Image-Guided Robotic Dental Implantation With Natural-Root-Formed Implants

Dental implantation is now recognized as the standard of the care for tooth replacement. Although many studies show high short term survival rates greater than 95%, long term studies (\u3e 5 years) have shown success rates as low as 41.9%. Reasons affecting the long term success rates might include surgical factors such as limited accuracy of implant placement, lack of spacing controls, and overheating during the placement. In this dissertation, a comprehensive solution for improving the outcome of current dental implantation is presented, which includes computer-aided preoperative planning for better visualization of patient-specific information and automated robotic site-preparation for superior placement and orientation accuracy. Surgical planning is generated using patient-specific three-dimensional (3D) models which are reconstructed from Cone-beam CT images. An innovative image-guided robotic site-preparation system for implants insertion is designed and implemented. The preoperative plan of the implant insertion is transferred into intra-operative operations of the robot using a two-step registration procedure with the help of a Coordinate Measurement Machine (CMM). The natural-root implants mimic the root structure of natural teeth and were proved by Finite Element Method (FEM) to provide superior stress distribution than current cylinder-shape implants. However, due to their complicated geometry, manual site-preparation for these implants cannot be accomplished. Our innovative image-guided robotic implantation system provides the possibility of using this advanced type of implant. Phantom experiments with patient-specific jaw models were performed to evaluate the accuracy of positioning and orientation. Fiducial Registration Error (FRE) values less than 0.20 mm and final Target Registration Error (TRE) values after the two-step registration of 0.36±0.13 mm (N=5) were achieved. Orientation error was 1.99±1.27° (N=14). Robotic milling of the natural-root implant shape with single- and double-root was also tested, and the results proved that their complicated volumes can be removed as designed by the robot. The milling time for single- and double-root shape was 177 s and 1522 s, respectively

A comparison of the Fitting Surface Trueness and Precision of Traditionally Constructed Complete Dentures and 3D Printed Dentures

Introduction: Complete denture construction was revolutionised in the 1930’s when (Polymethyl Methacrylate) PMMA was developed. But it does have its drawbacks. This research compared the fitting surface trueness and precision of complete dentures against the fitting surface trueness and precision of new 3D printed dentures using additive manufacturing techniques. Method: 10 sets of both traditional and 3-D printed dentures were constructed and assessed. Virtual digital dentures were created using scans of the master casts and try-ins from the traditional workflow. The teeth on the virtual dentures were digitally removed to produce printable denture blanks with tooth sockets. The blanks were printed. Commercially available teeth were re-inserted into the blanks. Post-print curing was completed in a light box. Scans of traditional and printed dentures were taken at each stage of production and measured for trueness and precision against the model scans. Results: Paired t-tests (p≤0.05) were conducted on traditional dentures and compared against printed baseplates without and with teeth. Means and signed standard deviations distances were calculated. Mean deviations were 0.055 +/- 0.008mm for upper traditional dentures and 0.061 +/- 0.075 mm for lowers. Printed mean deviations for upper and lower dentures measured 0.109 +/- 0.007 mm and 0.076 +/- 0.013mm. P values for upper trueness was calculated at (p= 0.000) and precision p value measured (p= 0.000). For lowers trueness p value was measured at (p= 0.006) and precision p value measured (p=0.006). Conclusion: Traditionally constructed dentures were significantly more accurate than printed dentures. The results for printing were promising. When printed, lowers were more accurate than traditionally constructed dentures until teeth were added. Light curing and storage of baseplates prior to adding teeth may be a factor in determining trueness and precision. Further work investigating storage of light cured dentures is needed as this may affect denture trueness and precision

Optimisation of surface coverage paths used by a non-contact robot painting system

This thesis proposes an efficient path planning technique for a non-contact optical “painting” system that produces surface images by moving a robot mounted laser across objects covered in photographic emulsion. In comparison to traditional 3D planning approaches (e.g. laminar slicing) the proposed algorithm dramatically reduces the overall path length by optimizing (i.e. minimizing) the amounts of movement between robot configurations required to position and orientate the laser. To do this the pixels of the image (i.e. points on the surface of the object) are sequenced using configuration space rather than Cartesian space. This technique extracts data from a CAD model and then calculates the configuration that the five degrees of freedom system needs to assume to expose individual pixels on the surface. The system then uses a closest point analysis on all the major joints to sequence the points and create an efficient path plan for the component. The implementation and testing of the algorithm demonstrates that sequencing points using a configuration based method tends to produce significantly shorter paths than other approaches to the sequencing problem. The path planner was tested with components ranging from simple to complex and the paths generated demonstrated both the versatility and feasibility of the approach

Proceedings of the 4th International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME2018)

The Mechatronics Department (Accredited by National Board of Accreditation, New Delhi, India) of the G H Patel College of Engineering and Technology, Gujarat, India arranged the 4th International Conference on Innovations in Automation and Mechatronics Engineering 2018, (ICIAME 2018) on 2-3 February 2018. The papers presented during the conference were based on Automation, Optimization, Computer Aided Design and Manufacturing, Nanotechnology, Solar Energy etc and are featured in this book

Machining of biocompatible materials: Recent advances

Machining of biocompatible materials is facing the fundamental challenges due to the specific material properties as well as the application requirements. Firstly, this paper presents a review of various materials which the medical industry needs to machine, then comments on the advances in the understanding of their specific cutting mechanisms. Finally it reviews the machining processes that the industry employs for different applications. This highlights the specific functional requirements that need to be considered when machining biocompatible materials and the associated machines and tooling. An analysis of the scientific and engineering challenges and opportunities related to this topic are presented

An advanced prototyping process for highly accurate models in biomedical applications

An integrated prototyping process for the derivation of complex medical models is introduced. The use of medical models can support today’s medicine by improving diagnosis and surgical planning, teaching and patient information. To withstand the challenges of time and accuracy, a process for generating accurate virtual and physical medical models is needed. The introduced process offers the possibility to derive virtual and physical models for biomedical engineering applications. Reviewing the current situation of medical virtual prototyping and rapid prototyping applications, limitations were found related to the influential variables of data acquisition, data processing, virtual reality use, and rapid prototyping manufacturing. An integrated prototyping concept (MPP) is introduced for embedding virtual prototyping and rapid prototyping in biomedical applications. Data processing and 3D modeling of complex anatomical structures from computerized image data were investigated and discussed in detail. Finally, parameter analyses were evaluated to derive optimal parameters needed for preparing 3D models for virtual prototyping and rapid prototyping processing in medicine. Summarizing from the accuracy analysis, the present investigation is the first to examine tomographic scanning as decisive factor for inaccuracy of medical prototyping models. The human nose is an example of a complex anatomical geometry, which has been an object of scientific research interest for several years. One of the applications introduced here uses the developed MPP concept as basis for a procedure that generates animated medical models in a virtual reality environment. Although, attempts are being made to reconstruct the human nose as an experimental rapid prototyping model, a process for accurate reconstruction as a transparent rapid prototyping model is still missing. The MPP concept allows fabricating individual models of the human nose with a high level of accuracy and transparency. Finally, temporal analysis revealed major time improvements in modeling complex anatomical models compared to approaches without optimized process sequences and approved parameters. The prototyping of the human hip was the second example used. The results of this particular example emphasized the strengths of the medial prototyping process in preparing hip models for presurgery planning. Here, accuracy was enhanced considerably. Rapid prototyping hip models can provide assistance as a surgical planning tool in complex cases, especially in improving surgical results and implant stability. Thus, the accuracy and time of model generation is improved, thereby establishing a defined process for medical model generation. Considering the novel findings of broad improvements in accuracy and time, a new field of research is emerging, serving both virtual surgery applications and physical implant generation. The MPP developed in this work can be viewed as an initial approach for launching international standards of prototyping technologies in medicine

Computer Numerical Controlled (CNC) machining for Rapid Manufacturing Processes

The trends of rapid manufacturing (RM) have influenced numerous developments of technologies mainly in additive processes. However, the material compatibility and accuracy problems of additive techniques have limited the ability to manufacture end-user products. More established manufacturing methods such as Computer Numerical Controlled (CNC) machining can be adapted for RM under some circumstances. The use of a 3-axis CNC milling machine with an indexing device increases tool accessibility and overcomes most of the process constraints. However, more work is required to enhance the application of CNC for RM, and this thesis focuses on the improvement of roughing and finishing operations and the integration of cutting tools in CNC machining to make it viable for RM applications. The purpose of this research is to further adapt CNC machining to rapid manufacturing, and it is believed that implementing the suggested approaches will speed up production, enhance part quality and make the process more suitable for RM. A feasible approach to improving roughing operations is investigated through the adoption of different cutting orientations. Simulation analyses are performed to manipulate the values of the orientations and to generate estimated cutting times. An orientations set with minimum machining time is selected to execute roughing processes. Further development is carried out to integrate different tool geometries; flat and ball nose end mill in the finishing processes. A surface classification method is formulated to assist the integration and to define the cutting regions. To realise a rapid machining system, the advancement of Computer Aided Manufacturing (CAM) is exploited. This allows CNC process planning to be handled through customised programming codes. The findings from simulation studies are supported by the machining experiment results. First, roughing through four independent orientations minimized the cutting time and prevents any susceptibility to tool failure. Secondly, the integration of end mill tools improves surface quality of the machined parts. Lastly, the process planning programs manage to control the simulation analyses and construct machining operations effectively

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems