CONALI ontology. A framework for design and evaluation of constructively aligned courses in higher education: putting in focus the educational goal verbs

An increasing number of Higher Education professionals have embraced the Constructivism theory in contrast with the traditional transmissive pedagogy approach where the focal figure is the teacher. Constructivists emphasizes that the learners acquire, or construct, knowledge through their own activities and previous knowledge. Teacher role is to set up an environment that can provide a good learning experience for the students. In view of this the alignment of the intended learning outcome (ILO) with the teaching and learning activity (TLA) and the assessment task (AT) of the course becomes an important requirement for good learning. The driver of the alignment is the educational goal verb (EGV) that represents the educational goal underling a specific intended learning outcome (ILO). This verb should be elicited by the course’s TLA and be the base for the consequent AT. The convergence of constructivism with this concept generates the constructive alignment pedagogical paradigm. The CONALI ontology answers the requirement for a structured framework to describe the vast body of knowledge developed in such a field. The salient aspects of constructive alignment have been extracted and classified in a comprehensive taxonomy. The following description of the semantic relationships among the different classes resulted in the CONALI ontology. The chosen modelling language is OWL: this provides the possibility to describe in a computer understandable way a higher education courses to an unprecedented level of detail. OWL enables also the creation of a specific knowledge base by populating the model. The knowledge base can then be analysed and interrogated on many important issues concerning the alignment of the instantiated course. The CONALI ontology becomes an important tool to design and synthesize the related domain knowledge. This paper proves the usability of CONALI ontology as tool to represent the courses in an engineering program and evaluate the alignment of their activities. The specific instantiation is based on the Industrial Engineering program at KTH Royal Institute of Technology in Stockholm, Sweden

An economic index for measuring firm’s circularity: the case of water industry

Transition towards circular-economy model is a must to sustain the planet resources. Under circular economy model wastewater is transformed from a waste into a resource. Therefore, a comprehensive circular economy index; the Circonomics Index, is proposed to measure circularity of wastewater industry. The component indicators of the index are linked directly to the three Rs; reduce, reuse and recycle, of circular economy. The novelty of the proposed Index is that it uses objectively constructed weights that reflect the environmental benefits of the treatment process, and the index captures the reuse and recycling efficiency of an WWTP, which reflect the specific nature of wastewater. The findings show that treatment technology is a major factor in determining the production efficiency, reuse rate and recycling performance of a WWTP. The results of using the Circonomics Index have profound implication for policy makers to speed up the process of moving to a circular economy

Model-Based Investigation of Machining Systems Characteristics : Static and Dynamic Stability Analysis

The increasing demands for precision and efficiency in machining call for new control strategies for machining systems based on the identification of static and dynamic characteristics under operational conditions. By considering the machining system as a closed-loop system consisting of a machine tool structure and a machining process, the join system characteristics can be analyzed. The capability of a machining system is mainly determined by its static and dynamic stiffness. The goal of this thesis is to introduce some concepts and methods regarding the identification of machining system stability. Two methods are discussed, one for the static behaviour analysis of a machine tool, and one for dynamic stability of a machining system. Preliminary results are indicating unambiguous identification of capabilities of machining systems static and dynamic characteristics. The static behaviour of a machine tool is evaluated by use of a loaded double ball bar (LDBB) device. The device reproduces the real interaction between the join system, the machine tool elastic structure and the cutting process. This load is not equivalent to real cutting forces, but it does have a similar effect on the structure. This has been investigated both trough simulation and experimental work. It is possible to capture the process – ­machine interaction in a machining system by use of the model-based identification approach. The identification approach takes into consideration this interaction and can therefore be used to characterize the machining system under operational conditions. The approach provides realistic prerequisites for in-process machining system testing. The model parameters can be further employed for control and optimization of the cutting process. Using different classification schemes, the model-based identification method is promising for the detection of instability. Furthermore, it is the author’s belief that a model-based stability analysis approach is needed to exploit the full potential of a model driven parts manufacturing approach.QC 2010110

A Computational Framework for Control of Machining System Capability : From Formulation to Implementation

Comprehensive knowledge and information about the static and dynamic behaviour of machine tools, cutting processes and their interaction is essential for machining system design, simulation, control and robust operation in safe conditions. The very complex system of a machine tool, fixture and cutting tools during the machining of a part is almost impossible to model analytically with sufficient accuracy. In combination with increasing demands for precision and efficiency in machining call for new control strategies for machining systems. These strategies need to be based on the identification of the static and dynamic stability under both the operational and off-operational conditions. To achieve this it is necessary to monitor and analyze the real system at the factory floor in full production. Design information and operational data can then be linked together to make a realistic digital model of a given machining system. Information from such a model can then be used as input in machining simulation software to find the root causes of instability. The work presented in this thesis deals with the static and dynamic capability of machining systems. The main focus is on the operational stability of the machining system and structural behaviour of only the machine tool, as well. When the accuracy of a machining system is measured by traditional techniques, effects from neither the static stiffness nor the cutting process are taken into account. This limits the applicability of these techniques for realistic evaluation of a machining system’s accuracy. The research presented in this thesis takes a different approach by introducing the concept of operational dynamic parameters. The concept of operational dynamic parameters entails an interaction between the structural elements of the machining systems and the process parameters. According to this concept, the absolute criterion of damping is used to evaluate the dynamic behaviour of a machining system. In contrast to the traditional theory, this methodology allows to determine the machining system's dynamic stability, in real time under operating conditions. This framework also includes an evaluation of the static deformations of a machine tool.  In this context, a novel concept of elastically linked system is introduced to account for the representation of the cutting force trough an elastic link that closes the force loop. In addition to the elastic link which behaves as a static element, a dynamic non-contact link has been introduced. The purpose is to study the non-linear effects introduced by variations of contact conditions in joints due to rotational speed.QC 2011112

Condition monitoring of rolling element bearings: benchmarking of data-driven methods

Condition-based maintenance (CBM) is a maintenance strategy used to gain updated information about equipment condition and is today considered a natural part of the engineering field. The replacement of the traditional scheduled maintenance strategy in favor of CBM has the potential to significantly improve the safety of the system operating in harsh environments of the operation and increase in productivity by prolonging the life of an asset and preventing costly breakdowns. For many years CBM remained the subject of vigorous research and discussions. Increasing the automation level and the number of sensors in industries allowed obtaining and collecting data in large amounts. The current level of computational power allows us to process and analyse this massive amount of data, which has given a new leap in the development of industrial analytics. Rather than in the case of classical knowledge-based modelling tools, data-driven methods propose modelling and forecasting frameworks based on data analysis. Consequently, the transition to data-driven modelling gave a leap in CBM research and has recently drawn increasing attention, providing new case studies, algorithms, and results. However, technical challenges remain. Despite great flexibility and good forecasting performances, there are several limitations of data-driven algorithms. This paper provides an overview of the data-driven failure algorithms for rolling element bearings monitoring. Bearings have played a pivotal role in industrial machinery to operate with high efficiency and safety. They are considered to be one of the most common machine elements of precision rotating machinery. A benchmarking of various predictive and descriptive algorithms was performed. The analysis was carried out on a dataset from the run-to-failure experiments on bearings from NASA's Data Repository. This paper also summarizes the current trends and highlights the limitations with respect to traditional knowledge-based modelling. Special attention is paid to identifying research gaps and promising research directions.QC 20210521</p

Assessment of Fault Detection and Monitoring Techniques for Effective Digitalization

As a result of digitalization, data is collected at every level of production as an enhancer for decision-making. However, including more sensors to collect additional information does not directly contribute to increasing the system reliability but instead raises challenges for optimal data utilization. This work presents an evaluation approach based on FMSA (Failure mode and symptoms analysis) combined with FMECA (Failure mode, effects and criticality analysis) prioritization methods. The different methods are applied to a feed-drive system to evaluate the suitability of the currently implemented detection and monitoring techniques. The recommendations derived from the evaluation can be utilized to maximize confidence in the monitoring and to minimize the sensors utilization and data collection. Since the FMEA family of assessment tools present shortcomings such as bias and uncertainty associated with their results, this work also aims at mitigating these effects in obtaining the monitoring priority numbers and their respective categorization and prioritization.Part of ISBN 978-981-18-8071-1QC 20231220RoD

In-situ prediction of the spatial surface roughness profile during slot milling

Quality inspection is traditionally considered non-productive. That is why the manufacturing industries aim to decrease inspection times to a bare minimum without sacrificing part quality. Alongside the implementation of the Industry 4.0 paradigm, data-driven in-situ quality control is a potential enabler for minimizing inspection times. In that, the surface roughness parameter prediction is the subject of a large body of research, but studies on the spatial surface roughness profile prediction are limited. This research contributes to this field by using vibration signals and physics-informed machine learning models for the in-situ prediction of the surface roughness profile. A tri-axial accelerometer mounted on the machine tool spindle is used to capture the vibrations during a slot milling process. For one tool revolution during a stable cut, the observed acceleration in the three axes and the surface roughness profile are periodic. A model is constructed to establish the correlation between the input signals and the spatial surface roughness profile by utilizing a physics-based model of the tool trajectory together with a two-layer feed-forward neural network. Furthermore, the feature engineering of denoised velocities and displacements derived by the numerical integration of the acceleration signals improves the prediction performance with overfitting. The results show a good correlation between the spatial surface roughness and the accelerometer signals

HYBRID MACHINING : ABRASIVE WATERJET TECHNOLOGIES USED IN COMBINATION WITH CONVENTIONAL METAL CUTTING

Abrasive Waterjet technology is one of the fastest growing metal cutting technologies. Even so, very little published material is available on hybrid processing where abrasive waterjet cutting is one of two or more metal cutting methods. There is also limited published material on thin-walled components cut with abrasive waterjet technology. This paper makes a comparison of conventional metal cutting methods to the more unconventional abrasive waterjet technique. It will serve as a stepping stone in building knowledge aiding in hybrid machining development. It will show the possibilities and limitations during milling of thin-walled Aluminum components and then compare this to the capabilities of abrasive waterjet cutting the same components. Differences in material removal and revert control as well as in vibrations and force requirements will be reviewed. In addition, the environmental issues will be discussed and it will be determined which of the methods is more sustainable. The paper also includes a large section on process methodology.QC 20171101</p

Parametric and Non-Parametric Identification of Micromilling Dynamics

Monitoring and control of micromilling process represents a challenging task in metal cutting. The lack of static and dynamic stiffness due to the small tool size represents one of the most important issues that has to be tackled in order to provide satisfactory control of the process. In this paper different methods for identifying micro-end milling dynamics for process monitoring are proposed. On one side a parametric approach based on the identification of machining system ODPs (Operation Dynamic Parameters) has been designed. On the other hand a non-parametric approach, based on the calculation of system Lyapunov exponents has been also tested to verify whether generalized methods for assessing system dynamics are also applicable in micromachining where the process nonlinearity’s can become relevant, thus limiting the effectiveness of other monitoring methods