Analysis and improvements of an existing industrial hot stamping machine

The importance of product presentation in the marketing industry is well known. Labels are crucial for providing information to the buyer, but at a modest additional expense, a beautiful label with exquisite embellishments may also give the goods a sensation of high quality and elegance. Enhancing the capabilities of stamping machines is required to keep up with the increasing velocity of the production lines in the modern manufacturing industry and to offer new opportunities for customization. It’s in this context of improvements and refinements that this work takes place. The thesis was developed during an internship at Studio D, the firm that designs the mechanics of the machines produced by Cartes. The The aim of this work is to study possible upgrades for the existing hot stamping machines. The main focus of this work is centred on two objectives: first, evaluating the pressing forces generated by this machine and characterising how the mat used in the stamping process reacts to such forces. Second, propose a new conformation for the press mechanism in order to improve the rigidity and performance of the machines. The first objective is reached through a combined approach: the mat is crudely characterized with experimental data, while the frame of the machine is studied through FEM analysis. The results obtained are combined and used to upgrade a worksheet that allows to estimate the forces exerted by the machines. The second objective is reached with the proposal of new, improved designs for the main components of the machines.

Characterization Of Single Use Reactor Used For The Manufacturing Of Formulated Products Within The Pharmaceutical Industry

Mixing is a fundamental unit operation in the pharmaceutical industry to ensure consistent product quality across different batches. It is usually carried out in mechanically stirred tanks, with a large variety of designs according to the process requirements. A key aspect of pharmaceutical manufacturing is the extensive and meticulous cleaning of the vessels between runs to prevent the risk of contamination. Single-use reactors represent an increasing trend in the industry since they do not require cleaning and sterilization, reducing the need for utilities such as steam to sterilize equipment and the time between production batches. In contrast to traditional stainless steel vessels, single-use reactors consist of a plastic bag used as a vessel and disposed of after use. This thesis aims to characterize the fluid dynamics features and the mixing performance of a commercially available single-use reactor. The characterization employs a combination of various experimental techniques. The analysis starts with the visual observation of the liquid behavior inside the vessel, focusing on the vortex shape evolution at different impeller speeds. The power consumption is then measured using a torque meter to quantify the power number. Particle Image Velocimetry (PIV) is employed to investigate local fluid dynamics properties such as mean flow field and mean and rms velocity profiles. The same experimental setup of PIV is exploited for another optical measurement technique, the Planar Laser-Induced Fluorescence (PLIF). The PLIF measurements complete the characterization of the reactor with the qualitative visualization of the turbulent flow and the quantitative assessment of the system performance through the mixing time. The results confirm good mixing performances for the single-use reactor over the investigated impeller speeds and reveal that the filling volume plays a significant role in the fluid dynamics of the system.

Computational design of a new type of sandwich cross-section for a defined solution of a tree-like column produced by wire-and-arc additive manufacturing

When it comes to designing a structure, architects and engineers want to join forces in order to create and build the most beautiful and efficient building. From finding new shapes and forms to optimizing the stability and the resistance, there is a constant link to be made between both professions. In architecture, there has always been a particular interest in creating new shapes and types of a structure inspired by many different fields, one of them being nature itself. In engineering, the selection of optimum has always dictated the way of thinking and designing structures. This mindset led through studies to the current best practices in construction. However, both disciplines were limited by the traditional manufacturing constraints at a certain point. Over the last decades, much progress was made from a technological point of view, allowing to go beyond today's manufacturing constraints. With the emergence of Wire-and-Arc Additive Manufacturing (WAAM) combined with Algorithmic-Aided Design (AAD), architects and engineers are offered new opportunities to merge architectural beauty and structural efficiency. Both technologies allow for exploring and building unusual and complex structural shapes in addition to a reduction of costs and environmental impacts. Through this study, the author wants to make use of previously mentioned technologies and assess their potential, first to design an aesthetically appreciated tree-like column with the idea of secondly proposing a new type of standardized and optimized sandwich cross-section to the construction industry. Parametric algorithms to model the dendriform column and the new sandwich cross-section are developed and presented in detail. A catalog draft of the latter and methods to establish it are then proposed and discussed. Finally, the buckling behavior of this latter is assessed considering standard steel and WAAM material properties.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.