Sustainability Benefits Analysis of CyberManufacturing Systems

Confronted with growing sustainability awareness, mounting environmental pressure, meeting modern customers’ demand and the need to develop stronger market competitiveness, the manufacturing industry is striving to address sustainability-related issues in manufacturing. A new manufacturing system called CyberManufacturing System (CMS) has a great potential in addressing sustainability issues by handling manufacturing tasks differently and better than traditional manufacturing systems. CMS is an advanced manufacturing system where physical components are fully integrated and seamlessly networked with computational processes. The recent developments in Internet of Things, Cloud Computing, Fog Computing, Service-Oriented Technologies, etc., all contribute to the development of CMS. Under the context of this new manufacturing paradigm, every manufacturing resource or capability is digitized, registered and shared with all the networked users and stakeholders directly or through the Internet. CMS infrastructure enables intelligent behaviors of manufacturing components and systems such as self-monitoring, self-awareness, self-prediction, self-optimization, self-configuration, self-scalability, self-remediating and self-reusing. Sustainability benefits of CMS are generally mentioned in the existing researches. However, the existing sustainability studies of CMS focus a narrow scope of CMS (e.g., standalone machines and specific industrial domains) or partial aspects of sustainability analysis (e.g., solely from energy consumption or material consumption perspectives), and thus no research has comprehensively addressed the sustainability analysis of CMS. The proposed research intends to address these gaps by developing a comprehensive definition, architecture, functionality study of CMS for sustainability benefits analysis. A sustainability assessment framework based on Distance-to-Target methodology is developed to comprehensively and objectively evaluate manufacturing systems’ sustainability performance. Three practical cases are captured as examples for instantiating all CMS functions and analyzing the advancements of CMS in addressing concrete sustainability issues. As a result, CMS has proven to deliver substantial sustainability benefits in terms of (i) the increment of productivity, production quality, profitability & facility utilization and (ii) the reduction in Working-In-Process (WIP) inventory level & material consumption compared with the alternative traditional manufacturing system paradigms

A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments

The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies. One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage. For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities. Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment. The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments. The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios. The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions. The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary

IUPUC Spatial Innovation Lab

During the summer of 2016 the IUPUC ME Division envi-sioned the concept of an “Imagineering Lab” based largely on academic makerspace concepts. Important sub-sections of the Imagineering Lab are its “Actualization Lab” (mecha-tronics, actuators, sensors, DAQ devices etc.) and a “Spatial Innovation Lab” (SIL) based on developing “dream stations” (computer work stations) equipped with exciting new tech-nology in intuitive 2D and 3D image creation and Virtual Reality (VR) technology. The objective of the SIL is to cre-ate a work flow converting intuitively created imagery to an-imation, engineering simulation and analysis and computer driven manufacturing interfaces. This paper discusses the challenges and methods being used to create a sustainable Spatial Innovation Lab

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Automated throughput optimisation: a cost effective I4.0 solution

The escalating consumer demand for cost-effective, high-quality products has compelled manufacturers to enhance efficiency while curbing expenses. The emergence of Industry 4.0 (I4.0) technologies has ushered in an era of closed loop manufacturing, enabled by real-time data processing and feedback. However, the prohibitive costs of I4.0 tools often restrict access for smaller manufacturers and machine shops. Additionally, the shift to I4.0 necessitates a distinct skill set, further disadvantaging smaller players. This research proposes an innovative approach leveraging existing resources. By introducing a method that integrates a touch trigger probe with aging, albeit, functional CNC machines, establishing a closed loop system. Through intelligent G-code utilisation, this system automates the recording and management of Process Control Indicators (PCIs) while optimising inspection frequencies and throughput. By adopting this method, manufacturers can embrace I4.0 methodologies without the burden of substantial capital expenditure or extensive workforce upskilling. This study not only offers an immediate solution for manufacturers but also serves as a pioneering model for wider I4.0 adoption. By highlighting the potential of repurposing existing machinery, this research highlights a path toward increased efficiency, competitiveness, and accessibility in the manufacturing landscape

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

Generative design for agile robot based additive manufacturing for sustainable aesthetic furniture products

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe Furniture manufacturing industry has been slow to adopt the latest manufacturing technologies, relying heavily upon specialised conventional machinery. This approach not only requires high levels of specialist knowledge, training and capital investment, but also suffers from significant traditional subtractive manufacturing waste and high logistics costs due to centralised manufacturing, with high levels of furniture product not re-cycled or re-used at the end of its life cycle. This doctoral research aims to address these problems by establishing a suitable digital manufacturing technology framework concept to create step changes in the furniture design to manufacturing pathway. The design stage has the potential to contribute massively to the environmental impact of products. In this research, a Robot Base Additive Manufacturing Concept cell for future furniture manufacturing is reported. Generative design illustrates its potential contribution to waste reduction, increased manufacturing efficiency, optimised product performance and reduced environmental impact constituting a truly lean and progressive future for Furniture Manufacturing Design. Through case studies the research will show the potential for exploiting Single Minute Exchange of Die (SMED) concepts through the rule-based AI generative design post-processing of geometry for robot manufacturing, examination of different methodologies for printing and thus the resultant potential for ‘Mass Customised’ Furniture. Aesthetics, structures and the use of Smart Materials not previously economic to manufacture will be considered to demonstrate the potential to flatten the traditional Bill of Materials (BOM) and reduce logistical issues. The Furniture Industry has developed from an artisan driven craft industry, whose pioneers saw themselves reflected in their crafts and cherished the sense of pride in the originality of their designs, now largely re-configured to an anonymous collective mass output. Digital technologies and smart materials enhancement allow innovative structural fabrication, presenting a plethora of potential for networked artisan craft industries to create extraordinary aesthetics and customisable product designs. Integrating these developments with the computing power of generative design provides the tools for practitioners to create concepts which are well beyond the insight of even the most consummate traditional designers. This framework is becoming an active area of research for application in many different industries. The step changes are empowering artisans to revolutionise the design to manufacture workflow, giving momentum to the concept of conceiving a pre-industrial model of manufacturing with bespoke sustainable design at its heart. The elements of the framework will be described and illustrated using case study models highlighting the potential for creating unique aesthetics for sustainable furniture products. The research presents the methodology to create and compare iterations employing different rule sets through a commercial generative design application and how these outputs can be further customised using parametric strategies in NURBS modellers, with the ultimate goal of creating aesthetic ‘Lean’ and sustainable innovative furniture of the future, thus illustrating how the creative use of digital networks in linking individual practitioners in the making of aesthetic customised products, manufactured local to their markets, could be achieved using this framework. This research shows a robust ‘green revolution’ is evidently necessary to satisfy the needs of an ever-growing population, allowing the world to thrive within the means of this planet. New approaches to the use of technologies can achieve these changes in Furniture Manufacturing and establish a truly enhanced Circular Economy. Governments around the World are encouraging these initiatives and these approaches are identified and rationalised alongside the drivers for change which will have major impacts on this manufacturing sector. This research critically examines the Furniture Design and Manufacturing technologies presented through a TRIZ framework against the desired outcomes. Using this approach together with the physical development of a robotic test cell, combined with case study data significant contributions to knowledge in the focused area of Furniture Manufacturing are identified, detailed and enhance Furniture Design, Manufacturing and Environmental Impact for the future. The focused approach also serves to highlight areas requiring further research