Monitoring Additive Manufacturing Machine Health

Additive manufacturing (AM) allows the production of parts and goods with many benefits over more conventional manufacturing methods. AM permits more geometrically complex designs, custom and low-volume production runs, and the flexibility to produce a wide variety of parts on a single machine with reduced pre-production cost and time requirements. However, it can be difficult to determine the condition, or health, of an AM machine since complex designs can increase the variability of part quality. With fewer parts produced, destructive testing is less desirable and statistical methods of tracking part quality may be less informative. Combined with the relatively more complex nature of AM machines, qualifying AM machines and monitoring their health to perform maintenance or repairs is a challenging task. We first present a case study that demonstrates the difficulty of monitoring the qualification of an AM machine. We then discuss some unique challenges AM presents when calibrating and taking measurements of laser power, and we demonstrate the relative insufficiency of this method in tracking the qualification status of an AM machine and the quality of the parts produced. Next, we present a framework that reverses the directionality of monitoring AM machine health. Rather than monitoring machine subsystems and intermediate metrics reflective of part quality, we instead directly monitor part quality through a combination of witness builds and witness parts that provide observational data to define the health status of a machine. Witness builds provide more accurate data separated from the noisy influence of parts and parameter settings, while witness artifacts provide more timely data but with less accuracy. Finally, machine health is modeled as a partially observed Markov decision process using the witness parts framework to maximize the long-term expected value per build. We show the superiority of this model by comparison to two less complex models, one that uses no use no witness parts and another that uses only witness builds. A case study shows the benefits of implementing the model, and a sensitivity analysis is performed to show relevant insights and considerations

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Accelerating Manufacturing Decisions using Bayesian Optimization: An Optimization and Prediction Perspective

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, man, and machine hours. So, each experiment is valuable and it\u27s critical to determine the optimal experiment location to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This thesis presents a multi-objective Bayesian optimization framework to find out the optimum processing conditions for a manufacturing setup. It uses an acquisition function to collect data points sequentially and iteratively update its understanding of the underlying design space utilizing a Gaussian Process-based surrogate model. In manufacturing processes, the focus is often on obtaining a rough understanding of the design space using minimal experimentation, rather than finding the optimal parameters. This falls under the category of approximating the underlying function rather than design optimization. This approach can provide material scientists or manufacturing engineers with a comprehensive view of the entire design space, increasing the likelihood of making discoveries or making robust decisions. However, a precise and reliable prediction model is necessary for a good approximation. To meet this requirement, this thesis proposes an epsilon-greedy sequential prediction framework that is distinct from the optimization framework. The data acquisition strategy has been refined to balance exploration and exploitation, and a threshold has been established to determine when to switch between the two. The performance of this proposed optimization and prediction framework is evaluated using real-life datasets against the traditional design of experiments. The proposed frameworks have generated effective optimization and prediction results using fewer experiments

Predicting Geometric Errors and Failures in Additive Manufacturing

Additive manufacturing is a process that has facilitated the cost effective production of complicated designs. Objects fabricated via additive manufacturing technologies often suffer from dimensional accuracy issues and other part specific problems such as thin part robustness, overhang geometries that may collapse, support structures that cannot be removed, engraved and embossed details that are indistinguishable. In this work we present an approach to predict the dimensional accuracy per vertex and per part. Furthermore, we provide a framework for estimating the probability that a model is fabricated correctly via an additive manufacturing technology for a specific application. This framework can be applied to several 3D printing technologies and applications. In the context of this paper, a thorough experimental evaluation is presented for binder jetting technology and applications.Comment: This version has been published in the Rapid Prototyping Journal (2023

Manufacturing technologies in mould industry and future challenges

This report is based on the manufacturing technologies actively used in the industry. The mould industry in Portugal is one of the biggest and considered the best in quality of moulds in various types of industries like Automobile, Medical and Aerospace. The State of the art mould manufacturing technologies like conformal cooling in complex parts, multi cavity moulds are taking over the industry standards to a new level of competitiveness in terms of business and quality achievement. The industry in Portugal is very well known for the quality of the moulds and the process fabrication of mould tools. The future is becoming more and more competitive with advancement in lean manufacturing and the enormous advancement in the 3D printers. With more time the advance in these technologies will help the requirement of polymer parts be met with high power and high capability of print quality achievable it is seen to be posing a threat to the mould manufacturers. They are in dire need to update the manufacturing process and to be in touch with the developments of production technologies in the world of polymers to stay in the competitive market for a long time. This report will touch upon the present manufacturing techniques and state of the art technologies that are coming into use in the industry and the challenges this industry will face due to increase in use of 3D printers

Research on hybrid manufacturing using industrial robot

The applications of using industrial robots in hybrid manufacturing overcome many restrictions of the conventional manufacturing methods, such as small part building size, long building period, and limited material choices. However, some problems such as the uneven distribution of motion accuracy within robot working volume, the acceleration impact of robot under heavy external loads, few methods and facilities for increasing the efficiency of hybrid manufacturing process are still challenging. This dissertation aims to improve the applications of using industrial robot in hybrid manufacturing by addressing following three categories research issues. The first research issue proposed a novel concept view on robot accuracy and stiffness problem, for making the maximum usage of current manufacturing capability of robot system. Based on analyzing the robot forward/inverse kinematic, the angle error sensitivity of different joint and the stiffness matrix properties of robot, new evaluation formulations are established to help finding the best position and orientation to perform a specific trajectory within the robot\u27s working volume. The second research issue focus on the engineering improvements of robotic hybrid manufacturing. By adopting stereo vision, laser scanning technology and curved surface compensation algorithm, it enhances the automation level and adaptiveness of hybrid manufacturing process. The third research issue extends the robotic hybrid manufacturing process to the broader application area. A mini extruder with a variable pitch and progressive diameter screw is developed for large scale robotic deposition. The proposed robotic deposition system could increase the building efficiency and quality for large-size parts. Moreover, the research results of this dissertation can benefit a wide range of industries, such as automation manufacturing, robot design and 3D printing --Abstract, page iv

Error mapping of build volume in selective laser melting

“Selective laser melting is one of the commonly used additive manufacturing processes employed for production of functional part. Therefore, quality aspects such as dimensional accuracy have become a point of great interest. Like all of the other additive manufacturing processes selective laser melting process suffers from the issue if having wide range of process parameters making the process control a complex task. Additionally, issues specific to the selective laser melting process such as position dependency of accuracy of the part, makes it difficult to predict the resulting dimensional inconsistencies in the part manufactured by this processes. This research is an effort to address the issue of part accuracy as a function of part positioning within the build volume. In this research part errors are defined as the function of part location using multiple linear regression model fitting technique. The resulting model is used to develop the understanding of the effect of principle directions onto errors in the part. In addition, a case study has been conducted to validate the model application for different combination of part geometries and part location. The model was found to be useful to accurately compensate part dimensions at same as well as different part locations within the build chamber for parts having same geometry as that of calibration part. The approach can prove to be useful for practical applications such as small batch production reducing part errors thereby requiring lesser post processing work”--Abstract, page iii