Development of a novel feature based manufacturability assessment system for high-volume injection moulding tool inserts

Selective Laser Melting (SLM) is a versatile fabrication method that provides freedom in design complexity through the development of net or near-net shape metallic components within certain limitations such as dimensional accuracy and surface finish. The rapid development of metallic Additive Manufacturing (AM) technologies and their wide-ranging applications facilitate the unprecedented challenges faced by automotive industries for the production of injection moulding tool inserts in timescales greatly reduced from those experienced when manufacturing using more established and conventional processes. It is accepted that AM has limitations with regard to surface roughness requiring post processing of the parts produced. Global demand is striving for the production of injection moulding tool inserts in terms of higher quality. Previous research was applied to investigate the benefits of AM in the production of low-volume injection moulding tool inserts. Potentially, AM could reduce manufacturing lead-time resulting in reduced processing costs while promising high level of flexibility in design. For many years it has been established that companies approved the use of AM for the sole purpose of prototyping and product sampling. Due to lack of knowledge of AM technologies, it has never been fully incorporated as a reliable technique for producing high-volume injection moulding tool inserts for the automotive industry, due to implications of previous research on surface finish of AM components, limitations in material use, durability, and incapability of improving product accuracy. Previous research was established for the production of low-volume injection moulding tool inserts. However, there is still a gap in research regarding the capabilities of AM technologies for the production of high-volume injection moulding tool inserts. Moreover, applying each manufacturing process individually is constrained by some technical limitations, therefore, establishing a paradigm that evaluates the manufacturability benefits of AM and subtractive manufacturing in a feature-based system is potentially valuable. This research addresses the competencies associated with adopting SLM for fabricating injection moulding tool inserts for high-volume production, and how advantageous it can be for the automotive industry. In this work, the tool life of SLM-fabricated injection moulding tool inserts and the functional approval of their respective end-products is analysed. Five sets of tool inserts (ten core and cavity inserts) of different spare part automotive components were manufactured using subtractive and SLM techniques. The tool inserts were grouped into different studies that assessed mechanical properties, microstructure, and performance when used to create end-use components. One of the studies was established to prove the tool life of SLM-fabricated tool inserts through the production of 150,000 functional components. The tool inserts performance was monitored under actual operating conditions considering high-level demands. The quality of the components produced from the SLM tool inserts were tested for geometric and dimensional accuracy as well as functional approval through an industrial quality control procedure as an end-use product. Products are functionally approved and are established to be within the permissible design tolerances for their application and industrial sector requirements. The results obtained from the different studies concluded that SLM is a viable and competitive approach for the fabrication of injection moulding tool inserts. Hence, a systematic approach is developed as a feature-based manufacturability assessment system (FBMAS) for the automotive sector to assist users to evaluate manufacturability limitations of SLM and subtractive manufacturing techniques for the production of injection moulding tool inserts. The manufacturability assessment process is based on a set of predetermined design features and geometric requirements which must be identified. Six tool inserts were acquired for the validation process, comparing real-life decisions of the experienced engineers with the outcome of the feature-based system. As a result, the manufacturability assessment system was able to present possible feature base recommendations for the manufacturability of high–volume injection tool inserts

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

A Short Review on 4D Printing

Additive Manufacturing can be described as a process to build 3D objects by adding layer-upon-layer of material, the material traditionally being plastics, metals or ceramics, however ‘smart’ materials are now in use. Nowadays, the term “3D Printing” has become a much-used synonym for additive manufacturing. The use of computing, 3D solid modeling applications, layering materials and machine equipment is common to majority of additive manufacturing technologies. Advancing from this 3D printing technology, is an emerging trend for what is being termed “4D printing”. 4D printing places dependency on smart materials, the functionality of additive manufacturing machines and in ingenious design processes. Although many developments have been made, limitations are still very much in existence, particularly with regards to function and application. The objective of this short review is to discuss the developments, challenges and outlook for 4D printing technology. The review revealed that 4D printing technology has application potential but further research work will be vital for the future success of 4D printing

Study on Parametric Optimization of Fused Deposition Modelling (FDM) Process

Rapid prototyping (RP) is a generic term for a number of technologies that enable fabrication of physical objects directly from CAD data sources. In contrast to classical methods of manufacturing such as milling and forging which are based on subtractive and formative principles espectively, these processes are based on additive principle for part fabrication. The biggest advantage of RP processes is that an entire 3-D (three-dimensional) consolidated assembly can be fabricated in a single setup without any tooling or human intervention; further, the part fabrication methodology is independent of the mplexity of the part geometry. Due to several advantages, RP has attracted the considerable attention of manufacturing industries to meet the customer demands for incorporating continuous and rapid changes in manufacturing in shortest possible time and gain edge over competitors. Out of all commercially available RP processes, fused deposition modelling (FDM) uses heated thermoplastic filament which are extruded from the tip of nozzle in a prescribed manner in a temperature controlled environment for building the part through a layer by layer deposition method. Simplicity of operation together with the ability to fabricate parts with locally controlled properties resulted in its wide spread application not only for prototyping but also for making functional parts. However, FDM process has its own demerits related with accuracy, surface finish, strength etc. Hence, it is absolutely necessary to understand the shortcomings of the process and identify the controllable factors for improvement of part quality. In this direction, present study focuses on the improvement of part build methodology by properly controlling the process parameters. The thesis deals with various part quality measures such as improvement in dimensional accuracy, minimization of surface roughness, and improvement in mechanical properties measured in terms of tensile, compressive, flexural, impact strength and sliding wear. The understanding generated in this work not only explain the complex build mechanism but also present in detail the influence of processing parameters such as layer thickness, orientation, raster angle, raster width and air gap on studied responses with the help of statistically validated models, microphotographs and non-traditional optimization methods. For improving dimensional accuracy of the part, Taguchi‟s experimental design is adopted and it is found that measured dimension is oversized along the thickness direction and undersized along the length, width and diameter of the hole. It is observed that different factors and interactions control the part dimensions along different directions. Shrinkage of semi molten material extruding out from deposition nozzle is the major cause of part dimension reduction. The oversized dimension is attributed to uneven layer surfaces generation and slicing constraints. For recommending optimal factor setting for improving overall dimension of the part, grey Taguchi method is used. Prediction models based on artificial neural network and fuzzy inference principle are also proposed and compared with Taguchi predictive model. The model based on fuzzy inference system shows better prediction capability in comparison to artificial neural network model. In order to minimize the surface roughness, a process improvement strategy through effective control of process parameters based on central composite design (CCD) is employed. Empirical models relating response and process parameters are developed. The validity of the models is established using analysis of variance (ANOVA) and residual analysis. Experimental results indicate that process parameters and their interactions are different for minimization of roughness in different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi-response performance index (MPI) using principal component analysis. Bacterial foraging optimisation algorithm (BFOA), a latest evolutionary approach, has been adopted to find out best process parameter setting which maximizes MPI. Assessment of process parameters on mechanical properties viz. tensile, flexural, impact and compressive strength of part fabricated using FDM technology is done using CCD. The effect of each process parameter on mechanical property is analyzed. The major reason for weak strength is attributed to distortion within or between the layers. In actual practice, the parts are subjected to various types of loadings and it is necessary that the fabricated part must withhold more than one type of loading simultaneously.To address this issue, all the studied strengths are combined into a single response known as composite desirability and then optimum parameter setting which will maximize composite desirability is determined using quantum behaved particle swarm optimization (QPSO). Resistance to wear is an important consideration for enhancing service life of functional parts. Hence, present work also focuses on extensive study to understand the effect of process parameters on the sliding wear of test specimen. The study not only provides insight into complex dependency of wear on process parameters but also develop a statistically validated predictive equation. The equation can be used by the process planner for accurate wear prediction in practice. Finally, comparative evaluation of two swarm based optimization methods such as QPSO and BFOA are also presented. It is shown that BFOA, because of its biologically motivated structure, has better exploration and exploitation ability but require more time for convergence as compared to QPSO. The methodology adopted in this study is quite general and can be used for other related or allied processes, especially in multi input, multi output systems. The proposed study can be used by industries like aerospace, automobile and medical for identifying the process capability and further improvement in FDM process or developing new processes based on similar principle

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

Digital Twins in Industry

Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies