Analysis of End-of-Life Process Data to enable Design Knowledge for Circularity

Doctoral thesis (2022)

The concept of Circular Economy (CE) is gaining increasing attention as an indispensable renewal of linear economy without neglecting sustainable development goals. Closing resource loops and keeping ... [more ▼]

The concept of Circular Economy (CE) is gaining increasing attention as an indispensable renewal of linear economy without neglecting sustainable development goals. Closing resource loops and keeping resources in the system at the highest level of use for as long as possible are cited as the main goals of CE. However, due to missing information exchange, the lack of consistency between the existing End-of-Life (EOL) infrastructure and the respective product designs hinders a successful circularity of resources. This research provides a modular method to collect, process, and apply EOL process data to provide the Beginning-of-Life (BOL) with important EOL-knowledge through a CE adapted product design assessment. EOL-data is collected using a Circular Value Stream Mapping (CVSM), EOL-information is processed using a digital state flow representation, and EOL-knowledge is applied by providing a graphical user interface for designers. The method is verified by a simulation model that serves as a decision-support tool for product designers in the context of a PET bottle case study in Luxembourg. The goal is to anticipate a circular flow of resources by reflectively aligning product design with the relevant EOL infrastructure. Within the linear economy, the focus has been on improving production processes while neglecting what happens to a product after its use. The developed method makes it possible to consider not only the requirements of users but also the actual end users, the EOL process chains, when designing products. [less ▲]

A Holistic Methodology to Deploy Industry 4.0 in Manufacturing Enterprises

Doctoral thesis (2022)

In the last decade, the manufacturing industry has seen a shift in the way products are produced due to the integration of digital technologies and existing manufacturing systems. This transformation is ... [more ▼]

In the last decade, the manufacturing industry has seen a shift in the way products are produced due to the integration of digital technologies and existing manufacturing systems. This transformation is often referred to as \textbf{Industry 4.0} (I4.0), which guarantees to deliver cost efficiency, mass customization, operational agility, traceability, and enable service orientation. To realize the potential of I4.0, integration of physical and digital elements using advanced technologies is a prerequisite. Large manufacturing companies have been embracing the I4.0 transformation swiftly. However, Small and Medium-sized Enterprises (SMEs) face challenges in terms of skills and capital requirements required for a smoother digital transformation. The goal of this thesis is to understand the features of a typical manufacturing SME and map them with the existing (e.g. Lean) and I4.0 manufacturing systems. The mapping is then used to develop a Self-Assessment Tool (SAT) to measure the maturity of a manufacturing entity. The SAT developed in this research has a critical SME focus. However, the scope of the SAT is not limited to SMEs and can be used for large companies. The analysis of the maturity of manufacturing companies revealed that the managerial dimensions of the companies are more mature than the technical dimensions. Therefore, this thesis attempts to fill the gap in technical dimensions especially Augmented Reality (AR) and Industrial Internet of Things (IIoT) through laboratory experiments and industrial validation. A holistic method is proposed to introduce I4.0 technologies in manufacturing enterprises based on maturity assessment, observations, technical road map, and applications. The method proposed in this research includes SAT, which measures the maturity of a manufacturing company in five categorical domains (\textbf{dimensions}): Strategy, Process and Value Stream, Organization, Methods and Tools, and Personnel. Furthermore, these dimensions are attributed to 36 modules, which help manufacturing companies measure their maturity level in terms of lean and I4.0. The SAT is tested in 100 manufacturing enterprises in Grande Région consisting of the pilot study (n=20) and maturity assessment (n=63). The observations from the assessment are then used to set up the technological road map for the research. AR and IIoT are the two technologies that are associated with the least mature modules, which are explored in depth in this thesis. A holistic method is incomplete without industry validation. Therefore, the above-mentioned technologies are applied in two manufacturing companies for further validation of the laboratory results. These applications include 1) the application of AR for maintenance and quality inspection in a tire manufacturing industry, and 2) the application of retrofitting technology for IIoT on a production machine in an SME. With the validated assessment model and the industrial applications, this thesis overall presents a holistic approach to introducing I4.0 technologies in manufacturing enterprises. This is accomplished through identifying the status of the company using maturity assessment and deriving the I4.0 roadmap for high potential modules. The skill gap in the addressed technologies is compensated by designing and testing prototypes in the laboratory before applying them in the industry. [less ▲]

Modeling and Control of Laser Wire Additive Manufacturing

Doctoral thesis (2022)

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing ... [more ▼]

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing process in the aerospace, automotive and other sectors. In the MAM, several parameters influence the product creation steps, making the MAM challenging. In this thesis, we modelize and control the deposition process for a type of MAM where a laser beam is used to melt a metallic wire to create the metal parts called the Laser Wire Additive Manufacturing Process (LWAM). In the dissertation, first, a novel parametric modeling approach is created. The goal of this approach is to use parametric product design features to simulate and print 3D metallic objects for the LWAM. The proposed method includes a pattern and the robot toolpath creation while considering several process requirements of LWAM, such as the deposition sequences and the robot system. This technique aims to develop adaptive robot toolpaths for a precise deposition process with nearly zero error in the product creation process. Second, a layer geometry (width and height) prediction model to improve deposition accuracy is proposed. A machine learning regression algorithm is applied to several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. The experimental results shows that the model has an error rate of (i.e., 2∼4%). Third, a physics-based model of the bead geometry including known process parameters and material properties was created. The model developed for the first time includes critical process parameters, the material properties and the thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate and the travel speed). The numerical results show a match of the model with the experimental measurements. Finally, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant, considering the constraints and the operating range of the parameters of the process inputs. The model simulation result shows an acceptable tracking of the reference height. [less ▲]

Adaptive model to increase resilience for emerging supply chains within the circular economs - 'Zirkelmesser' an innovative case study

in Procedia CIRP (2022)

Variations in quantities, qualities and time availability of input materials pose a major risk to circular supply chains (CSC) and require new models for creating and evaluating adaptive and resilient CSC ... [more ▼]

Variations in quantities, qualities and time availability of input materials pose a major risk to circular supply chains (CSC) and require new models for creating and evaluating adaptive and resilient CSC in the circular economy (CE). This can be achieved by consistently modelling the overarching relationship between resource input- and output streams, without neglecting the associated risks. The model proposed below consists of five components which are based on five resilience requirements for SCs and provides a data-based recommended course of action for managers at a low entry-barrier. It consists of a CSC visualization, a safety stock calculation, a risk monitoring for each SC node, a reporting logic, and a measurement catalogue. The inspiration for this model came from an innovative case study (“Zirkelmesser”) in the metal processing industry in which secondary products and materials are used to produce new products. Here, the problem of maintaining the resource supply arose and led to resilience issues. The mentioned case study is used as an application example for the model application and helps to make emerging circular supply chains predictable and better manageable, thus increasing their resilience. [less ▲]

Sustainability and Circular Economy in Learning Factories – Case Studies

in SSRN (2022, April 11)

Since the mitigation of climate change is one of the biggest challenges to face on a global scale, the topic has become more relevant also in industrial context. Learning factories have proven to be ... [more ▼]

Since the mitigation of climate change is one of the biggest challenges to face on a global scale, the topic has become more relevant also in industrial context. Learning factories have proven to be suitable environments to address and convey competencies to tackle industrial challenges in an interactive way. Hence, several learning factories are already dealing with sustainability topics in various use cases. This paper strives to present a state of the art of sustainability and circular economy in learning factories. Therefore, a classification framework is developed based on the state of the art of several learning factories and existing literature regarding the topic. This framework is then used to systematically describe the different activities regarding sustainability and circular economy that are currently ongoing in learning factories worldwide. This can be used to get an idea about the different aspects of the topic and how to address them, but furthermore also offers assistance to identify “blind spots” which could and should be addressed in learning factories in the future. [less ▲]

Retrofitting of legacy machines in the context of Industrial Internet of Things (IIoT)

in Procedia Computer Science (2022, February 08), 200

In the context of Industry 4.0 (I 4.0), one of the most important aspects is data, followed by the capital required to deploy advanced technologies. However, most Small and Medium Enterprises (SMEs) are ... [more ▼]

In the context of Industry 4.0 (I 4.0), one of the most important aspects is data, followed by the capital required to deploy advanced technologies. However, most Small and Medium Enterprises (SMEs) are neither data ready nor have the capital to upgrade their existing machinery. In SMEs, most of the legacy machines do not have data gathering capabilities. In this scenario, the concept of retrofitting the existing machinery with sensors and building an Industrial Internet of Things (IIoT) is more beneficial than upgrading the equipment to newer machinery. The current research paper proposes a simple architecture on retrofitting a legacy machine with external sensors for data collection and feeding the cloud-based databases for analysis/monitoring purposes. The design and functional aspects of the architecture are then tested in a laboratory environment on a drilling machine with no embedded sensors. Data related to the speed of the drill head and the bore depth are collected using newly retrofitted sensors to validate the proposed architecture. [less ▲]

LASER FUSION WELDING OF CU TO AL WITH SPIRAL TRAJECTORY AND MONITORING OF PROCESS SIGNALS

Doctoral thesis (2022)

Welding of Aluminium (Al) and Copper (Cu) in a dissimilar fashion is required for the manufacturing of solar thermal absorbers, battery modules and refrigeration applications. The high strength, thermal ... [more ▼]

Welding of Aluminium (Al) and Copper (Cu) in a dissimilar fashion is required for the manufacturing of solar thermal absorbers, battery modules and refrigeration applications. The high strength, thermal and electrical conductivity of Cu combined with the lightweight property of Al material enable the high performance of the product. A laser is a precise tool, which can increase the productivity and quality of the welding process. Welding Al and Cu is considered difficult because of the formation of complex intermetallic phases which reduce the strength of the joint. Laser brazing from low melting Al sheet to Cu sheet is the traditional technique to reduce the intermetallic phases. This thesis focuses on irradiation of laser beam from copper sheet to aluminium sheet in overlapped configuration. The approach is to form a large amount of intermixing to obtain (Cu) solid solution and Al-rich phase Al+Al2Cu in the interface. By this approach, it was found that a fusion zone with a large number of good phases was formed. The intermetallic compounds Al2Cu,Al3Cu4,Al4Cu9 are intermixed and small. Such a microstructure is beneficial for joint strength. The characterization was done by light optical microscopy and scanning electron microscope. EDS analysis was used to estimate the composition and identify the phases. It was found that a beneficial Cu solid solution phase is present in the joint. To qualify the joint and identify the weld status, melting characteristics during laser welding by observation of the optical emission in Ultraviolet-visible wavelength was studied. The Al melting peak at 396 nm and Cu melting peak at 578 nm was found to correlate to the welding process parameters. The signals correlate to the actual melting of Cu and Al sheets, which was investigated by the cross-sectional images and the weld images on the top of the Cu-Al weld. Therefore, the possibility for real-time analysis to identify different welding conditions is shown. [less ▲]

Simulation technology and application of safe collaborative operations in human-robot interaction

in Simulation Technology and Application of Safe Collaborative Operations in Human-Robot Interaction (2022)

Comparing software frameworks of Augmented Reality solutions for manufacturing

in Procedia Manufacturing (2021, November 03), 55

Augmented reality (AR) is a technology that allows overlaying of virtual elements on top of the physical environment. This enhances the perception and conveys additional information to the user. With the ... [more ▼]

Augmented reality (AR) is a technology that allows overlaying of virtual elements on top of the physical environment. This enhances the perception and conveys additional information to the user. With the emergence of industry 4.0 concepts in manufacturing landscape, AR found its way to improve existing Human-Machine Interfaces (HMI) on the shop-floor. The industrial setting has a wide variety of application opportunities from AR, ranging from training and digital work instructions to quality inspection and remote maintenance. Even though its implementation in the industry is rising in popularity, it is still mainly restricted to large companies due the limited availability of resources in Small and Medium Size Enterprises (SME). However, SMEs can benefit from AR solutions in its production processes. Therefore, this research aims to develop and present the results of comparison of two simple and cost-effective AR software frameworks for Hand Held Device (HHD) and a Head Mounted Device (HMD), which can be applied for developing AR applications for manufacturing. Two AR applications are developed using these software frameworks which are presented in the case study section. Android device is chosen as a HHD and HoloLens is the HMD used in the case study. The development structure can be reproduced by a wider range of enterprises with diverse needs and resource availability. [less ▲]

Bead Width Prediction in Laser Wire Additive Manufacturing Process

in Mbodj, Natago Guilé; Plapper, Peter (Eds.) Bead Width Prediction in Laser Wire Additive Manufacturing Process (2021, October 22)

In laser wire Additive manufacturing (LWAM), the final geometry is produced using layer-by-layer deposition principle of beads. To achieve good geometrical accuracy of the final product, proper ... [more ▼]

In laser wire Additive manufacturing (LWAM), the final geometry is produced using layer-by-layer deposition principle of beads. To achieve good geometrical accuracy of the final product, proper implementation of the bead geometry is essential. The process parameters have a direct influence on the bead geometry, thus to the printed part. In this paper, we propose a bead width prediction model to improve deposition accuracy. A regression algorithm is applied to the experimental results to predict the bead width dimension. Bead prediction equation relating the bead width growth for each layer is obtained for a given set of process parameters. The prediction equations show similar evolution trends and confirm the influence of deposition process parameters on the bead width. The proposed method demonstrates a prospective insight on a more proper selection of process or physical parameter intervening in laser wire additive manufacturing process. [less ▲]