Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter

This multidisciplinary study presents the application of two well known soft computing methods – flexible neural trees, and evolutionary fuzzy rules – for the prediction of the error parameter between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error

A Hybrid System for Dental Milling Parameters Optimisation

This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a high-precision machining centre with five axes for dental milling purposes

Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules

This multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures – evolutionary fuzzy rules and flexible neural trees – for the prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error

Hybridization of machine learning for advanced manufacturing

Tesis por compendio de publicacioines[ES] En el contexto de la industria, hoy por hoy, los términos “Fabricación Avanzada”, “Industria 4.0” y “Fábrica Inteligente” están convirtiéndose en una realidad. Las empresas industriales buscan ser más competitivas, ya sea en costes, tiempo, consumo de materias primas, energía, etc. Se busca ser eficiente en todos los ámbitos y además ser sostenible. El futuro de muchas compañías depende de su grado de adaptación a los cambios y su capacidad de innovación. Los consumidores son cada vez más exigentes, buscando productos personalizados y específicos con alta calidad, a un bajo coste y no contaminantes. Por todo ello, las empresas industriales implantan innovaciones tecnológicas para conseguirlo. Entre estas innovaciones tecnológicas están la ya mencionada Fabricación Avanzada (Advanced Manufacturing) y el Machine Learning (ML). En estos campos se enmarca el presente trabajo de investigación, en el que se han concebido y aplicado soluciones inteligentes híbridas que combinan diversas técnicas de ML para resolver problemas en el campo de la industria manufacturera. Se han aplicado técnicas inteligentes tales como Redes Neuronales Artificiales (RNA), algoritmos genéticos multiobjetivo, métodos proyeccionistas para la reducción de la dimensionalidad, técnicas de agrupamiento o clustering, etc. También se han utilizado técnicas de Identificación de Sistemas con el propósito de obtener el modelo matemático que representa mejor el sistema real bajo estudio. Se han hibridado diversas técnicas con el propósito de construir soluciones más robustas y fiables. Combinando técnicas de ML específicas se crean sistemas más complejos y con una mayor capacidad de representación/solución. Estos sistemas utilizan datos y el conocimiento sobre estos para resolver problemas. Las soluciones propuestas buscan solucionar problemas complejos del mundo real y de un amplio espectro, manejando aspectos como la incertidumbre, la falta de precisión, la alta dimensionalidad, etc. La presente tesis cubre varios casos de estudio reales, en los que se han aplicado diversas técnicas de ML a distintas problemáticas del campo de la industria manufacturera. Los casos de estudio reales de la industria en los que se ha trabajado, con cuatro conjuntos de datos diferentes, se corresponden con: • Proceso de fresado dental de alta precisión, de la empresa Estudio Previo SL. • Análisis de datos para el mantenimiento predictivo de una empresa del sector de la automoción, como es la multinacional Grupo Antolin. Adicionalmente se ha colaborado con el grupo de investigación GICAP de la Universidad de Burgos y con el centro tecnológico ITCL en los casos de estudio que forman parte de esta tesis y otros relacionados. Las diferentes hibridaciones de técnicas de ML desarrolladas han sido aplicadas y validadas con conjuntos de datos reales y originales, en colaboración con empresas industriales o centros de fresado, permitiendo resolver problemas actuales y complejos. De esta manera, el trabajo realizado no ha tenido sólo un enfoque teórico, sino que se ha aplicado de modo práctico permitiendo que las empresas industriales puedan mejorar sus procesos, ahorrar en costes y tiempo, contaminar menos, etc. Los satisfactorios resultados obtenidos apuntan hacia la utilidad y aportación que las técnicas de ML pueden realizar en el campo de la Fabricación Avanzada

Evaluation of Autonomous Robotic Milling Methodology for Natural Tooth-Shaped Implants Based on SKO Optimization

Robotic surgery is one of the most demanding and challenging applications in the field of automatic control. One of the conventional surgeries, the dental implantation, is the standard methodology to place the artificial tooth root composed of titanium material into the upper or lower jawbone. During the dental implant surgery, mechanical removal of the bone material is the most critical procedure because it may affect the patient\u27s safety including damage to the mandibular canal nerve and/or piercing the maxillary sinus. With this problem, even though short term survival rates are greater than 95%, long term success rate of the surgery is as low as 41.9% in 5 years. Since criteria of bone loss should be less than 0.2 mm per year, a high degree of anatomical accuracy is required. Considering the above issues leads to the employment of more precise surgery using computer assisted medical robots. In this dissertation, a computer-aided open-loop intra-operative robotic system with pre-operative planning is presented to improve the success rate of the dental implantation using different types of milling algorithms that also incorporate natural root-shaped implants. This dissertation also presents the refinement and optimization of three-dimensional (3D) dental implants with the complex root shapes of natural teeth. These root shapes are too complex to be drilled manually like current commercial implants and are designed to be conducive to robotic drilling utilizing milling algorithms. Due to the existence of sharp curvatures and undercuts, anatomically correct models must be refined for 3D robotic milling, and these refined shapes must be shown to be optimized for load bearing. Refinement of the anatomically correct natural tooth-shaped models for robotic milling was accomplished using Computer-Aided-Design (CAD) tools for smoothing the sham curvatures and undercuts. The load bearing optimization algorithm is based on the Soft-Kill Option (SKO) method, and the geometries are represented using non-uniform rational B-spline (NURBS) curves and surfaces. Based on these methods, we present optimized single and double root-shaped dental implants for use with robotic site preparation. Evaluation of phantom experiment has led us to investigate how the position, orientation, and depth of the robotic drilling defined with the dental tool exhibit accuracy and efficiency

Learning from nature: Bio-Inspiration for damage-tolerant high-performance fibre-reinforced composites

Over millions of years Nature has attained highly optimized structural designs with remarkable toughness, strength, damage resistance and damage tolerance - properties that are so far difficult to combine in artificial high-performance fibre-reinforced polymers (HPFRPs). Recent studies, which have successfully replicated the structures and especially the toughening mechanisms found in flora and fauna, are reviewed in this work. At the core of the manufacturing of damage-tolerant bio-inspired composites, an understanding of the design principles and mechanisms is key. Universal and naturally-inherent design features, such as hierarchical- and organic-inorganic-structures as well as helical or fibrous arrangements of building blocks were found to promote numerous toughening mechanisms. Common to these features, the outstanding ability of diffusing damage at a sub-critical state has been identified as a powerful and effective mechanism to achieve high damage tolerance. Novel manufacturing processes suitable for HPFRP (such as tailored high-precision tape placement, micro-moulding, laser-engraving and additive manufacturing) have recently gained immense traction in the research community. This stems from the achievable and required geometrical complexity for HPFRPs and the replication of subtly balanced interaction between the material constituents. Even though trends in the literature clearly show that a bio-inspired material design philosophy is a successful strategy to design more efficient composite structures with enhanced damage tolerance and mechanical performance, Nature continues to offer new challenging opportunities yet to be explored, which could lead to a new era of HPFRP composites

Unveiling the prospects of point-of-care 3D printing of Polyetheretherketone (PEEK) patient-specific implants

Additive manufacturing (AM) or three-dimensional (3D) printing is rapidly gaining acceptance in the healthcare sector. With the availability of low-cost desktop 3D printers and inexpensive materials, in-hospital or point-of-care (POC) manufacturing has gained considerable attention in personalized medicine. Material extrusion-based [Fused Filament Fabrication (FFF)] 3D printing of low-temperature thermoplastic polymer is the most commonly used 3D printing technology in hospitals due to its ease of operability and availability of low-cost machines. However, this technology has been limited to the production of anatomical biomodels, surgical guides, and prosthetic aids and has not yet been adopted into the mainstream production of patient-specific or customized implants. Polyetheretherketone (PEEK), a high-performance thermoplastic polymer, has been used mainly in reconstructive surgeries as a reliable alternative to other alloplastic materials to fabricate customized implants. With advancements in AM systems, prospects for customized 3D printed surgical implants have emerged, increasing attention for POC manufacturing. A customized implant may be manufactured within few hours using 3D printing, allowing hospitals to become manufacturers. However, manufacturing customized implants in a hospital environment is challenging due to the number of actions necessary to design and fabricate the implants. The focus of this thesis relies on material extrusion-based 3D printing of PEEK patient-specific implants (PSIs). The ambitious challenge was to bridge the performance gap between 3D printing of PEEK PSIs for reconstructive surgery and the clinical applicability at the POC by taking advantage of recent developments in AM systems. The main reached milestones of this project include: (i) assessment of the fabrication feasibility of PEEK surgical implants using material extrusion-based 3D printing technology, (ii) incorporation of a digital clinical workflow for POC manufacturing, (iii) assessment of the clinical applicability of the POC manufactured patient-specific PEEK scaphoid prosthesis, (iv) visualization and quantification of the clinical reliability of the POC manufactured patient-specific PEEK cranial implants, and (v) assessment of the clinical performance of the POC manufactured porous patient-specific PEEK orbital implants. During this research work, under the first study, we could demonstrate the prospects of FFF 3D printing technology for POC PEEK implant manufacturing. It was established that FFF 3D printing of PEEK allows the construction of complex anatomical geometries which cannot be manufactured using other technologies. With a clinical digital workflow implementation at the POC, we could further illustrate a smoother integration and faster implant production (within two hours) potential for a complex-shaped, patented PEEK patient-specific scaphoid prosthesis. Our results revealed some key challenges during the FFF printing process, exploring the applicability of POC manufactured FFF 3D printed PEEK customized implants in craniofacial reconstructions. It was demonstrated that optimal heat distribution around the cranial implants and heat management during the printing process are essential parameters that affect crystallinity, and thus the quality of the FFF 3D printed PEEK cranial implants. At this stage of the investigation, it was observed that the root mean square (RMS) values for dimensional accuracy revealed higher deviations in large-sized cranial prostheses with “horizontal lines” characteristics. Further optimization of the 3D printer, a layer-by-layer increment in the airflow temperature was done, which improved the performance of the FFF PEEK printing process for large-sized cranial implants. We then evaluated the potential clinical reliability of the POC manufactured 3D printed PEEK PSIs for cranial reconstruction by quantitative assessment of geometric, morphological, and biomechanical characteristics. It was noticed that the 3D printed customized cranial implants had high dimensional accuracy and repeatability, displaying clinically acceptable morphologic similarity concerning fit and contours continuity. However, the tested cranial implants had variable peak load values with discrete fracture patterns from a biomechanical standpoint. The implants with the highest peak load had a strong bonding with uniform PEEK fusion and interlayer connectivity, while air gaps and infill fusion lines were observed in implants with the lowest strength. The results of this preclinical study were in line with the clinical applicability of cranial implants; however, the biomechanical attribute can be further improved. It was noticed that each patient-specific reconstructive implant required a different set of manufacturing parameters. This was ascertained by manufacturing a porous PEEK patient-specific orbital implant. We evaluated the FFF 3D printed PEEK orbital mesh customized implants with a metric considering the design variants, biomechanical, and morphological parameters. We then studied the performance of the implants as a function of varying thicknesses and porous design constructs through a finite element (FE) based computational model and a decision matrix based statistical approach. The maximum stress values achieved in our results predicted the high durability of the implants. In all the implant profile configurations, the maximum deformation values were under one-tenth of a millimeter (mm) domain. The circular patterned design variant implant revealed the best performance score. The study further demonstrated that compounding multi-design computational analysis with 3D printing can be beneficial for the optimal restoration of the orbital floor. In the framework of the current thesis, the potential clinical application of material extrusion-based 3D printing for PEEK customized implants at the POC was demonstrated. We implemented clinical experience and engineering principles to generate a technical roadmap from preoperative medical imaging datasets to virtual surgical planning, computer-aided design models of various reconstructive implant variants, to the fabrication of PEEK PSIs using FFF 3D printing technology. The integration of 3D printing PEEK implants at the POC entails numerous benefits, including a collaborative team approach, quicker turnaround time of customized implants, support in pre-surgical and intraoperative planning, improved patient outcomes, and decreased overall healthcare cost. We believe that FFF 3D printing of customized PEEK implants could become an integral part of the hospitals and holds potential for various reconstructive surgery applications

Shape-Memory Polymers in Dentistry: Systematic Review and Patent Landscape Report

To perform a systematic review (SR) of existing literature and a patent landscape report (PLR) regarding the potential applications of shape-memory polymers (SMPs) in dentistry

3D printing assisted development of bioinspired structure and device for advanced engineering

Smart materials with bio-inspired structure and stimuli responsive features can sense the external and internal condition changes, such as temperature, light intensity, pH or ion concentration. Those unique functions have been widely utilized in cutting edge engineering applications, such as flexible sensors, soft robotics and tissue engineering. Meanwhile, conventional manufacturing methods such as moulding, and lithography-based microfabrication still represent the mainstream force in scale up manufacturing. Considerable limitations for these technologies, such as on demand rapid prototyping, the high cost and low-volume production, remain to be overcome. In this PhD project, I explored the advanced manufacturing in facilitating the complex structure, with higher controllability, lower prototyping cost and extended applications (flexible sensors, soft robots, biomedical devices, etc.). The key practice is to utilize the high resolution 3D printing technology to create dedicated bio inspired structures based on functional materials. Combined with advanced micro/nano engineering, we have achieved a variety of techniques/prototypes for future applications, such as optical control, micro-fluidic and bio-medical systems, etc