Thesis (doctoral)--Technische Universiteit Delft, 1998. / Summary also in Dutch. Includes bibliographical references.

Thesis (M.Soc.Sc.)--University of Hong Kong, 1989. / Also available in print.

Thesis (Ph. D.)--University of Hong Kong, 2005.

Thesis (Ph.D.)--Boston University / The Boston Manufacturing Company was established on the Charles River in Waltham, Massachusetts, in 1813. It was America's first modern factory not because it first put the processes of carding, spinning and weaving under one roof, as has often been stated, but because it first put all these processes to work by power. The company had t1velve original proprietors; the three most important were Francis Cabot Lowell, Nathan Appleton, and Patrick Tracy Jackson, all Boston merchants who had made fortunes in commerce and who sought new fields for investment when the War of 1812 made shipping unprofitable. Lowell was especially influential, for in 1811 he visited English factories and memorized plans for a power loom--export of textile machinery and emigration of mechanics was strictly prohibited by British law. To superintend its machine shop, the new company fortunately found Paul Moody. His mechanical genius gave the industry many improvements and several inventions. His shop became a "school for mechanics" and, although the company tried to prevent it, many of the workers stayed only long enough to learn, before answering the huge demand for Waltham-trained men in other factories. [TRUNCATED]

James Womack and his colleagues Daniel Jones and Daniel Roos changed the way western civilization approached manufacturing. In 1990, they published a book called ‘The Machine That Changed the World: The Story of Lean Production’. It was a concept that had slowly filtered from the east but had not made its mark on the manufacturing sector. The concept of lean, born out of the Japanese Toyota Manufacturing System, was first thought to be impossible to duplicate outside of Japan. Since Womack and company popularised this “new” way of producing goods and delivering services it spread across industries finding popularity in the medical, engineering, accounting and especially the manufacturing industries. Over the last few decades lean practices has been synonymous with efficiency, cost reduction, supply chain optimisation and innovative problem solving (Anvari Norzima, Rosnah, Hojjati and Ismail, 2010; Pieterse et al., 2010; Womack et al., 1990). Lean process implementation has been researched in abundance, as has failed attempts at lean implementation. The purpose of this study was to identify and assess enablers of lean sustainability in organisations where lean processes are already being implemented. The literature study found Organisational Culture, Leadership, Employee Engagement and Trade Unions participation as factors that contributed to successful lean implementations. The author developed a model to test Organisational Culture, Leadership, Employee Engagement and Trade Unions as enablers to sustain lean practices in organisations in South Africa’s manufacturing industries. The results proved that Organisational Culture, Leadership and Employee Engagement were considered enablers for lean sustainability. These three enablers have an interlinked relationship and together help sustainability. Lacking just one factor would surely result in unsustainable lean practices. The study was conducted in the quantitative paradigm, as the hypothesised relationship was statistically tested. The data was collected from a homogenous group via an email sent with a link to the questionnaire. The data was statistically analysed with Statistica software and Microsoft Excel.

No description available.

Globally distributed additive manufacturing is a relatively new frontier in the field of product lifecycle management. Designers are independent of additive manufacturing services, often thousands of miles apart. Manufacturing data must be transmitted electronically from designer to manufacturer to realize the benefits of such a system. Unalterable blockchain legers can record transactions between customers, designers, and manufacturers allowing each to trust the other two without needing to be familiar with each other. Although trust can be established, malicious printers or customers still have the incentive to produce unauthorized or pirated parts. To prevent this, machine instructions are encrypted and electronically transmitted to the printing service, where an authorized printer decrypts the data and prints an approved number of parts or products. The encrypted data may include G-Code machine instructions which contain every motion of every motor on a 3D printer. Once these instructions are decrypted, motor drivers send control signals along wires to the printer's stepper motors. The transmission along these wires is no longer encrypted. If the signals along the wires are read, the motion of the motor can be analyzed, and G-Code can be reverse engineered. This thesis demonstrates such a threat through a simulated attack on a G-Code controlled device. A computer running a numeric controller and G-Code interpreter is connected to standard stepper motors. As G-Code commands are delivered, the magnetic field generated by the transmitted signals is read by a Hall Effect sensor. The rapid oscillation of the magnetic field corresponds to the stepper motor control signals which rhythmically move the motor. The oscillating signals are recorded by a high speed analog to digital converter attached to a second computer. The two systems are completely electronically isolated. The recorded signals are saved as a string of voltage data with a matching time stamp. The voltage data is processed through a Matlab script which analyzes the direction the motor spins and the number of steps the motor takes. With these two pieces of data, the G-Code instructions which produced the motion can be recreated. The demonstration shows the exposure of previously encrypted data, allowing for the unauthorized production of parts, revealing a security flaw in a distributed additive manufacturing environment. / Master of Science / Developed at the end of the 20th century, additive manufacturing, sometimes known as 3D printing, is a relatively new method for the production of physical products. Typically, these have been limited to plastics and a small number of metals. Recently, advances in additive manufacturing technology have allowed an increasing number of industrial and consumer products to be produced on demand. A worldwide industry of additive manufacturing has opened up where product designers and 3D printer operators can work together to deliver products to customers faster and more efficiently. Designers and printers may be on opposite sides of the world, but a customer can go to a local printer and order a part designed by an engineer thousands of miles away. The customer receives a part in as little time as it takes to physically produce the object. To achieve this, the printer needs manufacturing information such as object dimensions, material parameters, and machine settings from the designer. The designer risks unauthorized use and the loss of intellectual property if the manufacturing information is exposed. Legal protections on intellectual property only go so far, especially across borders. Technical solutions can help protect valuable IP. In such an industry, essential data may be digitally encrypted for secure transmission around the world. This information may only be read by authorized printers and printing services and is never saved or read by an outside person or computer. The control computers which read the data also control the physical operation of the printer. Most commonly, electric motors are used to move the machine to produce the physical object. These are most often stepper motors which are connected by wires to the controlling computers and move in a predictable rhythmic fashion. The signals transmitted through the wires generate a magnetic field, which can be detected and recorded. The pattern of the magnetic field matches the steps of the motors. Each step can be counted, and the path of the motors can be precisely traced. The path reveals the shape of the object and the encrypted manufacturing instructions used by the printer. This thesis demonstrates the tracking of motors and creation of encrypted machine code in a simulated 3D printing environment, revealing a potential security flaw in a distributed manufacturing system.

A cyber manufacturing system (CMS) is a concept generated from the cyber-physical system (CPS), providing adequate data and computation resources to support efficient and optimal decision making. Examples of these decisions include production control, variation reduction, and cost optimization. A CMS integrates the physical manufacturing equipment and computation resources via Industrial Internet, which provides low-cost Internet connections and control capability in the manufacturing networks. Traditional quality engineering methodologies, however, typically focus on statistical process control or run-to-run quality control through modeling and optimization of an individual process, which makes it less effective in a CMS with many manufacturing systems connected. In addition, more personalization in manufacturing generates limited samples for the same kind of product designs, materials, and specifications, which prohibits the use of many effective data-driven modeling methods. Motivated by Additive Manufacturing (AM) with the potential to manufacture products with a one-of-a-kind design, material, and specification, this dissertation will address the following three research questions: (1) how can in situ data be used to model multiple similar AM processes connected in a CMS (Chapter 3)? (2) How to improve the accuracy of the low-fidelity first-principle simulation (e.g., finite element analysis, FEA) for personalized AM products to validate the product and process designs (Chapter 4) in time? (3) And how to predict the void defect (i.e., unmeasurable quality variables) based on the in situ quality variables. By answering the above three research questions, the proposed methodology will effectively generate in situ process and quality data for modeling multiple connected AM processes in a CMS. The research to quantify the uncertainty of the simulated in situ process data and their impact on the overall AM modeling is out of the scope of this research. The proposed methodologies will be validated based on fused deposition modeling (FDM) processes and selective laser melting processes (SLM). Moreover, by comparing with the corresponding benchmark methods, the merits of the proposed methods are demonstrated in this dissertation. In addition, the proposed methods are inherently developed with a general data-driven framework. Therefore, they can also potentially be extended to other applications and manufacturing processes. / Doctor of Philosophy / Additive manufacturing (AM) is a promising advanced manufacturing process that can realize the personalized products in complex shapes with unprecedented materials. However, there are many quality issues that can restrict the wide deployment of AM in practice, such as voids, porosity, cracking, etc. To effectively model and further mitigate these quality issues, the cyber manufacturing system (CMS) is adopted. The CMS can provide the data acquisition functionality to collect the real-time process data which directly or indirectly related to the product quality in AM. Moreover, the CMS can provide the computation capability to analyze the AM data and support the decision-making to optimize the AM process. However, due to the characteristics of AM process, there are several challenges effectively and efficiently model the AM data. First, there are many one-of-a-kind products in AM, and leads to limited observations for each product that can support to estimate an accurate model. Therefore, in Chapter 3, I would like to discuss how to jointly model personalized products by sharing the information among these similar-but-non-identical AM processes with limited observations. Second, for personalized product realization in AM, it is essential to validate the product and process designs before fabrication quickly. Usually, finite element analysis (FEA) is employed to simulate the manufacturing process based on the first-principal model. However, due to the complexity, the high-fidelity simulation is very time-consuming and will delay the product realization in AM. Therefore, in Chapter 4, I would like to study how to predict the high-fidelity simulation result based on the low-fidelity simulation with fast computation speed and limited capability. Thirdly, the defects of AM are usually inside the product, and can be identified by the X-ray computed tomography (CT) images after the build of the AM products. However, limited by the sensor technology, CT image is difficult to obtain for online (i.e., layer-wise) defect detection to mitigate the defects. Therefore, as an alternative, I would like to investigate how to predict the CT image based on the optical layer-wise image, which can be obtained during the AM process in Chapter 5. The proposed methodologies will be validated based on two types of AM processes: fused deposition modeling (FDM) processes and selective laser melting processes (SLM).

The decision to enter a foreign market has long-term implications for the investing firm, as has its choice of entry mode. The hierarchical model of market entry modes proposes that entry modes can be categorised as equity-based or non-equity based, and further categorised by type as joint ventures and wholly owned subsidiaries, exports and contractual agreements. The hierarchical model of market entry modes proposes that there are factors that influence the entry mode at the level of equity versus non-equity but not within the type of equity or non-equity.

Discrete linear control theory is used in this research to examine the effects of system choice and data errors on the performance of production control systems. Two common information flow systems are modelled. These are the Reorder Cycle (ROC) and the Material Requirements Planning (MRP) systems. System choices include the choice of forecasting method, the choice of delivery policy, and the choice of inventory rule. The source of data errors include stock recording errors, delay in stock recording, incorrect bills of material. The other part of the research examines the sequence of implementing a new production control system. Different ways of moving from a Reorder Cycle to a Material Requirements Planning system are studied. Guidelines for an implementation sequence are produced.