A k-nearest neighbour technique for experience-based adaptation of assembly stations

YesWe present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions

Learning and reuse of engineering ramp-up strategies for modular assembly systems

YesWe present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.Funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project

A multi-agent architecture for plug and produce on an industrial assembly platform

YesModern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems.The reported research has been part of the EU FP7 research project “PRIME

Modular reconfiguration of flexible production systems using machine learning and performance estimates

YesThis paper presents an agent-based framework for reconfiguring modular assembly systems using machine learning and system performance estimates based on previous reconfigurations. During a reconfiguration, system integrators and engineers make changes to the machine to meet new production requirements by increasing capacity or manufacturing new product variants. The framework provides a method for automatically evaluating these changes in terms of impact on the performance of the production system, and building a knowledge base. Such knowledge is used to support future reconfigurations by recommending changes that are likely to improve the performance based on previous reconfigurations. The agent architecture of the framework has two levels, one for individual assembly stations and one for the entire production line. Knowledge bases of changes are built and utilised at both levels using machine learning and performance estimates. A prototype implementation of the proposed framework has been evaluated on an assembly production system in an industrial scenario. Preliminary results show that framework helps to reduce the time and resources required to complete a system reconfiguration and reach the desired production objectives.This work was supported by the SURE Research Projects Fund of the University of Bradford and the European Commission [grant agreement n. 314762]

An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios

Automated experience-based learning for plug and produce assembly systems

YesThis paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.European Union [grant number 314762]

Early diagnosis and personalised treatment focusing on synthetic data modelling: Novel visual learning approach in healthcare

YesThe early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting in suboptimal diagnosis. To address the impacts of data challenges, improve resource allocation, and achieve better health outcomes, a novel visual learning approach is proposed. This study contributes to the visual learning approach by determining whether less or more synthetic data are required to improve the quality of a dataset, such as the number of observations and features, according to the intended personalised treatment and early diagnosis. In addition, numerous visualisation experiments are conducted, including using statistical characteristics, cumulative sums, histograms, correlation matrix, root mean square error, and principal component analysis in order to visualise both original and synthetic data to address the data challenges. Real medical datasets for cancer, heart disease, diabetes, cryotherapy and immunotherapy are selected as case studies. As a benchmark and point of classification comparison in terms of such as accuracy, sensitivity, and specificity, several models are implemented such as k-Nearest Neighbours and Random Forest. To simulate algorithm implementation and data, Generative Adversarial Network is used to create and manipulate synthetic data, whilst, Random Forest is implemented to classify the data. An amendable and adaptable system is constructed by combining Generative Adversarial Network and Random Forest models. The system model presents working steps, overview and flowchart. Experiments reveal that the majority of data-enhancement scenarios allow for the application of visual learning in the first stage of data analysis as a novel approach. To achieve meaningful adaptable synergy between appropriate quality data and optimal classification performance while maintaining statistical characteristics, visual learning provides researchers and practitioners with practical human-in-the-loop machine learning visualisation tools. Prior to implementing algorithms, the visual learning approach can be used to actualise early, and personalised diagnosis. For the immunotherapy data, the Random Forest performed best with precision, recall, f-measure, accuracy, sensitivity, and specificity of 81%, 82%, 81%, 88%, 95%, and 60%, as opposed to 91%, 96%, 93%, 93%, 96%, and 73% for synthetic data, respectively. Future studies might examine the optimal strategies to balance the quantity and quality of medical data

Efficient Finite Element Mesh Mapping Using Octree Indexing

NoModern manufacturing involves multiple stages of complex process chains where Finite Element Analysis is frequently used as a simulation method on a discretized mesh to provide an accurate estimation of factors such as stresses, strains, and displacements. The choice of the most suitable element type and density is dependent on the individual manufacturing process or treatment applied at each stage of the process chain. To map between unalike Finite Element meshes, differing in density and/or element type, an Octree spatial index was evaluated as a solution for highly performant mapping. Compared to existing solutions, the Octree spatial index introduces parallelism within index creation and provides a strategy to perform the most complex interpolation technique, Element Shape Function, in a more computationally efficient manner

Compile-time Computation of Polytime Functions

We investigate the computational power of C++ compilers. In particular, it is known that any partial recursive function can be computed at compile time, using the template mechanism to define primitive recursion, composition, and minimalization. We show that polynomial time computable functions can be computed at compile-time using the same mechanism, together with template specialization