DIG-MAN: Integration of digital tools into product development and manufacturing education

General objectives of PRODEM education. Teaching of product development requires various digital tools. Nowadays, the digital tools usually use computers, which have become a standard element of manufacturing and teaching environments. In this context, an integration of computer-based technologies in manufacturing environments plays the crucial and main role, allowing to enrich, accelerate and integrate different production phases such as product development, design, manufacturing and inspection. Moreover, the digital tools play important role in management of production. According to Wdowik and Ratnayake (2019 paper: Open Access Digital Tool’s Application Potential in Technological Process Planning: SMMEs Perspective, https://doi.org/10.1007/978-3-030-29996-5_36), the digital tools can be divided into several main groups such as: machine tools and technological equipment (MTE), devices (D), internet(intranet)-based tools (I), software (S). The groups are presented in Fig. 1.1. Machine tools and technological equipment group contains all existing machines and devices which are commonly used in manufacturing and inspection phase. The group is used in physical shaping of manufactured products, measurement tasks regarding tools and products, etc. The next group of devices (D) is proposed to separate the newest trends of using mobile and computer-based technologies such as smartphones or tablets and indicate the necessity of increased mobility within production sites. The similar need of separation is in the case of internet(intranet)-based tools which indicate the growing interest in network-based solutions. Hence, D and I groups are proposed in order to underline the significance of mobility and networking. These two groups of the digital tools should also be supported in the nearest future by the use of 5G networks. The last group of software (S) concerns computer software produced for the aims of manufacturing environments. There is also a possibility to assign the defined solutions (e.g. computer programs) to more than one group (e.g. program can be assigned to software and internet-based tools). The main role of tools allocated inside separate groups is to support employees, managers and customers of manufacturing firms focused on abovementioned production phases. The digital tools are being developed in order to increase efficiency of production, quality of manufactured products and accelerate innovation process as well as comfort of work. Nowadays, digital also means mobile. Universities (especially technical), which are focused on higher education and research, have been continuously developing their teaching programmes since the beginning of industry 3.0 era. They need to prepare their alumni for changing environments of manufacturing enterprises and new challenges such as Industry 4.0 era, digitalization, networking, remote work, etc. Most of the teaching environments nowadays, especially those in manufacturing engineering area, are equipped with many digital tools and meet various challenges regarding an adaptation, a maintenance and a final usage of the digital tools. The application of these tools in teaching needs a space, staff and supporting infrastructures. Universities adapt their equipment and infrastructures to local or national needs of enterprises and the teaching content is usually focused on currently used technologies. Furthermore, research activities support teaching process by newly developed innovations. Figure 1.2 presents how different digital tools are used in teaching environments. Teaching environments are divided into four groups: lecture rooms, computer laboratories, manufacturing laboratories and industrial environments. The three groups are characteristic in the case of universities’ infrastructure whilst the fourth one is used for the aims of internships of students or researchers. Nowadays lecture rooms are mainly used for lectures and presentations which require the direct communication and interaction between teachers and students. However, such teaching method could also be replaced by the use of remote teaching (e.g. by the use of e-learning platforms or internet communicators). Unfortunately, remote teaching leads to limited interaction between people. Nonverbal communication is hence limited. Computer laboratories (CLs) usually gather students who solve different problems by the use of software. Most of the CLs enable teachers to display instructions by using projectors. Physical gathering in one room enables verbal and nonverbal communication between teachers and students. Manufacturing laboratories are usually used as the demonstrators of real industrial environments. They are also perfect places for performing of experiments and building the proficiency in using of infrastructure. The role of manufacturing labs can be divided as: • places which demonstrate the real industrial environments, • research sites where new ideas can be developed, improved and tested. Industrial environment has a crucial role in teaching. It enables an enriched student experience by providing real industrial challenges and problems

Multiple 2D self organising map network for surface reconstruction of 3D unstructured data

Surface reconstruction is a challenging task in reverse engineering because it must represent the surface which is similar to the original object based on the data obtained. The data obtained are mostly in unstructured type whereby there is not enough information and incorrect surface will be obtained. Therefore, the data should be reorganised by finding the correct topology with minimum surface error. Previous studies showed that Self Organising Map (SOM) model, the conventional surface approximation approach with Non Uniform Rational B-Splines (NURBS) surfaces, and optimisation methods such as Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimisation (PSO) methods are widely implemented in solving the surface reconstruction. However, the model, approach and optimisation methods are still suffer from the unstructured data and accuracy problems. Therefore, the aims of this research are to propose Cube SOM (CSOM) model with multiple 2D SOM network in organising the unstructured surface data, and to propose optimised surface approximation approach in generating the NURBS surfaces. GA, DE and PSO methods are implemented to minimise the surface error by adjusting the NURBS control points. In order to test and validate the proposed model and approach, four primitive objects data and one medical image data are used. As to evaluate the performance of the proposed model and approach, three performance measurements have been used: Average Quantisation Error (AQE) and Number Of Vertices (NOV) for the CSOM model while surface error for the proposed optimised surface approximation approach. The accuracy of AQE for CSOM model has been improved to 64% and 66% when compared to 2D and 3D SOM respectively. The NOV for CSOM model has been reduced from 8000 to 2168 as compared to 3D SOM. The accuracy of surface error for the optimised surface approximation approach has been improved to 7% compared to the conventional approach. The proposed CSOM model and optimised surface approximation approach have successfully reconstructed surface of all five data with better performance based on three performance measurements used in the evaluation

Geometry modeling and grid generation using 3D NURBS control volume

The algorithms for volume grid generation using NURBS geometric representation are presented. The parameterization algorithm is enhanced to yield a desired physical distribution on the curve, surface and volume. This approach bridges the gap between CAD surface/volume definition and surface/volume grid generation. Computational examples associated with practical configurations have shown the utilization of these algorithms

Coaxing a planar curve to comply

AbstractA long-standing problem in computer graphics is to find a planar curve that is shaped the way you want it to be shaped. A selection of various methods for achieving this goal is presented. The focus is on mathematical conditions that we can use to control curves while still allowing the curves some freedom. We start with methods invented by Newton (1643–1727) and Lagrange (1736–1813) and proceed to recent methods that are the subject of current research. We illustrate almost all the methods discussed with diagrams. Three methods of control that are of special interest are interpolation methods, global minimization methods (such as least squares), and (Bézier) control points. We concentrate on the first of these, interpolation methods

Parallel mesh adaptive techniques for complex flow simulation: geometry conservation

Dynamic mesh adaptation on unstructured grids, by localised refinement and derefinement, is a very efficient tool for enhancing solution accuracy and optimising computational time. One of the major drawbacks, however, resides in the projection of the new nodes created, during the refinement process, onto the boundary surfaces. This can be addressed by the introduction of a library capable of handling geometric properties given by a CAD (computer-aided design) description. This is of particular interest also to enhance the adaptation module when the mesh is being smoothed, and hence moved, to then reproject it onto the surface of the exact geometry

Footwear bio-modelling: An industrial approach

There is a growing need within the footwear sector to customise the design of the last from which a specific footwear style is to be produced. This customisation is necessary for user comfort and health reasons, as the user needs to wear a suitable shoe. For this purpose, a relationship must be established between the user foot and the last with which the style will be made; up until now, no model has existed that integrates both elements. On the one hand, traditional customised footwear manufacturing techniques are based on purely artisanal procedures which make the process arduous and complex; on the other hand, geometric models proposed by different authors present the impossibility of implementing them in an industrial environment with limited resources for the acquisition of morphometric and structural data for the foot, apart from the fact that they do not prove to be sufficiently accurate given the non-similarity of the foot and last. In this paper, two interrelated geometric models are defined, the first, a bio-deformable foot model and the second, a deformable last model. The experiments completed show the goodness of the model, with it obtaining satisfactory results in terms of comfort, efficiency and precision, which make it viable for use in the sector