Customising with 3D printing: The role of intelligent control

© 2018 Elsevier B.V. The emergence of direct digital manufacturing creates new opportunities for the production of highly customised goods especially when it is combined with conventional manufacturing methods. Nevertheless, this combination creates a need for systems that can effectively manage and control the resulting distributed manufacturing process. In this paper, we explore three different configurations that can enable direct digital manufacturing for customisation, ranging from fully integrated to inter-organisational set up. Additionally, control requirements of such systems are developed and the suitability of intelligent control is explored. By ‘intelligent control’ we mean production control that is capable of assessing and interacting with the production environment and adapting production accordingly. We argue that the so called intelligent product paradigm provides a suitable mechanism for the development of such intelligent control systems. In this approach, the intelligent product directly co-ordinates with design agent, 3D printing agents and other conventional manufacturing system agents to schedule, assign and execute tasks independently. Via a case example of a realistic production system, we propose and implement such an intelligent control system and we analyse its feasibility in supporting 3D printing enabled customisation

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Multi-Agents System Approach to Industry 4.0: Enabling Collaboration Considering a Blockchain

Dissertação de Mestrado em Engenharia InformáticaThe evolution of existing technologies and the creation of new ones paved the way for a new revolution in the industrial sector. With the introduction of the existing and new technologies in the manufacturing environment, the industry is moving towards the fourth industrial revolution, called Industry 4.0. The fourth industrial revolution introduces many new components like 3D printing, Internet of things, artificial intelligence, and augmented reality. The automation of the traditional manufacturing processes and the use of smart technology are transforming industries in a more interconnected environment, where there is more transparent information and decentralised decisions. The arrival of Industry 4.0 introduces industries to a new environment, where their manufacturing processes are more evolved, more agile, and with more efficiency. The principles of Industry 4.0 rely on the interconnection of machines, devices, sensors, and people to communicate and connect. The transparency of information guaranties that decision makers are provided with clear and correct information to make informed decisions and the decentralisation of decisions will create the ability for machines and systems to make decisions on their own and to perform tasks autonomously. Industry 4.0 is making manufacturing processes more agile and efficient, but due to the fast pace of trends and the shift from the traditional mass production philosophy towards the mass customisation, following the Industry 4.0 guidelines might not be enough. The mass customisation paradigm was created from the desire that customers have in owning custom made products and services, tailor made to their needs. The idea to perform small tweaks in a product to face the needs of a consumer group, keeping the production costs like the ones from the mass production, without losing efficiency in the production. This paradigm poses great challenges to the industries, since they must be able to always have the capability to answer the demands that may arise from the preparation and production of personalised products and services. In the meantime, organisations will try to increasingly mark its position in the market, with competition getting less relevant and with different organisations worrying less with their performance on an individual level and worrying more about their role in a supply chain. The need for an improved collaboration with Industry 4.0 is the motivation for the model proposed in this work. This model, that perceives a set of organisations as entities in a network that want to interact with each other, is divided into two parts, the knowledge representation and the reasoning and interactions. The first part relies on the Blockchain technology to securely store and manage all the organisation transactions and data, guaranteeing the decentralisation of information and the transparency of the transactions. Each organisation has a public and private profile were the data is stored to allow each organisation to evaluate the others and to allow each organisation to be evaluated by the remainder of the organisations present in the network. Furthermore, this part of the model works as a ledger of the transactions made between the organisations, since that every time two organisations negotiate or interact in any way, the interaction is getting recorded. The ledger is public, meaning that every organisation in the network can view the data stored. Nevertheless, an organisation will have the possibility, in some situations, to keep transactions private to the organisations involved. Despite the idea behind the model is to promote transparency and collaboration, in some selected occasions organisations might want to keep transactions private from the other participants to have some form of competitive advantage. The knowledge representation part also wants to provide security and trust to the organisation that their data will be safe and tamper proof. The second part, reasoning and interactions, uses a Multi-Agent System and has the objective to help improve decision-making. Imagining that one organisation needs a service that can be provided by two other organisations, also present in the network, this part of the model is going to work towards helping the organisations choose what is the best choice, given the scenario and data available. This part of the model is also responsible to represent every organisation present in the network and when organisations negotiate or interact, this component is also going to handle the transaction and communicate the data to the first part of the model.A constante evolução de tecnologias atuais e a criação de novas tecnologias criou as condições necessárias para a existência de uma nova revolução industrial. Com a evolução de dispositivos móveis e com a chegada de novas tecnologias e ferramentas que começaram a ser introduzidas em ambiente industrial, como a impressão 3D, internet das coisas, inteligência artificial, realidade aumentada, entre outros, a industria conseguiu começar a explorar novas tecnologias e automatizar os seus processos de fabrico tradicionais, movendo as industrias para a quarta revolução industrial, conhecida por Industria 4.0. A adoção dos princípios da Indústria 4.0 levam as indústrias a evoluir os seus processos e a ter uma maior e melhor capacidade de produção, uma vez que as mesmas se vão tornar mais ágeis e introduzir melhorias nos seus ambientes de produção. Uma dessas melhorias na questão da interoperabilidade, com máquinas, sensores, dispositivos e pessoas a comunicarem entre si. A transparência da informação vai levar a uma melhor interpretação dos dados para efetuar decisões informadas, com os sistemas a recolher cada vez mais dados e informação dos diferentes pontos do processo de manufatura. (...

Developing distributed manufacturing strategies from the perspective of a product-process matrix

In today’s highly competitive global business landscape, customers demand personalised products and responsive distribution systems, hence fuelling the concept of Distributed Manufacturing (DM) as a paradigm that suggests the geographical distribution of manufacturing systems adjacent to the markets to enable ‘production on demand’. To this end, the objective of this research is to explore the DM concept to inform firms about the dynamically changing manufacturing environment, along with the emerging opportunities, and support business stakeholders in implementing DM-oriented strategies to achieve digitalisation, personalisation, and localisation. More specifically, the present research builds upon the Dynamic Capability Theory (DCT) and conducts semi-structured interviews with a panel of 16 experts from the Fast-Moving Consumer Goods, Automotive, and Engineering industries to develop 12 exploratory industry cases. Our analysis highlights that companies can adopt three strategies to implement DM and realise shorter lead times and personalised product offerings, namely: (i) small-scale DM; (ii) in-house decoupled manufacturing; and (iii) outsourced decoupled manufacturing. However, the economic viability of the DM concept is identified as a significant barrier to relinquish the traditional centralised economies-of-scale. This research contributes by applying the DCT to the DM concept to advocate the viability and sustainability of manufacturing systems in the era of Industry 4.0. Pertaining to the originality of this research, limited work is available on the applicability of DM in industries, from the DCT perspective, to accomplish competitive advantages in the dynamic environment of manufacturing.his research has received funding from the EPSRC under Reference No. EP/K02888X/1, Project Title: “Engineering Driven Sustainable Supply Networks – A UK/India Collaborative Study”

Anarchic manufacturing: implementing fully distributed control and planning in assembly

This paper demonstrates that a distributed control and planning system can fulfil an idealised mixed-model assembly problem and compete with traditional systems. The anarchic manufacturing system is a distributed planning and control system, based on a free market structure, where system elements have decision-making authority and autonomy. Mixed-model assembly is typically managed centrally for production planning and control, using simplification and hierarchical structures to manage complexity. In developing anarchy, inter-job cooperation is implemented to synergise jobs together and fulfil global objectives efficiently. The anarchic system maximises available flexibility, through embracing complexity, and reduces myopic decision making by maximising an agent’s lifetime profitability. Through agent-based simulation experiments, the anarchic system is compared to fixed and flexible centralised systems. The proposed system outperforms traditional systems when the scenario’s structural flexibility allows agile and delayed dynamic decision making. Additionally, the anarchic system managed dynamic bottleneck disruptions as effectively as flexible centralised systems

Design and Planning of Manufacturing Networks for Mass Customisation and Personalisation: Challenges and Outlook

AbstractManufacturers and service providers are called to design, plan and operate globalized manufacturing networks, addressing to challenges such as ever-decreasing lifecycles and increased product complexity. These factors, caused primarily by mass customisation and demand volatility, generate a number of issues related to the design and planning of manufacturing systems and networks, which are not holistically tackled in industrial and academic practices. The mapping of production performance requirements to process and production planning requires automated closed-loop control systems, which current systems fail to deliver. Technology-based business approaches are an enabler for increased enterprise performance. Towards that end, the issues discussed in this paper focus on challenges in the design and planning of manufacturing networks in a mass customization and personalization landscape. The development of methods and tools for supporting the dynamic configuration and optimal routing of manufacturing networks and facilities under cost, time, complexity and environmental constraints to support product-service personalization are promoted

Efficient Order and Resource Coordination in Mass Customization

Mass customization manufacturing systems require a high level of adaptability and flexibility in production – especially in production planning and control. In particular, the Coordination of orders and resources is critical, because of the high volatility and the make to order principle. Multi-agent systems theoretically provide the required features to handle that complexity, but a lack of informational integration and organizational incompatibilities lead to low applicability. The application of Internet Technology provides the necessary interoperability and organizational alignment to support an overall application of multi-agent systems in mass customization.Mass Customization; Internet Technologies; Multi Agent Systems; Production Planning and Control