Digital fabrication of custom interactive objects with rich materials

As ubiquitous computing is becoming reality, people interact with an increasing number of computer interfaces embedded in physical objects. Today, interaction with those objects largely relies on integrated touchscreens. In contrast, humans are capable of rich interaction with physical objects and their materials through sensory feedback and dexterous manipulation skills. However, developing physical user interfaces that offer versatile interaction and leverage these capabilities is challenging. It requires novel technologies for prototyping interfaces with custom interactivity that support rich materials of everyday objects. Moreover, such technologies need to be accessible to empower a wide audience of researchers, makers, and users. This thesis investigates digital fabrication as a key technology to address these challenges. It contributes four novel design and fabrication approaches for interactive objects with rich materials. The contributions enable easy, accessible, and versatile design and fabrication of interactive objects with custom stretchability, input and output on complex geometries and diverse materials, tactile output on 3D-object geometries, and capabilities of changing their shape and material properties. Together, the contributions of this thesis advance the fields of digital fabrication, rapid prototyping, and ubiquitous computing towards the bigger goal of exploring interactive objects with rich materials as a new generation of physical interfaces.Computer werden zunehmend in Geräten integriert, mit welchen Menschen im Alltag interagieren. Heutzutage basiert diese Interaktion weitgehend auf Touchscreens. Im Kontrast dazu steht die reichhaltige Interaktion mit physischen Objekten und Materialien durch sensorisches Feedback und geschickte Manipulation. Interfaces zu entwerfen, die diese Fähigkeiten nutzen, ist allerdings problematisch. Hierfür sind Technologien zum Prototyping neuer Interfaces mit benutzerdefinierter Interaktivität und Kompatibilität mit vielfältigen Materialien erforderlich. Zudem sollten solche Technologien zugänglich sein, um ein breites Publikum zu erreichen. Diese Dissertation erforscht die digitale Fabrikation als Schlüsseltechnologie, um diese Probleme zu adressieren. Sie trägt vier neue Design- und Fabrikationsansätze für das Prototyping interaktiver Objekte mit reichhaltigen Materialien bei. Diese ermöglichen einfaches, zugängliches und vielseitiges Design und Fabrikation von interaktiven Objekten mit individueller Dehnbarkeit, Ein- und Ausgabe auf komplexen Geometrien und vielfältigen Materialien, taktiler Ausgabe auf 3D-Objektgeometrien und der Fähigkeit ihre Form und Materialeigenschaften zu ändern. Insgesamt trägt diese Dissertation zum Fortschritt der Bereiche der digitalen Fabrikation, des Rapid Prototyping und des Ubiquitous Computing in Richtung des größeren Ziels, der Exploration interaktiver Objekte mit reichhaltigen Materialien als eine neue Generation von physischen Interfaces, bei

Multi-objective Optimization of Turning Performance Characteristics using GA Coupled with AHP based Approach

Heat resistive super alloys (HRSAs) which are commonly known as Inconel alloys are extensively used in aeronautical, food processing and automobile industries. The machinability and parametric optimization of Inconel 825 have not been reported much in the literatures. This study attempts to experimentally investigate and optimize the process parameters during machining Inconel 825 for multiple performance characteristics. Spindle speed (N), feed rate (f) and depth of cut (d) are optimized for different responses namely surface roughness (Ra), cutting force (Fz) and metal removal rate (MRR). Feed is found to have the highest influence on Ra and Fz. A mathematical model based on multiple regression analysis is developed for predicting Ra, Fz and MRR. Taguchi analysis is used for optimizing single objective through mean effect plots. For simultaneously optimizing all the responses a weighted combination of objective function is formulated and optimized using genetic algorithm (GA).The optimum parametric combination being 1200 rpm, 0.113 mm/rev and 0.825 mm for N, f and d respectively. In the present work analytical hierarchy processes (AHP) is employed for evaluating weights for each performance measures based on their relative importance. Also, Pareto optimality approach is used for obtaining optimum solutions that produce components with maximum MRR at desired value of Ra is another new contribution of this research. The developed approach can be economically applied for the production of quality components from Inconel 825 by industries

A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed

Reducing risk in pre-production investigations through undergraduate engineering projects.

This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level. The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor. The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits. The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process

A hybrid approach of anfis—artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology and chip reduction coefficient are considered. Further, to get the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling are accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Ultra-high precision machining of contact lens polymers

Contact lens manufacture requires a high level of accuracy and surface integrity in the range of a few nanometres. Amidst numerous optical manufacturing techniques, single-point diamond turning is widely employed in the making of contact lenses due to its capability of producing optical surfaces of complex shapes and nanometric accuracy. For process optimisation, it is ideal to assess the effects of various conditions and also establish their relationships with the surface finish. Presently, there is little information available on the performance of single point diamond turning when machining contact lens polymers. Therefore, the research work undertaken herewith is aimed at testing known facts in contact lens diamond turning and investigating the performance of ultra-high precision manufacturing of contact lens polymers. Experimental tests were conducted on Roflufocon E, which is a commercially available contact lens polymer and on Precitech Nanoform Ultra-grind 250 precision machining. Tests were performed at varying cutting feeds, speed and depth of cut. Initial experimental tests investigated the influence of process factors affecting surface finish in the UHPM of lenses. The acquired data were statistically analysed using Response Surface Method (RSM) to create a model of the process. Subsequently, a model which uses Runge-Kutta’s fourth order non-linear finite series scheme was developed and adapted to deduce the force occurring at the tool tip. These forces were also statistically analysed and modelled to also predict the effects process factors have on cutting force. Further experimental tests were aimed at establishing the presence of the triboelectric wear phenomena occurring during polymer machining and identifying the most influential process factors. Results indicate that feed rate is a significant factor in the generation of high optical surface quality. In addition, the depth of cut was identified as a significant factor in the generation of low surface roughness in lenses. The influence some of these process factors had was notably linked to triboelectric effects. This tribological effect was generated from the continuous rubbing action of magnetised chips on the cutting tool. This further stresses the presence of high static charging during cutting. Moderately humid cutting conditions presented an adequate means for static charge control and displayed improved surface finishes. In all experimental tests, the feed rate was identified as the most significant factor within the range of cutting parameters employed. Hence, the results validated the fact that feed rate had a high influence in polymer machining. The work also established the relationship on how surface roughness of an optical lens responded to monitoring signals and parameters such as force, feed, speed and depth of cut during machining and it generated models for prediction of surface finishes and appropriate selection of parameters. Furthermore, the study provides a molecular simulation analysis for validating observed conditions occurring at the nanometric scale in polymer machining. This is novel in molecular polymer modelling. The outcome of this research has contributed significantly to the body of knowledge and has provided basic information in the area of precision manufacturing of optical components of high surface integrity such as contact lenses. The application of the research findings presented here cuts across various fields such as medicine, semi-conductors, aerospace, defence, telecom, lasers, instrumentation and life sciences

Characterising the dynamic response of ultrasonic cutting devices

The current work begins by considering a range of common high power ultrasonic components in order to establish a standardised approach to tool design for optimum performance. The vibration behaviour of tuned components resonating longitudinally at ultrasonic frequencies around 35 kHz is modelled via finite element analysis and measured by experimental model analysis. Significant improvements in experimental validation of the models are achieved by the use of a 3D LDV, which allows modal analysis from both in-plane and out-of-plane measurement, which is critical in proposing alternative designs. The vibration characteristics of complex multiple-component systems used in ultrasonic cutting of food products are also investigated. Commonly, the design approach for ultrasonic systems neglects to account for the mutual effects of physically-coupled components in the system vibration. The design of systems also neglects the nonlinear dynamic effects which are inherent in high power systems due to the nonlinearities of piezoelectric transducers. The first issue is tackled by considering the vibration behaviour of the whole system and the influence of individual components and, particularly, offers design improvements via modification of block horns and cutting blade components, which are modelled and validated. The issue of nonlinearity is addresses by identifying the mechanisms of energy leakage into audible frequencies and characterising the common multimodal responses. For this study, design modifications focused on reducing the number of system modes occurring at frequencies below the tuned system frequency. As a consequence of these approaches, insights for the design of multiple-component systems in general are provided

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency