An intelligent technology selection algorithm for complex decision environments – a unique knowledge based approach

This paper investigates the role of an intelligent technology selection algorithm for complex decision environments. The elicitation and formulization of expert decision thinking is presented in support of a unique approach to the subject. The problem definition is defined and an overview of the current state of the art provides a background into the subject area. The notion utilizes a knowledge base in prior selection of criteria ratings in a fuzzy ruled base system for analytic factors. The approach aims to optimize investment portfolios at manufacturing organizations by providing an efficient and quality decision-making process

A self-learning case and rule-based reasoning algorithm for intelligent technology evaluation and selection [Abstract]

This research programme proposes to fulfill the existing gap in knowledge by providing an experience-oriented decision algorithm to solve technology selection problems based on cases and expert’s experience. The approach adopts historical case-based data to extract rules through the ID3 rule induction algorithm. The decision model integrates a rule induction approach in a rule-based knowledge system and database management system to support automated knowledge mining and usage. The adoption of a pair-wise comparison algorithm within the similarity index assists in relating the importance of the criteria within the knowledgebases reasoner. A series of historical and new solutions are presented in a scoring index based on the requirements of a new case

A symbiotic human–machine learning approach for production ramp-up

Constantly shorter product lifecycles and the high number of product variants necessitate frequent production system reconfigurations and changeovers. Shortening ramp-up and changeover times is essential to achieve the agility required to respond to these challenges. This work investigates a symbiotic human–machine environment, which combines a formal framework for capturing structured ramp-up experiences from expert production engineers with a reinforcement learning method to formulate effective ramp-up policies. Such learned policies have been shown to reduce unnecessary iterations in human decision-making processes by suggesting the most appropriate actions for different ramp-up states. One of the key challenges for machine learning based methods, particularly for episodic problems with complex state-spaces, such as ramp-up, is the exploration strategy that can maximize the information gain while minimizing the number of exploration steps required to find good policies. This paper proposes an exploration strategy for reinforcement learning, guided by a human expert. The proposed approach combines human intelligence with machine’s capability for processing data quickly, accurately, and reliably. The efficiency of the proposed human exploration guided machine learning strategy is assessed by comparing it with three machine-based exploration strategies. To test and compare the four strategies, a ramp-up emulator was built, based on system experimentation and user experience. The results of the experiments show that human-guided exploration can achieve close to optimal behavior, with far less data than what is needed for traditional machine-based strategies

Innovation landscape and challenges of smart technologies and systems - a European perspective

Latest developments in smart sensor and actuator technologies are expected to lead to a revolution in future manufacturing systems’ abilities and efficiency, often referred to as Industry 4.0. Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. Feedback from a workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing

Influencing factors for implementing automation in manufacturing businesses – a literature review

The latest developments in Robotics and Autonomous Systems (RAS) are expected to lead to a transformation of future production systems’ capabilities and productivity. While increased human-robot collaboration as well as higher degrees of autonomous systems within a manufacturing context will be essential to achieve the next breakthrough in both agility as well as productivity, they will pose significant new challenges for how production systems are planned and engineered to maximise the potential and minimise the risks of this new technology for manufacturing businesses. Therefore, a main focus of this review was on determining the critical success factors for the implementation of RAS and on gaining a deeper understanding of the current research focus. The research results lead to a broader discussion of the implications arising from future automation and human-robot collaboration which highlights the current limitation of decision making criteria considered in the current literature. The results of the review have been quantitatively verified with the use of the text mining tool WordSmith Tool (v7.0)

Dynamic vs dedicated automation systems - a study in large structure assembly

The manufacturing industry needs to increase productivity and flexibility to stay competitive. This requires more adaptable and versatile production capabilities. It is expected that dynamic systems, consisting of mobile robots, will be particularly prominent in manufacturing environments where it is difficult to move components and products in a flexible manner. This paper compares the relative advantages of a dynamic, mobile robot-based system with traditional dedicated automation systems. The study uses simulations to evaluate several representative scenarios with different product supply bottlenecks, interference among mobile robots and mixes of products inspired by the aerospace industry. The results show that mobility enables higher resource utilisation and increased flexibility. This highlights the potential operational advantages mobile robot-based systems would offer and gives clear justification to continue the development of dynamic, self-organising production systems based on mobile robots

A comparison of the manufacturing resilience between fixed automation systems and mobile robots in large structure assembly

The modern manufacturing industry is undergoing major transformations due to global competition and rapidly changing market demands. Traditional systems with rigid structures are very difficult to reconfigure every time a change in production is required. A promising alternative to these is seen in mobile, self-organising manufacturing systems, where self-deploying and independent entities such as mobile robots are used to facilitate a more reconfigurable assembly process. In addition, an integral part of manufacturing is the transportation of components within the manufacturing environment. Conveyor systems are often unsuitable for moving components that are large, heavy or awkward, making them difficult to use in large structure assembly. Currently, such components are commonly transported by cranes to dedicated automation systems which are seen as expensive and unadaptable. In this paper we investigate the differences in resilience to variations between a set of mobile robots and the widely accepted fixed automation systems under different conditions. Therefore, instead of transporting components or parts to manufacturing equipment we analyse the potential benefits of transporting the equipment to the large parts. By means of simulations, the two systems are compared to one-another in scenarios of identical part arrival times and part processing capacities. Assuming equal production rates, we assess their ability to respond to (1) rush orders, (2) variable arrival times and (3) production mix variation. Currently, there are no specific algorithms for process control of such mobile systems. For this reason we apply the First-In-First-Out task-selection rule. We present a comparison of resilience measures between the systems

Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors

In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending angle during actuation. An experimental setup was prepared to test the soft actuators under controllable operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending angles measured using a developed image processing program. Regression analysis and neural networks are two data-driven modelling techniques that were implemented and compared in this study, to evaluate their ability in predicting the bending angle response of the tested soft actuators at different input pressures and testing orientations. This serves as a step towards controlling this class of soft bending actuators, using data-driven empirical models that lifts the need for complex analytical modelling and material characterisation. The aim is to ultimately create a more controllable version of this class of soft pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation

Function-behaviour-structure model for modular assembly equipment

Reconfigurable modular manufacturing systems provide a solution to manage current challenges of dynamic, customer driven markets. Powerful methods are needed for rapid configuration of system. This research focuses on the ontological definition of modular assembly device domain knowledge which builds the foundation for such methods. In this word formal representations will be defined based on linked models of functions, behaviour and structure of the equipment modules. The method will be discussed using an illustrative example

Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors - a data-driven approach

In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation