-
Compare [9], which includes all necessary tools for quality checks. Nevertheless, the
input data require preprocessing. CloudCompare demands specific import formats and
both models have to be in the same scale and units. Partly it is done via AutomationML
and the CAD software Inventor, which can convert and provide the required format and
possess an API for AutomationML.

The procedure is based on the concept shown in Fig. 3 and considers only quality
control. Input data is the CAD model, the 3D scan as a point cloud of the fabricated model
and the technical requirements. Also, the bill of materials is required for all the compo-
nents (e.g. pipes, flanges, mounts) attached to the main vessel from the manufacturing
phase.

The quality check is done in multiple steps but customised for this solution.

1. Preprocessing input data

2. Check and visualise for completeness

3. Check and visualise for deviations

4. Collect, list and allocate each position of 1 and 2.

5. Recirculation of all positions towards the manufacturer for further arrangements

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Comparison of CAD model and point cloud; Left: checking for missing parts (white: CAD model, red: point cloud); Right: Visualisation of deviations - green is within the tolerance, red above and blue below (Color figure online)</figcaption></figure>
As a basis for the pilot case, the AutomationML approach is used, and the single steps are implemented. After receiving all required information for the design and manufacturing, the quality check can be done and results in a list for the designer and manufacturer for further checking. Figure 4 shows exemplarily the results of the first two steps. Used was a partly fabricated vessel for the first examination. Instead of checking every single attachment, only the deviations have to be checked.

# 4 Conclusion

We found and described a way to depict and transform manual processes in a digital thread and work on a specific implementation through a pilot case. This digital thread will help improve the quality checking by reducing the effort of manual work. Further, digitalising the entire manufacturing process allows data and information to be easily integrated and exchanged between the stakeholders. So far, we can import, adapt and provide the input data for CloudCompare to start with the comparison algorithms. The connection between AutomationML and CloudCompare is the next big step.

# 5 Discussion and Outlook

Nevertheless, the project is still running, and a lot of work has to be done. Continuing work is the flow of information towards the AutomationML implementation to achieve a complete digital thread.

Parallel, the pilot line with its particular goal has to be completed with steps 4 and 5 to complete the circle of information and gives us the feedback loop.

Defining an API that links AutomationML with CloudCompare is a task that could not yet be achieved. The data is imported manually and exported as a list in an Excel file.

**Acknowledgements.** This work was supported by the European Union's Horizon 2020 research and innovation programme under the PENELOPE project under grant agreement No. 958303.

# References

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2. The Amazon's supply chain competitive advantage. http://www.strategylab.org/my-blog/the-amazons-supply-chain-competitive-advantage. Accessed 14 May 2022

3. Singh, V., Willcox, K.E.: Engineering Design with Digital Thread. AIAAJ (2018). https://doi.org/10.2514/1.J057255

4. Industry 4.0 within the Aerospace Sector: Prepare for Takeoff. https://manufacturing.cioreyview.com/cxoinight/industry-40-within-the-aerospace-sector-prepare-for-takeoff-nid-32049-cid-34.html. Accessed 14 May 2022
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7. Bakkari, M., Khatory A., Industry 4.0: Strategy for More Sustainable Industrial Development in SMEs, April 2017

8. What is AutomationML. https://www.automationml.org/about-automationml/automationml/. Accessed 14 May 2022

9. CloudCompare Homepage. https://www.cloudcompare.org. Accessed 14 May 20225