A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed

Reducing risk in pre-production investigations through undergraduate engineering projects.

This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level. The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor. The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits. The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Crowdsourcing business models : focusing on the crowd-labor industry and the implications for management and markets

Purpose: This thesis analyzes the Crowd-Labor phenomenon, a subset of the Crowdsourcing industry where users of the related online platforms post tasks/projects and other users work on those tasks, usually in exchange for a payment. This work documents the current status of the platforms operating in this industry, providing new information regarding the numbers and trends. Its second objective is to understand how those companies are organized, what features they possess and how those features are related across different types of platforms. Methodology: Data collection regarding seventy-seven (77) characteristics from fifty-one (51) platforms. The characteristics are about the platforms themselves, their operations and the features they offer to their users. That was followed by an analysis of the data, and a grouping of certain related characteristics (for example, the sum of the number of available languages on the platform) and a correlation analysis to understand which types of platforms exist and what kind of platforms obtain that best performance. Findings: The analysis revealed that there are clusters of platforms based on the type of tasks/projects available on those platforms. Industry characteristics related with performance were analyzed, namely the existence of a forum, APIs, open challenges, the possibility of login & register using Facebook, fix payment fees for contractors, a leaderboard, the existence of multiple languages, internal exams for contractors to get certifications, tracking quality mechanisms and the possibility of project owners only paying when satisfied. Automated features (APIs and internal exams/certifications) stood out as a new positive performance differentiator for this recent industry, which is an original literature contribution originated from this thesis. Practical use: This work presents the current state of the Crowd-Labor industry, its benchmarks or industry standards, users’ motivations and a fact based opinion regarding its future, creating new knowledge that could be particularly useful for researchers, academics, crowdsourcing initiative owners, crowd-Labor users, entrepreneurs and investors. Limitations: An important limitation is that some of the answers to the characteristics used to analyze the Crowd-Labor Platforms were not made public by the platform owners, which didn’t make possible to capture the full picture of some of these platforms. However by studying 51 platforms, the collected data offers statistical evidence in the form of correlations that are statistically significant, which support the conclusions drawn from the analysis

Smart Building Data Collection and Ventilation System Energy Prediction

Data has the potential to transform our environments for the better if utilized to its full potential. A highly interesting use case of data is in relation to Smart Buildings, where IoT technology presents new possibilities. With appropriate collection and structuring of the available data, many new opportunities present themselves. In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To illustrate the potential in the data, one specific problem is researched, namely that of indoor climate optimization and its effects on energy usage. The problem description and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model predictive control and various machine learning methods to predict energy usage. For a one day simulation, the proposed optimization strategy yields a 174.86% increase in energy usage. The conducted work indicates that the proposed model identification technique is unsuitable for the underlying data utilized in this work. The proposed model predictive control strategy and machine learning methods contain promising results

Smart Building Data Collection and Ventilation System Energy Prediction

Data has the potential to transform our environments for the better if utilized to its full potential. A highly interesting use case of data is in relation to Smart Buildings, where IoT technology presents new possibilities. With appropriate collection and structuring of the available data, many new opportunities present themselves. In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To illustrate the potential in the data, one specific problem is researched, namely that of indoor climate optimization and its effects on energy usage. The problem description and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model predictive control and various machine learning methods to predict energy usage. For a one day simulation, the proposed optimization strategy yields a 174.86% increase in energy usage. The conducted work indicates that the proposed model identification technique is unsuitable for the underlying data utilized in this work. The proposed model predictive control strategy and machine learning methods contain promising results

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book