Assignment & Internship

Are you an enthusiastic student looking for a challenging internship or graduation assignment in the manufacturing industry in Twente? Van Den Bos Corrugated Machinery (CM) is looking for you to assist us in modernising our development processes!

As part of the PARADAIM consortium, Van Den Bos CM is developing its own Virtual Factory Suite: A virtual environment to test large conveyor systems in action, before they physically exist! With this suite, we hope to make our software more robust, while reducing the commissioning time at our customers. In addition, we believe that a 3D visualisation of the transport flow can add value during the initial consultations with our customers.

Although the virtual factory will inherently offer visual insight into our transportation systems, the true power of this suite is to obtain and process large amounts of realistic transport data under a wide variety of conditions. This is typically not feasible on location, let alone with the flexibility and speed that the virtual factory can. Therefore, we are looking for an intern or graduating student that wants tackle this big data challenge and turn the simulated scenario into meaningful Key Performance Indices (KPIs)! This would involve getting familiar with our simulation software and explanding it with your own Python add-ons. Of course, you will not do this alone! In our office, you will work in close collaboration with our sales- and software engineers.

Are you an enthusiastic student looking for a challenging internship or graduation assignment in the manufacturing industry in Twente? Van Den Bos Corrugated Machinery (CM) is looking for you to assist us in modernising our development processes!

As part of the PARADAIM consortium, Van Den Bos CM is developing its own Virtual Factory Suite: A virtual environment to test large conveyor systems in action, before they physically exist! With this suite, we hope to make our software more robust, while reducing the commissioning time at our customers. In addition, we believe that a 3D visualisation of the transport flow can add value during the initial consultations with our customers.

To build our virtual factory, we will use a digital factory layout plan created by our sales department in cooperation with the customer. 3D models with internal flow logic can then be placed on their appropriate positions, based on the layout plan. To avoid duplicate work while ensuring the virtual factory is as user-friendly as possible, we are looking for an intern or graduate student to assist in the automation of this translation step! This includes creating customised widgets and add-ons in Python for existing simulation software. Of course, you will not do this alone! In our office, you will work in close collaboration with our sales- and software engineers.

Are you an enthusiastic student looking for a challenging internship or graduation assignment in the manufacturing industry in Twente? Van Den Bos Corrugated Machinery (CM) is looking for you to assist us in modernising our development processes!

As part of the PARADAIM consortium, Van Den Bos CM is developing its own Virtual Factory Suite: A virtual environment to test large conveyor systems in action, before they physically exist! With this suite, we hope to make our software more robust, while reducing the commissioning time at our customers. In addition, we believe that a 3D visualisation of the transport flow can add value during the initial consultations with our customers.

The building blocks of our Virtual Factory Suite will be parametric transport blocks: plug- and-play 3D models with actuators, sensors and internal logic that can mimic the behaviour of our systems on-site. The sales team will then use these blocks to simulate factory flow, while software engineers can use the blocks to test their industrial controllers. We are looking for an enthusiastic intern/graduate who wants to help us bring this library to life in a modular way. Of course, you will not do this alone! In our office, you will work in close collaboration with our.

Are you an enthusiastic student looking for a challenging internship or graduation assignment in the industrial automation industry? Van Den Bos Corrugated Machinery (CM) in Almelo is looking for you to assist us in modernising our development processes!

As part of the PARADAIM consortium, Van Den Bos CM is developing its own Virtual Factory Suite: A virtual environment to test large conveyor systems in action, before they physically exist! With this suite, we hope to make our software more robust, while reducing the commissioning time on-site at our customers. In addition, we believe that a 3D visualisation of the transport flow can add value during the initial consultations with our customers.

Therefore, we are looking for an enthousiastic interns / graduating engineers who want to collaborate in taking this project to the next level! Practically, this means (partly) automating the virtual commissioning process. Key challenges you could work on include:

- Defining a set of variables which characterize a ‘bugged’ virtual factory state,

- Creating an anomoly detection algorithm to identify ‘bugged’ states during simulation,

- Designing & implementing an autonomous ‘adversarial’ algorithm, which tries to ‘bug

out’ the virtual environment by creating critical factory states.

- Evaluating the detection- & ‘adversarial’ algorithms in comparison to manual inputs.

Of course, you will not do this alone! In our office, you will work in close collaboration with our engineers.

An ageing workforce with more demanding skill requirements has altered the profile of the industrial worker, making evident the need for the creation of inclusive and adaptable workplaces. Within this context, considering the synergies between Industry 4.0 & 5.0 brings together new opportunities through a technological perspective; thus, enabling the development of adaptable and flexible workplaces in the context of Cyber physical Systems (CPS).

This assignment will focus on the development of a decision support systems for human-operated workplaces based on real industrial data. As part of the research, you will work in cooperation with the shop floor operation of a bicycle manufacturer where motion capture (Mocap) data of specific assembly workstations will be utilized. Through the use of digital human model simulation tools, this project aims at integrating Mocap data into a realistic simulation model depicting the real process. In the next stage, machine learning techniques will be applied to scale-up the simulation layer by creating a surrogate model with prediction capabilities. The goal of this integrated approach is to support the reconfiguration of a real industrial process, thus enabling the accommodation of workers with varying anthropometric characteristics.

Primary supervisor: Dr. Sri Kolla | s.kolla@utwente.nl

Daily supervisor: Victor Bittencourt | v.bittencourtlima@utwente.nl

Background

The transition to high-performance, sustainable battery technologies—such as those employing silicon-based anodes, lithium-metal systems, and solid-state electrolytes—requires precise mechanical and thermal control during testing. This is a critical process in the overall automated serial production and assembly of batteries, to ensure adherence to strict quality control process required in industry.

One critical parameter for reliable and reproducible cell performance is the application of uniform and constant mechanical pressure. Proper pressure management ensures optimal contact between cell layers, reduces internal resistance, and enables full exploitation of the electrochemical performance and cycle life of advanced battery materials. In parallel, real-time monitoring of mechanical, thermal, and electrochemical conditions during testing can significantly enhance understanding of material behavior and degradation mechanisms, aiding in the development of next-generation batteries.

Objective

The objective of this Master's thesis is to design, prototype, and evaluate a modular and reusable cell holder for lithium-ion pouch cells that applies a defined and constant pressure during long-term electrochemical testing. The holder should also integrate relevant sensors (e.g., force, temperature, displacement) to enable real-time monitoring of test conditions and cell behavior.

Background

Modern battery production involves a sequence of tightly linked processes: from electrode coating and drying to cell assembly and formation. Each step generates valuable information about process parameters and intermediate product features. However, this data is often fragmented across machines, measurement setups, and manual lab characterization.
To enable efficient process optimization and quality control, a coherent system-level data architecture is necessary, one that connects machine data, material characterization, and product quality information throughout the entire manufacturing line.

Objective
The goal of this master's assignment is to develop the first concept of a system-level data architecture, using the new battery production line at the University of Twente as a case study. The work focuses on identifying relevant process parameters and product features, understanding how data is currently generated and stored, and designing a consistent structure for data collection, linking, and accessibility across different production steps.

Tasks

·       Mapping the production line and identifying all relevant process steps, machines, and measurement systems.

·       Identifying all key process parameters (e.g., temperature, coating speed, pressure) and product features (e.g., thickness, porosity, adhesion).

·       Understanding and evaluation of the current data type and formats, acquisition systems, and storage methods.

·       Developing a conceptual data architecture for the system.

·       Modeling and analyzing the data (e.g., linking time-series data to material and batch identifiers).

·       Developing the Integration roadmap and proposing how the architecture can be scaled toward a complete manufacturing line.

Background

Silicon has become an important material for a number of industries, including semiconductor manufacturing. These industries produce huge quantities of silicon waste, both in production and end of life of the product. For sustainable reasons, scientists are exploring the use of these wastes to produce silicon anode used in battery systems. However, environmental impact and cost implications of the entire process to recover the silicon kerf and produce functional silicon anode has not been clearly quantified. Furthermore, the optimal parametrization of the processes to reduce the environmental impact and cost without compromising the quality of the anode, has not been thoroughly investigated.

Description

This exciting MSc assignment assesses cost and environmental impact of producing silicon anodes from silicon kerf at laboratory scale. Wealthy of multidisciplinary knowledge and skills will be explored and you will take an active role by having hands on experience with various manufacturing processes and equipment, as well as research exposure. Figure below depicts an overview of the series of activities and related equipment to be carried out and experienced.

Aim

The assignment aims to establish the optimal procedure for producing silicon anode using silicon kerf from the semiconductor industry. You will run laboratory experiments with different set of parameters throughout the manufacturing process chain, which also include comparing conventional approach with addictive manufacturing techniques to produce the anode.

Tasks

The MSc assignment will be carried out through the following tasks:

1.     Review literature on silicon kerf, anode manufacturing process chain, additive manufacturing in electrode production and battery cell performance.

2.     Develop experiment design (set of different parameters) for the ball milling process and anode production (including through additive manufacturing).

3.     Support in the production of silicon anodes through conventional and additive manufacturing approaches.

4.     Collect data and analyze the results from the different experiments .

Research objectives

The research objectives of this MSc assignment are:

·       To investigate how different set of ball milling parameters affect the obtained particle sizes.

·       To assess the energy consumption in each process step of the anode production and how they vary based on the experiments.

·       To derive general insights on which experiment is most optimal based on cost, environmental impact and cell performance.

Courses and supervision

This assignment is available for Master’s Mechanical Engineering and you will be supported by Shun Yang on sustainable manufacturing systems, while Davoud Jafari will guide the aspects on additive manufacturing. Furthermore, you will work alongside a member of the electrochemical laboratory, who will support with the anode production and testing.

Background

Circular Economy (CE) strategies such as reuse, remanufacturing or recycling depend strongly on the ability to accurately assess the condition of returned products and components during disassembly. In industrial practice, disassembly is often manual, experience-based, and prone to variability, which can lead to inconsistent circular decision-making.

Digital assistance systems such as Augmented Reality based worker instructions (ARKITE), have shown potential in supporting manufacturing operations such as assembly and disassembly. However, their application has largely focused on task guidance, while their use in disassembly assessment and circular decision support remains limited. Furthermore, there is a lack of studies evaluating such systems across different product types, which is essential for understanding their broader applicability and limitations in circular manufacturing systems.

Description

This MSc assignment evaluates the use of ARKITE to support disassembly assessment of components in a circular manufacturing context. The project adopts a multi-case study approach, considering three different products: a mini-drone, a stepper machine, and a battery pack. These products represent diverse characteristics in terms of size, complexity, and component criticality.

The ARKITE is applied to inspect and classify components according to circular economy strategies. Rather than focusing on product-specific optimization, the assignment aims to identify common principles, requirements, and limitations of digital disassembly support that are applicable across different product types.

Objectives

The main objective of this project is to evaluate the effectiveness and applicability of digital assistance systems for disassembly assessment and circular decision-making across different types of manufactured products.

A secondary objective is to develop disassembly workflows for Circular Economy (CE) strategies based on the product characteristics.

Tasks

The MSc assignment will be carried out through the following tasks:

1. Review literature on circular manufacturing, disassembly inspection, and digital assistance systems.
2. Characterize the three products (mini-drone, stepper machine, battery pack).
3. Define generic disassembly inspection requirements.
4. Design a digital-assisted disassembly worker instructions using an ARKITE.
5. Implement and test the workflow for each product in a Learning Factory environment.
6. Collect and analyze disassembly KPIs across the different case studies.

Research objectives

The research objectives of this MSc assignment are:

• To investigate how digital assistance systems can support disassembly assessment across different product types.
• To evaluate the impact of digital support on circular decision-making.
• To analyze how product characteristics influence digital support effectiveness.
• Derive general insights for digital disassembly support in circular manufacturing beyond the specific case studies.

Courses and supervision

This assignment is available for Master’s Mechanical Engineering and the student will be guided by Shun Yang on sustainable manufacturing systems part of the assignment, while Sri Kolla will guide the aspects on the digital assistance systems side of the assignment.

It is advised that the student has passed or has knowledge regarding the following courses:

• Sustainability in Manufacturing
• Sustainable Cyber Physical Production Systems.