The transition towards a circular economy has become important. Manufacturing industry being a major stakeholder in this transition has started exploring the potential of this transition and challenges in implementation. Ambitious companies such as Gorenje d.d. has taken the circular economy transition seriously and aims to become a pioneer in implementing circular manufacturing systems. One vital step in this transition is the business model shift from the linear (sales model) to a circular model such as 'product as a service'. This brings new challenges to Original Equipment Manufacturers (OEMs) that have never been experienced in their conventional businesses. One of the challenges is to establish an information communication and technology (ICT) infrastructure that enables information management and sharing as well as establishes a real-time communication between relevant stakeholders. Outlining such an ICT infrastructure is the objective of this paper.

One of the latest developments in manufacturing systems is reconfigurable manufacturing systems in which reconfigurable machine tools are the heart of such systems. The aim of application of this type of machine tool is having rapid cost-effective responsiveness to changes in new product variants or volume in manufacturing companies because of different interests, needs and desires of customers in global competitive market. The important tools to implement this kind of systems are systematic methodologies and enablers like open architecture controllers, Interface standard and comprehensive and integrated software to make the couple of limited optimized configurations of the machine tool. To choose the best configuration among proposed configuration by the software we need some evaluation methods based on smart chosen criteria to choose the best modular machine tool structure. One of the important points to choose the best configuration is ease of reconfigurability. The aim of this paper is to introduce a flexible and practical index for different products in reconfigurable manufacturing systems. This Index is defined based on two important factors: number of the changeable modules and the complexity of the interfaces including mechanical, informational and power (hydraulic, pneumatic or electrical).Generally the more number of the modules and the more complex interfaces means the more difficult to reconfigure the machine tool structure.

Recent developments in the area of Cyber-Physical Production Systems prove that high technology readiness level is already achieved and industrialization of such technologies is not far from today. Although these technologies seem to be convenient in providing solutions to environmental uncertainties, their application provides adaptability only at shop floor level. Needless to say, an enterprise cannot reach true adaptability without ensuring adaptation skills at every level in its hierarchy. Commonly used production planning and control approaches in industry today inherit from planning solutions which are developed in response to historical market characteristics. However, market tendency in recent years is towards making personalized products a norm. The emerging complexity out of this trend obliges planning systems to a transition from non-recurring, static planning into continuous re-planning and re-configuration of systems. Therefore, there is a need of responsive planning solutions which are integrated to highly adaptable production system characteristics.

In this dissertation, Demand Responsive Planning, DRP, is presented which is a planning framework aiming to respond to planning needs of shifting trends in both production system technologies and market conditions. The DRP is based on three main constructs such as dynamicity, responsiveness and use of precise data. These features set up the foundation of accomplishing a high degree of adaptability in planning activities. By this means, problems from an extensive scope can be handled with a responsive behavior (i.e. frequent re-planning) by the use of precise data. The use of precise data implies to execute planning activities subject to actual demand information and real-time shop floor data. Within the context of the DRP, both a continuous workload control method and a dynamic capacity adjustment approach are developed. A test-bed is coded in order to simulate proposed method based on a system emulation reflecting the characteristics of cyber-physical production systems at shop floor level.

Continuous Precise Workload Control, CPWLC, method is a novel approach aiming at precise control of workload levels with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. As a result, improved shop floor performances are achieved through controlling workload levels precisely by the release of appropriate job types at the right time.

Presented dynamic capacity adjustment approach utilizes rapid re-configuration capability of cyber-physical systems in achieving more frequent capacity adjustments. Its implementation architecture is integrated to the CPWLC structure. By this means, a holistic approach is realized whereby improved due date performance is accomplished with minimized shop floor congestion. Hence, sensitivity to changing demand patterns and urgent job completions is improved.

Dynamic and unforeseeable characteristic of the current market and production environment is not feasible to be met through pre-set parameters being dependent on the predictions. Handling this matter requires to keep focus on production system adaptability. Evolvable Production System has achieved fully system reconfigurability through process oriented modularity and multi agent based distributed control system architecture. One of the essential enhancements provided by EPS on the shop floor is achieving minimized/eliminated system setup time in response to changing product requirements.

Manufacturing planning and control system, on the other hand, follows hierarchical principles which are quite much reliant on the predicted information so to structure production and planning environment on it. Production system limitations, such as lack of adaptability in response to changing conditions, are in fact influencing the planning system to be structured on the predictions. The enhancements which are ensured by the architecture of EPS enable to relax the constraints on planning system which are imposed by the limitations of production system. These enhancements have an effect at different levels in the planning hierarchy. On the light of these improvements, the planning framework as it is used so far in the industry becomes invalid and this arise a requirement for planning system structure to be designed according to a fully reconfigurable system to be able to benefit such a production system by all means.

This thesis targets to enlighten the relation between the production system characteristics and planning system structure by emphasizing the planning problems and proposing a planning reference architecture solution to be able achieve a responsive planning framework.

The variability in the market conditions is growing in terms of its frequency of change and range of diversity. In response to this new industrial panorama, research on production systems is aiming to achieve truly reconfigurable shop floors. Frequent changes in such systems require also frequent re-planning with updated information. In this regard the Continuous Precise Workload Control method, is a recent approach aiming at precise control of workload in the shop floor with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. The study in this paper is two folded; (1) in order to highlight its distinctive characteristics, the presented workload approach is defined in terms of eight dimensions of the workload control concept and (2) the penalty of idleness which affects the decision of release is analyzed by an experiment design in order to investigate its correlation with two critical parameters, norm value and assessment range. The results show that the idleness penalty factor decreases the idleness of the resources up to a point where the adverse effect is initiated. Besides there are strong indications towards the correlation of idleness penalty factor with the norm value.

Purpose – Evolvable production systems enable fully reconfiguration capabilities on the shop floor through process-oriented modularity and multiagent-based distributed control. To be able to benefit architectural and operational characteristics of evolvable systems, there is a need of a newplanning approach which links shop floor characteristics and planning operations. This paper seeks to address these issues.

Design/methodology/approach – Evolvable production system has a structured methodology in itself. Consistent to this, a reference planningarchitecture is developed aiming to achieve agility on planning activities. Besides a workload control method is proposed and implemented as a part ofthe planning architecture.

Findings – First applications of evolvable systems have been implemented through European research projects. Shop floor working principles andarchitectural characteristics are consistent to facilitate more agility on planning activities which are framed at a planning reference architecture calleddemand responsive planning. As an implementation case, an agent-based workload control method is proposed and implemented. The characteristicsof EPS and proposed planning architecture enable continuous and dynamic workload control of the shop floor to be implemented.

Originality/value – This paper presents a new planning model compatible with evolvable production systems targeting to agility to demand onplanning and control activities benefiting shop floor enhancements of a fully reconfigurable system which enables to relax constraints imposed fromproduction systems to planning. In addition, a continuous and dynamic workload control method is proposed and implemented.

On the way to achieve mass customization production systems have to obtain the capability of rapid reconfiguration of not only physical components but also from control point of view. Evolvable Production System targets highly adaptable mechanical and control solutions that can enhance reusability and interoperability of modules, enabling lifetime extension of the modules. The focus of EPS paradigm is to achieve overall system adaptability by autonomous modules which are dedicated to specific processes with the capability of short deployment time at shop floor without reprogramming effort. From the operational point of view EPS brings significant enhancements considering shop floor dynamics and performances therefore positioning of EPS principles and approaches in production system typology from different perspectives is essential. This has been done by two means which are process flow structure and customer order decoupling point location.

The balance between assembly process optimality and their system’s ability to adapt to new requirements is a key to success for assembly companies. To increase SME’s survivability, an effective methodology is needed to handle the requirements of both agility and mass customization. Evolvable Assembly Systems (EAS) paradigm is a next generation assembly systems focused on these issues. Three key issues are here in focus: process-oriented approach, fine modular granularity, and module intelligence through lighter multi-agent technology at the shop floor level. These issues

Production planning and control strategies have been changing in line with the constant change on product and customer requirements, under the light of technological and scientific advancements. Production systems which are based on mass production became obsolete in time hence companies, being profit oriented, are in need of new solutions towards mass customization to handle rapidly changing market conditions. To deal with this issue, production systems and production planning strategies have to be complementing each other. In this paper Evolvable Production Systems and its compatibility to Just in Time (JIT) Production compared to Material Requirement Planning (MRP) will be discussed.

Objective of Tiphys project is building an Open Networked Platform for the learning of Industry 4.0 themes. The project will create a Virtual Reality (VR) platform, where users will be able to design and create a VR based environment for training and simulating industrial processes but they will be able to study and select among a set of models in order to standardize the learning and physical processes as a virtual representation of the real industrial world and the required interactions so that to acquire learning and training capabilities.. The models will be structured in a modular approach to promote the integration in the existing mechanisms as well as for future necessary adaptations. The students will be able to co-create their learning track and the learning contents by collaborative working in a dynamic environment. The paper presents the development and validation of the learning model, built on CONALI learning ontology. The concepts of the ontology will be detailed and the platform functions will be demonstrated on selected use cases.

Current demand for agile and sustainable production systems increases the needs of strategies to cope with the arising complexity of product transformation. In the EPS framework these strategies include a process-oriented modularization as well as distributed control solutions based on Multi-Agent technology. Reference Architecture and ontological representation of the logical and physical links in the system are at the basis of developing and running a production system in an EPS environment. This paper provides a review of EPS features and their effects on the complexity and the ability to handle it.

Darwin's evolutionary theory of natural selection has had a strong impact on both science and culture, and has over the last decades become a popular inspiration in engineering sciences. Both the wide range of scientific areas where evolutionary theory is applied, and the simplistic metaphors used to explain evolution in schools and non-scientific situations have caused confusion of how key evolutionary concepts should be understood. In this paper, the cornerstones in biological and social evolutionary theory are identified and addressed from an engineering point of view. Previous efforts to apply evolutionary theories within engineering are then addressed and related to the needs and opportunities within manufacturing and assembly.

The open architecture computer numerical control (OACNC) system meets the individualized demand of modern industry for its characteristics of flexibility, adaptability, versatility, and expansibility. Existing OACNC systems depend on specialized software which reduces the openness of the OACNC system. This paper introduces a new OACNC system based on a soft-integrated communication module. The module improves the data exchange principle in communication shared memory and is built by open API. Therefore the OACNC system can be compatible with different communication protocols between periodic and aperiodic. Then a case is proposed to test the compatibility and extensibility of the system. Finally, the prospect of the OACNC system and future research is discussed.

Modern machineries are often cyber-physical system-of-systems controlled by intelligent controllersfor collaborative operations on the productions of complex products. To assure theefficiency and effectiveness, a consolidation of concerns across managerial levels, product lifecyclestages, and product lines or families becomes necessary. This calls for a common informationinfrastructure in terms of ontology, models, methods and tools. For industrial manufacturerssubjected to increased cost pressure and market volatility, the availability of such an informationinfrastructure would promote their abilities of making optimized and proactive decisions andthereby their competitiveness and survivability. This paper presents a virtual environment thatconstitutes an information infrastructure for the management and development of evolvableproduction systems (EPS) in manufacturing. It adopts mature modeling frameworks throughEAST-ADL for an effective model-based approach. The contribution is centered on a meta-modelthat offers a common data specification and semantic basis for information management acrossproduct lifecycle, models and tools, both for resource planning and for anomaly treatment. Aprototype tool implementation of this virtual environment for validation is also presented.

In this paper, the temperature measuring tool based on NiCr/NiSi thin film thermocouple is developed. 6,061 aluminium alloys is selected as the experiment object. Based on the cutting area temperature result during field test, the central composite design is utilised, which contains the parameters of cutting speed, feed rate and cutting depth. Regarding each parameter, three levels are selected and then the second-order regression equation between cutting area temperature and three cutting parameters is established. The data of experimental measurement corresponds well with the mathematical prediction, which confirms that the experimental and mathematical methods are valid in the research on cutting area temperature.

Robots are becoming more adaptive and aware of their surroundings. This has opened up the research area of tight human-robot collaboration, where humans and robots work directly interconnected rather than in separate cells. The manufacturing industry is in constant need of developing new products. This means that operators are in constant need of learning new ways of manufacturing. If instructions to operators and interaction between operators and robots can be virtualized this has the potential of being more modifiable and available to the operators. Augmented Reality has previously shown to be effective in giving operators instructions in assembly, but there are still knowledge gaps regarding evaluation and general design guidelines. This paper has two aims. Firstly it aims to assess if demonstrators can be used to simulate human-robot collaboration. Secondly it aims to assess if Augmented Reality-based interfaces can be used to guide test-persons through a previously unknown assembly procedure. The long-term goal of the demonstrator is to function as a test-module for how to efficiently instruct operators collaborating with a robot. Pilot-tests have shown that Augmented Reality instructions can give enough information for untrained workers to perform simple assembly-tasks where parts of the steps are done with direct collaboration with a robot. Misunderstandings of the instructions from the test-persons led to multiple errors during assembly so future research is needed in how to efficiently design instructions.

The increasing market fluctuations and customized products demand have dramatically changed the focus of industry towards organizational sustainability and supply chain agility. Such critical changes inevitably have a direct impact on the shop-floor operational requirements. In this sense, a number of innovative production paradigms emerged, providing the necessary theoretical background to such systems. Due to similarities between innovative modular production floors and natural complex systems, modern paradigms theoretically rely on bio-inspired concepts to attain the characteristics of biological systems. Nevertheless, during the implementation phase, bio-inspired principles tend to be left behind in favor of more traditional approaches, resulting in simple distributed systems with considerable limitations regarding scalability, reconfigurable ability and distributed problem resolution.

This paper analyzes and presents a brief critical review on how bio-inspired concepts are currently being explored in the manufacturing environment, in an attempt to formulate a number of challenges and properties that need to be considered in order to implement manufacturing systems that closely follow the biological principles and consequently present overall characteristics of complex natural systems.

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

In almost all supply chains, materials need to be stored or buffered, implying that manufacturing companies need effective replenishment methods. However, this is challenging, since companies must balance inventory costs and customer service in complex and different situations. Therefore, it is important to choose replenishment methods carefully. One well-known and widespread method is Material Requirements Planning (MRP). But the method has problems, such as regarding volume flexibility. There are other methods, but the literature lacks case studies and detailed descriptions and analysis of them, especially for visually oriented methods. Therefore, it is important to explore different methods for materials supply.

The purpose of this study is to analyze the application of visually oriented replenishment methods in manufacturing industry and further to design a tentative decision tool for selecting methods. The research aims to provide some answers to three key questions.

The first research question concerns general factors that are important for evaluating how effective replenishment methods are. From the literature review, different factors emerged such as product characteristics (fit in the Kraljic matrix, volume issues, size, etc.); information, trust, and geographical proximity between supplier and customer; and different logistics goals (delivery service elements, tied-up capital, use of resources, inventory accuracy). It is also import to include the basic principles methods are related to. The factors were summarized in an analysis model, which is structured with three main areas (planning environment/conditions, basic principles, and effects). The model is used to analyze four case studies.

The  second research  question focuses  on  the  characteristics for  visually orientedreplenishment methods. Examples of characteristics are: easy to understand and operate, offers uncomplicated flows, substantially applicable for noncritical and leverage parts with high yearly requirement and fairly even consumption, provides potential for  reduced  errors  in  stores/flows, potential for  providing high  delivery service and low levels of tied-up capital and resource utilization.

The third research question focuses on what a tentative decision tool for selecting replenishment methods might look like, based on the factors that emerged from the other research questions and studies by others. In order to achieve an efficient materials supply, companies need to consider these factors when selecting replenishment methods. The decision tool consists of different steps, considering aspects of the planning environment/conditions in relation to the product and the supplier. The importance of the companies’ goals/motives for materials supply must also be assessed. The output from the decision tool is appropriate replenishment methods.

Assembly interface of aircraft vertical tail is a large thin-wall structure and made from titanium alloys, which causes easily machining vibration, deformation and undercutting in finish machining due to its low stiffness, low thermal conductivity, and high chemical activity. To address these problems, a novel eddy current damper for assembly interfaces machining (ECD-AIM) is proposed to suppress multimodal vibration in the machining of the assembly interfaces. Within the context, the mathematical model of damping performance of the damper is established based on the principle of electromagnetic induction, based on which a novel design of the damper is proposed, and optimized by considering the relationship between damping performance and the key components of the damper. Then, the dynamics model of the suppression system of the assembly interface machining is established, where the relationship between vibration velocity and damping performance of the damper is obtained by using numerical analysis and finite element simulation. Finally, the damping performance of the damper is validated in terms of the three configurations (no applied ECD-AIM, a single ECD-AIM, and dual ECD-AIMs) via a set of dynamic tests (impact tests and harmonic tests) and cutting tests. The test results demonstrate that the configuration of dual ECD-AIMs can guarantee stability and reliability of assembly interface machining. The proposed damper can provide a feasible solution for vibration suppression in a limited workspace.

Today, in the era of modern Intelligent Production Environments (IPE) and Industry 4.0, the manufacturing of a product takes place in various partial steps and these mostly in different locations, potentially distributed all over the world. The producing companies must assert in the global market and always find new ways to cut costs by saving tax, changing to the best providers, and by using the most efficient and fastest production processes. Furthermore, they must be inevitably based on a cloud-based repository and distributed architectures to make data and information accessible everywhere as well as development processes and knowledge available for a worldwide cooperation. A so called Collaborative Adaptive (Production) Process Planning (CAPP) can be supported by semantic approaches for knowledge representation and management as well as knowledge sharing, access, and re-use in a flexible and efficient way. In this way, to support CAPP scenarios, semantic representations of such knowledge integrated into a machine-readable process formalization is a key enabling factor for sharing in cloud-based knowledge repositories. This is especially required for, e.g., Small and Medium Enterprises (SMEs). When SMEs work together on a production planning for a joint product, they exchange component production and manufacturing change information between different planning subsystems. These exchanges are mostly based on the already well-established Standard for the Exchange of Product model data (STEP), not least to obtain a computer-interpretable representation. Moreover, so-called Function Block (FB) Domain Models could support these planning process. FBs serve as a high-level planning-process knowledge-resource template and to the representation of knowledge. Furthermore, methodologies are required, which based on process-oriented semantic knowledge-representation, such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozess-orientiertes Innovations Management, WPIM). WPIM is already a web and cloud-based tool suites and can represent such planning processes and their knowledge resources and can therefore be used to support the integration and the management of distributed CAPP knowledge in Manufacturing Change Management (MCM), as well as its access and re-use. That is also valid for Assembly-, Logistics- and Layout Planning (ALLP). On the one hand, a collaborative planning in a machine-readable and integrated representation will be possible as well as an optimization for mass production. On the other hand, within a cloud-based semantic knowledge repository, that knowledge can be shared with all partners and contributors. To combine all these functionalities, in 2016 we have already introduced a method, called Knowledge-based Production Planning (KPP). We outlined the theoretical advantages of integrating CAPP with Collaborative Manufacturing Change Management (CMCM) in the last year at MSEC16. In this Paper, we will demonstrate our first implementations of the KPP application with an integrated visual direct manipulative process editor as well as a first prototype of our mediator architecture with a semantic integration including a query library based on the KPP ontology.

Industrialization of construction is a business strategy to significantly improve competitiveness. However, the organization structure of the construction firms needs to support the new production system. The knowledge on why and how this business development can be accomplished is scarce, both within academia and in business practice. This research seeks to fill this knowledge gap.

The purpose of organization structure and the production system have is to coordinate the firm’s processes and control the work performing resources. Information is one of the most fundamental dimensions for steering and controlling the work. The different information types are determined by the firm’s product customization strategy and the production system flexibility. Further, diverse information types are managed in different extent by the organizational steering mechanisms. Consequently, firms with dissimilar customization strategy or production flexibility should organizationally be designed differently in order to be efficient.

The developed model identifies four generic production topologies: “engineer-to-order” (ETO), “manufacture-to-order” (MTO), “assembly-to-order” (ATO), and “make-standard-products” (MSP). The differences between the topologies can be related to the location of the “customer-order-decoupling-point” (CODP) in the product realization process; and to what extent the upstream and downstream processes continuously use stored information or process information to accomplish the work of each product order. The model predicts which organization structure mechanisms that should be used for which processes for each production topology. It is the specific configuration of the mechanisms that gives each production topology their organizational capability. The model has been validated by case studies in four organizations, each representing one of the four generic production topologies. Three cases considered housing and one studied truck manufacturing.

It has been shown that the conventional housing firms have an ETO-production topology, while industrialized housing firms belonging to one of the others, i.e. MTO, ATO or MSP. The reason is that ETO-firms rely on crafts-based production to manage the work, while the other topologies base their steering mechanisms on industrial principles. These two types of production are fundamentally different, which also explain the need for different organization structures. The research complements previous knowledge and significantly increases the ability to predict, analyze and explain an organization’s design and behavior. The model can be used in practice to guide business development work and performance improvement programs.

The design process has a significant impact on the performance and profitability of a housing project. Therefore, decisions made during the design process should take into consideration knowledge and experience from other processes in previously accomplished projects, specifically from the production phase. How to capture and use production experience in housing has not gained enough interest, possibly leading to sub-optimal improvements during the construction process. This motivates research on how onsite production experience from similar previous projects can be captured and used to improve constructability without risking customer values. Based on the concept of constructability, ’design for manufacturing and assembly’ and the theory of waste, the method ’design for construction’ (DFC) has been developed. The four-step model complements the conventional construction process, and consists of the following steps: (1) specify customer values and similar previous projects; (2) identify onsite waste and cost drivers in previous projects; (3) develop criteria to evaluate constructability; and (4) evaluate constructability of the design. The DFC method is exemplified and tested through a case study, in which it was shown that the method facilitated identification of all problems that were considered in the investigated project. The method also highlighted other project obstacles that potentially could have been solved to improve constructability.

Graphcut-based algorithm is adopted in many video object segmentation systems because different terms can be probabilistically fused together in a framework. Constructing spatio-temporal coherences is an important stage in segmentation systems. However, many steps are involved when computing a key term with good discriminative power. If the cascade steps are adopted, the inaccurate output of the previous step will definitely affect the next step, leading to inaccurate segmentation. In this paper, a key term that is computed by a single framework referred to as boundary-constrained low-rank sparse representation (BCLRSR) is proposed to achieve the accurate segmentation. By treating the elements as linear combinations of dictionary templates, low-rank sparse optimization is adopted to achieve the spatio-temporal saliency. For adding the spatial information to the low-rank sparse model, a boundary constraint is adopted in the framework as a Laplacian regularization. A BCLRSR saliency is then obtained by the represented coefficients, which measure the similarity between the elements in the current frame and the ones in the dictionary. At last, the object is segmented by minimizing the energy function, which is formalized by the spatio-temporal coherences. The experiments on some public datasets show that our proposed algorithm outperforms the state-of-the-art methods.

In recent years human-robot collaboration has been an important topic in manufacturing industries. By introducing robots into the same working cell as humans, the advantages of both humans and robots can be utilized. A robot can handle heavy lifting, repetitive and high accuracy tasks while a human can handle tasks that require the flexibility of humans. If a worker is to collaborate with a robot it is important to have an intuitive way of communicating with the robot. Currently, the way of interacting with a robot is through a teaching pendant, where the robot is controlled using buttons or a joystick. However, speech and touch are two communication methods natural to humans, where speech recognition and haptic control technologies can be used to interpret these communication methods. These technologies have been heavily researched in several research areas, including human-robot interaction. However, research of combining these two technologies to achieve a more natural communication in industrial human-robot collaboration is limited. A demonstrator has thus been developed which includes both speech recognition and haptic control technologies to control a collaborative robot from Universal Robots. This demonstrator will function as an experimental platform to further research on how the speech recognition and haptic control can be used in human-robot collaboration. The demonstrator has proven that the two technologies can be integrated with a collaborative industrial robot, where the human and the robot collaborate to assemble a simple car model. The demonstrator has been used in public appearances and a pilot study, which have contributed in further improvements of the demonstrator. Further research will focus on making the communication more intuitive for the human and the demonstrator will be used as the platform for continued research.

This paper presents a novel system using Augmented Reality and Expert Systems to enhance the quality and efficiency of shop-floor operators. The novel system proposed provides an adaptive tool that facilitates and enhances support on the shop-floor, due to its ability to dynamically customize the instructions displayed, dependent upon the competence of the user. A comparative study has been made between an existing method of quality control instructions at a machining line in an automotive engine plant and this novel system. It has been shown that the new approach outcompetes the existing system, not only in terms of perceived usability but also with respect to two other important shop-floor variables: quality and productivity. Along with previous research, the outcomes of these test cases indicate the value of using Augmented Reality technology to enhance shop-floor operators' ability to learn and master new tasks.

A 5 step method for developing configurable “products in product” platforms to be used in an engineer-to-order (ETO) design process is proposed. The idea is based on the transformation of typical product architectures into modular design platforms where standard and variant modules are identified and developed. The platform modules can then be configured and combined with traditional design methods to meet the project specific requirements in the design process. Based on 1193 project designs, several configurable “products in product” were identified and developed in a real case study using the proposed method.

This paper presents a geometry modelling approach to cross-section parameters of chamfered cutting edge on a ball-end mill of solid carbide (BEMSC). Both the cutting edge curve and the CR (chamfer in rake face) face models are derived. Based on the CR face model, a new method for CR face grinding path generation is proposed. By determining the relationship between the length and the angle parameters of the CR face equation, its grinding path can be derived. After solving the rake face equation using this method, its grinding path as well as the grinding paths of the LF (land on flank face) face and the second flank face can also be computed. The geometry model has been validated through a series of numerical simulations.

The high-efficiency utilization of cutting tool resource is closely related to the flexible decision of tool life criterion, which plays a key role in manufacturing systems. Targeting a flexible method to evaluate tool life, this paper presents a data-driven approach considering all the machining quality requirements, e.g., surface integrity, machining accuracy, machining stability, chip control, and machining efficiency. Within the context, to connect tool life with machining requirements, all patterns of tool wear including flank face wear and rake face wear are fully concerned. In this approach, tool life is evaluated systematically and comprehensively. There is no generalized system architecture currently, and a four-level architecture is therefore proposed. Workpiece, cutting condition, cutting parameter, and cutting tool are the input parameters, which constrain parts of the independent variables of the evaluation objective including first-level and second-level indexes. As a result, tool wears are the remaining independent variables, and they are calculated consequently. Finally, the performed processes of the method are experimentally validated by a case study of turning superalloys with a polycrystalline cubic boron nitride (PCBN) cutting tool.

To improve flexibility and accurateness of the optimisation in machining, this paper presents a big data analytics based optimisation method for enriched process planning in the concept of which cutting condition and cutting tool are optimised together and simultaneously. Within the context, the machining factors (workpiece, machining requirement, machine tool, machining process and machining result etc.) are concerned and represented by data attributes. In case that, the new machining resource, new materials and new machining tools etc., can be represented by a group of parameters, so that each machining cases can be treated by data regardless of the relevant experiments, which can enhance practicality and flexibility of potential application in real industry. Also a hybrid method combining neural networks (NN), analytic hierarchy process (AHP), and evolution based algorithm (EBA) or swarm intelligence based algorithm (SIBA) is proposed. NN based model is trained by the big data to improve the accurateness of each single objective, AHP is employed for multi-objective, and EBA or SBA is used to execute the optimising calculation.

For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

The increasing market fluctuations and customized products demand have dramatically changed the focus of industry towards organizational sustainability and supply chain agility. Such critical changes in the strategic vision of the companies inevitably have a direct impact on the shop-floor operational requirements. In this sense, traditional shop-floor approaches are becoming increasingly inadequate leading to the adoption of more pluggable and reusable solutions.

The emergence o modern manufacturing paradigms translates the effort undertaken by the academia in order to provide the required background to support the implementation of such distributed mechatronic systems. Biological systems, due to their similar distributed network-like structure, represent naturally a common analogy and source of inspiration for such distributed modular approaches. Hence, modern manufacturing paradigms usually rely on complexity science biologically inspired concepts to attain distributed control, adaptability, evolution, flexibility and robustness as core concepts. This originated the implementation of a number of different multi-agent based architectures. Nevertheless, with time the majority of the these implementation efforts left behind most of the bio-inspired concepts resulting in simple distributed approaches with considerable limitations regarding scalability, reconfigurability and distributed problem resolution. Particularly under the scope of Evolvable Production System (EPS) the implementation of self-organising mechanisms based on negotiation interaction protocols and dynamic coalition-based hierarchical complexity, have considerable hindered the system performance and limited the full exploitation of the paradigm potential.

In this context, this licentiate thesis is focused on the development of a self-organising manufacturing systems that holistically mimics the main structural and regulatory principles followed by natural systems.

For this purpose, the present approach was designed as opposed to the current tendency followed by modern productions approaches, in which the product holds the production knowledge and is responsible for the management of its own production. Instead, the production knowledge was reduced to the minimum and distributed over the manufacturing components. Self-organising principles heavily inspired on the regulatory mechanisms of biological systems, were then devised to regulate the critical control mechanisms of the manufacturing system. Hence, similarly to the natural world the characteristics and the system overall production emerge as consequence of the micro-dynamics of the systems. In this way, it becomes therefore possible to attain a system that is not only highly reconfigurable and scalable but also able to distributively tackle the manufacturing processes.

Although the present work has been developed under the Evolvable Production System context, the introduced approach can be easily adapted to a wider range of modular networked-based systems.

Monitoring is a significant issue for finishing the assembly interfaces of large-scale components before final assembly. Acquisition and supervision of the pivotal data is essential to ensure the security and reliability for machining the large and complicated components with high-value. This process is generally cumbersome and time-consuming because there are various types of data coming from different components and sensors. The problem becomes more serious when considering the whole shop floor. Recently, MTConnect has been proven to be an effective method to realize standardized data collection and monitoring process. However, MTConnect is still under development and cannot cover the whole finishing process such as on-machining measuring (OMM) and fixturing. To address the issue, an MTConnect compliant method with extended data models is proposed in this paper to implement a standardized monitoring system. Firstly, a finishing system for the assembly interfaces is introduced, including the framework, workflow and key procedures and data. Then extended MTConnect data models are proposed to represent the finishing system including on-machine touch-trigger probe and sensor-based intelligent fixturing related information. Based on the extended MTConnect data models, a web-based monitoring system is developed for data collection and monitoring by combining an MTConnect agent and an OPC adapter. The proposed approach is validated by collecting and monitoring the key process data using an airplane vertical tail as an application. The advantages of using MTConnect would be more significant when extended to the entire factory and implemented in cloud manufacturing in the future.

Improved use of resources is of enormously high relevance and crucial for achieving as well as maintaining a sustainable condition. Especially industrial production has a superior responsibility in creating a long-term viable way of living. However, there is a lack of systematic evaluation procedures for operational resources in production systems, meaning product material, energy, equipment and humans. Especially small and medium-sized enterprises, representing the largest number of all companies in Europe, are mostly not able to deal with these kinds of issues in addition to their daily business.

The purpose of this licentiate thesis is the investigation for and development of an evaluation approach for resource efficiency and effectiveness in small and medium-sized enterprises. Two literature reviews and one industrial case study have been carried out. To start with, the first  literature  review  founds a theoretical  basis  for  the meaning of resource efficiency and effectiveness. The second literature review investigates latest measures and approaches for production system evaluation. In the next step an evaluation approach for resource efficiency and effectiveness evaluation in small and medium-sized enterprises is developed and tested within a single case study. The result consists of a comprehensive approach for small and medium-sized enterprises with focus on economic short-term and long-term improvements.

The concept of circular economy (CE) is to an increasing extent treated as a solution to series of challenges such as waste generation, resource scarcity and sustaining economic benefits. However the concept of circularity is not of novel as such. Specific circumstances and motivations have stimulated ideas relevant to circularity in the past through activities such as reuse, remanufacturing or recycling. Main objectives of this work are: to provide a comprehensive review of research efforts encompassing aspects of resources scarcity, waste generation and economic advantages; to explore the CE landscape in the context of these three aspects especially when they are considered simultaneously; based on an idea of a comprehensive CE framework, propose an implementation strategy using top-down and bottom-up approach in a concurrent manner. To fulfill this objective a comprehensive review of state-of-the-art research is carried out to understand different ideas relevant to CE, motivation for the research and context of their recurrence. Main contributions of this paper are a comprehensive CE framework and a practical implementation strategy for a regenerative economy and natural environment. The framework emphasizes on a combined view of three main aspects i.e. environment, resources and economic benefits. It also underlines that joint support of all stakeholders is necessary in order to successfully implement the CE concept at large scale. The proposed framework and implementation strategy also identify new avenues for future research and practice in the field of CE.

Intuitive and robust multimodal robot control is the key toward human-robot collaboration (HRC) for manufacturing systems. Multimodal robot control methods were introduced in previous studies. The methods allow human operators to control robot intuitively without programming brand-specific code. However, most of the multimodal robot control methods are unreliable because the feature representations are not shared across multiple modalities. To target this problem, a deep learning-based multimodal fusion architecture is proposed in this paper for robust multimodal HRC manufacturing systems. The proposed architecture consists of three modalities: speech command, hand motion, and body motion. Three unimodal models are first trained to extract features, which are further fused for representation sharing. Experiments show that the proposed multimodal fusion model outperforms the three unimodal models. This paper indicates a great potential to apply the proposed multimodal fusion architecture to robust HRC manufacturing systems.

In human-robot collaborative manufacturing, industrial robots would work alongside the human workers who jointly perform the assigned tasks. Recent researches revealed that recognised human motions could be used as input for industrial robots control. However, the information feedback channel from industrial robots to human workers is still limited. In response to the requirement, this research explores the potential of adopting augmented reality (AR) technologies in a worker support system for human-robot collaborative manufacturing. The robot commands and worker instructions can be virtually augmented for human workers intuitively and instantly. The designed AR-based worker support system is demonstrated by a case study.

The surface quality of chamfer milling of stainless steel is closed related to the products of 3C (Computer, Communication and Consumer electronics), where a cutter is a major part to achieve that. Targeting a high-quality cutter, an experimental evaluation is carried out on the influence of grinding texture of cutter flank face on surface quality. The mathematic models of chamfer cutter are established, and they are validated by a numerical simulation. Also the grinding data are generated by the models and tested by a grinding simulation for safety reasons. Then, a set of chamfer cutting tools are machined in a five-axis CNC grinding machine, and consist of five angles between the cutting edge and the grinding texture on the 1st flank faces, i.e., 0A degrees, 15A degrees, 30A degrees, 45A degrees and 60A degrees. Furthermore, the machined cutting tools are tested in a series of milling experiments of chamfer hole of stainless steel, where cutting forces and surface morphologies are measured and observed. The results show that the best state of both surface quality and cutting force is archived by the tool with 45A degrees grinding texture, which can provide a support for manufacturing of cutting tool used in chamfer milling.

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.