SUSTAINABLE RESOURCE UTILIZATION IN MANUFACTURING OF PRINTED CIRCUIT BOARD ASSEMBLY: EXERGY ANALYSIS OF THE PROCESS

Engineering for sustainable development requires prudent utilization of resources under economic, environmental and societal constraints. Resource utilization must follow a holistic approach. This brings in a need for comprehensive metrics which are simple, standard and universal. Thermodynamics may offer a metric that focuses on both quality and quantity of energy resources which may carry information to be combined with other metrics. This metric may be a thermodynamic property called exergy or available energy, which provides a better insight into resource use in both energy and non-energy producing systems. This thesis is devoted to a study of the exergy concept in manufacturing. A high volume PCB assembly, manufactured in a state of the art soldering facility is chosen for the study. Various mass and energy resources flowing through the production line were quantified in terms of exergy. On the basis of exergy content and exergy utilization in the production process, the sustainability in terms of resources use is discussed. An early version of this approach was presented at the International Symposium on Sustainable Systems and Technologies, IEEE, Washington DC, in May 2010

Measures from complexity science provide manufacturing companies with insights previously unavailable to them

"Operational effectiveness" is the means by which market leading value propositions are delivered to customers. The contribution of "operational effectiveness" in manufacturing to competitive strategy has been established. The "operational effectiveness" approach has been based on scientific reductionism, defining itself by list of "best practice" tools, techniques and philosophies. This thesis argues that this had led to loss of the "operational effectiveness" whole, causing variegated success in application of these tools. Complex systems SCience, with its origins in communications, control theory and non-linear dynamical systems, has provided previously unavailable insight into real-world systems. The applications to date in manufacturing have been in soft systems appearing either as metaphors or computationally difficult optimisation-type problems. This thesis argues that manufacturing companies are complex systems. As a result measures for complex behaviour and structure in these systems will yield fresh insight currently unavailable through the reductionist worldview of "operational effectiveness". This research proposes a new framework of agents that connects the "operational effective" and complex systems views, without the loss of system richness of the complexity worldview. This framework comprises interrelated sets of input/output Entities, produced through Activities carried out by Resources. Novel measures of flow efficacy and the structure of these set-based relationships are then obtained based on this common framework by the use of Entropy and QanalYSis respectively. A methodology for the creation of the framework, its measurement and validation is then proposed and tested. Three case studies have been carried out in the actual processes of a manufacturing company. The studies have been able to show a consistent benefit from the use of complexity-based measures over and above what would have been available from "operational effectiveness. Correlation and independence have been observed in the concurrent use of both complexity measures on the frameworks, demonstrating their independence and interdependence

Acrylonitrile Butadiene Styrene Hybrid Fuel with Radially Azimuthally Partitioned Paraffin Cells

Additively manufactured fuels are becoming more common in the area of hybrid rockets due to the enhanced possibilities provided by computer aided design and improved additive material technology. When integrated with a highly compliant yet energetic paraffin wax, the additive manufactured material can help support the paraffin wax during the burn, and improve overall performance. This study investigates thin-walled acrylonitrile butadiene styrene structures that separate paraffin wax into azimuthally partitioned cells. The fuel grains are tested using a vertical test stand, custom nitrous system, and data acquisition system. The computer program Chemical Equilibrium with Applications is used to compare common hybrid fuels such as sorbitol, polybutadiene acrylic acid acrylonitrile, and poly(methyl methacrylate) along with the manufactured fuel. The experimental results indicate the promise of higher performance using paraffin. The analyses, however, show that refinements in grain design are necessary to fully realize the advantages of paraffin

Magnetic field assisted milli-scale robotic assembly machine: an approach to massively parallel swarm robotic automation systems

The vision of highly parallel, automated manufacturing systems that can build macroscopic products by heterogeneous assembly of many small devices will have a major impact in manufacturing. In this study, a novel milli-scale robotic assembly machine with parallel capabilities, assisted with programmable magnetic field, is developed. The machine prototype consists of a 16x16 array of electromagnets. The dimensions of the electromagnets are 5mm high with an inner diameter of 1.1mm and outer diameter of 2.5mm. All the electromagnets are driven by a 16x16 array of H-Bridges, and an Arduino microcontroller is used to control and program the arrays. Using the machine to manipulate up to nine milli-scale robots simultaneously is demonstrated. The robot is designed with a 3x3 electromagnets array to operate and it consists of two parts: a polycarbonate chassis and five grade N42 NdFeB permanent magnets located at four corners and center of the chassis. The capability of pick-and-place millimeter size devices, such as SMD (Surface Mounted Device) LEDs (Light Emitting Diodes), specifically 0805 LEDs, is demonstrated by using the prototype machine. A milli-scale tweezer is designed using AutoCAD Fusion 360 and simulated with COMSOL Multiphysics. The milli-scale tweezer is fabricated using a home-built Computer Numerical Control (CNC) machine. The tweezer is subsequently mounted to the robots. For proof-of-concept, simultaneous operation for pick-and-place two LEDs is carried out by two milli robots. Furthermore, an 8x8 LED array is assembled by operating a single robot, which proves the potential capability of assembling an LED screen with the presented technology. The problems and challenges as well as the future outlook are discussed in the last chapter

Statistical Techniques and Artificial Neural Networks for Image Analysis

The main topic of this PhD thesis is image analysis. The subject was investigated from different perspectives, starting from different image types and with different goals. At the beginning x-ray hazelnuts images were analyzed. The target of this analysis was to determine whether a hazelnut was good or not. In order to do this an Artificial Neural Network was used, whose features were statistical variables. At a later stage a SVM was implemented to try to get better results. The second kind of images were still x-ray ones; they were, however, images coming from a PCB productive process. What we were asked to do was to highlight the air bubbles trapped into a solder joint (particularly those inside the thermal pads). In this case filters and morphological operations were used. The third case were ulcers photographs: the goal of the collaboration with SIF (Società Italiana di Flebologia, Italian society of phlebology) is to give doctors a way to evaluate ulcers remotely, in order to customize the treatments according to how the healing behaves. A small digression was the development of a small and cheap Arduino-based robot for an educational laboratory (Xkè?, in collaboration with prof. Angelo Raffaele Meo from DAUIN, Politecnico di Torino). This should have been the first step towards the development of an evolved robot for agricultural purposes, but the project didn’t start

Secure CAN logging and data analysis

2020 Fall.Includes bibliographical references.Controller Area Network (CAN) communications are an essential element of modern vehicles, particularly heavy trucks. However, CAN protocols are vulnerable from a cybersecurity perspective in that they have no mechanism for authentication or authorization. Attacks on vehicle CAN systems present a risk to driver privacy and possibly driver safety. Therefore, developing new tools and techniques to detect cybersecurity threats within CAN networks is a critical research topic. A key component of this research is compiling a large database of representative CAN data from operational vehicles on the road. This database will be used to develop methods for detecting intrusions or other potential threats. In this paper, an open-source CAN logger was developed that used hardware and software following the industry security standards to securely log and transmit heavy vehicle CAN data. A hardware prototype demonstrated the ability to encrypt data at over 6 Megabits per second (Mbps) and successfully log all data at 100% bus load on a 1 Mbps baud CAN network in a laboratory setting. An AES-128 Cipher Block Chaining (CBC) encryption mode was chosen. A Hardware Security Module (HSM) was used to generate and securely store asymmetric key pairs for cryptographic communication with a third-party cloud database. It also implemented Elliptic-Curve Cryptography (ECC) algorithms to perform key exchange and sign the data for integrity verification. This solution ensures secure data collection and transmission because only encrypted data is ever stored or transmitted, and communication with the third-party cloud server uses shared, asymmetric secret keys as well as Transport Layer Security (TLS)

Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation

Interacting with the environment using hands is one of the distinctive abilities of humans with respect to other species. This aptitude reflects on the crucial role played by objects\u2019 manipulation in the world that we have shaped for us. With a view of bringing robots outside industries for supporting people during everyday life, the ability of manipulating objects autonomously and in unstructured environments is therefore one of the basic skills they need. Autonomous manipulation is characterized by great complexity especially regarding the processing of sensors information to perceive the surrounding environment. Humans rely on vision for wideranging tridimensional information, prioprioception for the awareness of the relative position of their own body in the space and the sense of touch for local information when physical interaction with objects happens. The study of autonomous manipulation in robotics aims at transferring similar perceptive skills to robots so that, combined with state of the art control techniques, they could be able to achieve similar performance in manipulating objects. The great complexity of this task makes autonomous manipulation one of the open problems in robotics that has been drawing increasingly the research attention in the latest years. In this work of Thesis, we propose possible solutions to some key components of autonomous manipulation, focusing in particular on the perception problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information to be processed for inferring how to interact with objects. The object modeling and grasping pipeline based on superquadric functions we designed meets this need, since it reconstructs the object 3D model from partial point cloud and computes a suitable hand pose for grasping the object. Retrieving objects information with touch sensors only is a relevant skill that becomes crucial when vision is occluded, as happens for instance during physical interaction with the object. We addressed this problem with the design of a novel tactile localization algorithm, named Memory Unscented Particle Filter, capable of localizing and recognizing objects relying solely on 3D contact points collected on the object surface. Another key point of autonomous manipulation we report on in this Thesis work is bi-manual coordination. The execution of more advanced manipulation tasks in fact might require the use and coordination of two arms. Tool usage for instance often requires a proper in-hand object pose that can be obtained via dual-arm re-grasping. In pick-and-place tasks sometimes the initial and target position of the object do not belong to the same arm workspace, then requiring to use one hand for lifting the object and the other for locating it in the new position. At this regard, we implemented a pipeline for executing the handover task, i.e. the sequences of actions for autonomously passing an object from one robot hand on to the other. The contributions described thus far address specific subproblems of the more complex task of autonomous manipulation. This actually differs from what humans do, in that humans develop their manipulation skills by learning through experience and trial-and-error strategy. Aproper mathematical formulation for encoding this learning approach is given by Deep Reinforcement Learning, that has recently proved to be successful in many robotics applications. For this reason, in this Thesis we report also on the six month experience carried out at Berkeley Artificial Intelligence Research laboratory with the goal of studying Deep Reinforcement Learning and its application to autonomous manipulation