Cloud-based manufacturing-as-a-service environment for customized products

This paper describes the paradigm of cloud-based services which are used to envisage a new generation of configurable manufacturing systems. Unlike previous approaches to mass customization (that simply reprogram individual machines to produce specific shapes) the system reported here is intended to enable the customized production of technologically complex products by dynamically configuring a manufacturing supply chain. In order to realize such a system, the resources (i.e. production capabilities) have to be designed to support collaboration throughout the whole production network, including their adaption to customer-specific production. The flexible service composition as well as the appropriate IT services required for its realization show many analogies with common cloud computing approaches. For this reason, this paper describes the motivation and challenges that are related to cloud-based manufacturing and illustrates emerging technologies supporting this vision byestablishing an appropriate Manufacturing-as-a-Service environment based on manufacturing service descriptions

A Method to Represent Heterogeneous Materials for Rapid Prototyping: The Matryoshka Approach

Purpose—The purpose of this paper is to present a new method for representing heterogeneous materials using nested STL shells, based, in particular, on the density distributions of human bones. Design/methodology/approach—Nested STL shells, called Matryoshka models, are described, based on their namesake Russian nesting dolls. In this approach, polygonal models, such as STL shells, are “stacked” inside one another to represent different material regions. The Matryoshka model addresses the challenge of representing different densities and different types of bone when reverse engineering from medical images. The Matryoshka model is generated via an iterative process of thresholding the Hounsfield Unit (HU) data using computed tomography (CT), thereby delineating regions of progressively increasing bone density. These nested shells can represent regions starting with the medullary (bone marrow) canal, up through and including the outer surface of the bone. Findings—The Matryoshka approach introduced can be used to generate accurate models of heterogeneous materials in an automated fashion, avoiding the challenge of hand-creating an assembly model for input to multi-material additive or subtractive manufacturing. Originality/Value—This paper presents a new method for describing heterogeneous materials: in this case, the density distribution in a human bone. The authors show how the Matryoshka model can be used to plan harvesting locations for creating custom rapid allograft bone implants from donor bone. An implementation of a proposed harvesting method is demonstrated, followed by a case study using subtractive rapid prototyping to harvest a bone implant from a human tibia surrogate

Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning

Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts

Process planning for the subtractive rapid manufacturing of heterogeneous materials: Applications for automated bone implant manufacturing

This research presents a subtractive rapid manufacturing process for heterogeneous materials, in particular for custom shaped bone implants. Natural bone implants are widely used in the treatment of severe fractures or in tumor removal. In order for the human body to accept the bone implant material and heal properly, it is essential that the bone implant should be both mechanically and biologically compatible. Currently, the challenge of having correctly shaped natural bone implants created from an appropriate material is met through hand-shaping done by a surgeon. CNC-RP is a rapid machining method and software that can realize a fully automated Subtractive Rapid Prototyping (RP) process, using a 3-axis milling machine with a 4th axis indexer for multiple setup orientations. It is capable of creating accurate bone implants from different clinically relevant material including natural bone. However, there are major challenges that need to be overcome in order to implement automated shape machining of natural bones. They are summarized as follows: (1) Unlike homogeneous source materials for which a part can be machined from any arbitrary location within the original stock, for the case of donor bones, the site and orientation of implant harvest need to consider the nature of the heterogeneous internal bony architecture. (2) For the engineered materials, the source machining stock is in the convenient form of geometrically regular shapes such as cylinders or rectangular blocks and the entities of sacrificial supports can connect the part to the remaining stock material. However, irregularly-shaped bones and the heterogeneity of bone make the design of a fixture system for machining much more complicated. In this dissertation, two major areas of research are presented to overcome these challenges and enable automated process planning for a new rapid manufacturing technique for natural bone implants. Firstly, a new method for representing heterogeneous materials using nested STL shells is proposed. The nested shells model is called the Matryoshka mode, based in particular on the density distribution of human bone. The Matryoshka model is generated via an iterative process of thresholding the Hounsfield Unit (HU) data from a computed tomography (CT) scan, thereby delineating regions of progressively increasing bone density. Then a harvesting algorithm is developed to determine a suitable location to generate the bone implant from within the donor bone is presented. In this harvesting algorithm, a density score and similarity score are calculated to evaluate the overall effectiveness of that harvest site. In the second research area, an automated fixturing system is proposed for securing the bone implant during the machining process. The proposed method uses a variant of sacrificial supports (stainless surgical screws) to drill into appropriate locations and orientations through the free-form shaped donor bone, terminating at proper locations inside the solid part model of the implant. This automated fixturing system has been applied to machine several bone implants from surrogate bones to 3D printed Matryoshka models. Finally, the algorithms that are developed for setup planning are implemented in a CAD/CAM software add-on called CNC-RPbio . The results of this research could lead to a clinically relevant rapid machining process for custom shaped bone implants, which could create unique implants at the touch of a button. The implication of such high accuracy implants is that patients could benefit from more accurate reconstructions of trauma sites, with better fixation stability; leading to potentially shorter surgeries, less revisions, shorter recovery times and less likelihood of post-traumatic osteoarthritis, to name a few

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