Developing Measurement Science to Verify and Validate the Identification of Robot Workcell Degradation

Robot systems have become more prevalent in manufacturing operations as the technology has become more accessible to a wider range of manufacturers, especially small to medium-sized organizations. Although these robot technologies have become more affordable, easier to integrate, and greater in functional capability, these advanced systems increase workcell complexity leading to the presence of more fault and failure modes. Given increasing manufacturing competitiveness, maximizing asset availability and maintaining desired quality and productivity targets have become essential. The National Institute of Standards and Technology (NIST) is developing measurement science (e.g., test methods, performance metrics, reference data sets) to monitor the degradation within a manufacturing workcell that includes a six-degree-of-freedom robot arm. Numerous components of the workcell influence the accuracy of the robot’s tool center position. Identifying the component(s) responsible for process degradation prior to the process performing out of specification will provide manufacturers with advanced intelligence to maintain or maximize their performance targets and asset availability. NIST’s research in robot workcell health promotes workcell component health characterization and develops methods and tools to verify and validate this approach. This paper presents the overall research plan and the efforts to date in developing appropriate test methods, identifying key sources of workcell degradation, and presenting baseline performance data that is leveraged for health assessment.

A Review on Additive Manufacturing of Ceramics

Additive manufacturing (AM), also known as 3D printing, has been used for rapid prototyping due to its ability to produce parts with complex geometries from computer-aided design files. Currently, polymers and metals are the most commonly used materials for AM. However, ceramic materials have unique mechanical properties such as strength, corrosion resistance, and temperature resistance. This paper provides a review of recent AM techniques for ceramics such as extrusion-based AM, the mechanical properties of additively manufactured ceramics, and the applications of ceramics in various industries, including aerospace, automotive, energy, electronics, and medical. A detailed overview of binder-jetting, laser-assisted processes, laminated object manufacturing (LOM), and material extrusion-based 3D printing is presented. Finally, the challenges and opportunities in AM of ceramics are identified.

Development of a Pilot Manufacturing Cyberinfrastructure With an Information Rich Mechanical CAD 3D Model Repository

Data driven advanced manufacturing research is dependent on access to large datasets made available from across the product lifecycle — from the concept design phase all the way down to end use and disposal. Despite such data being generated at a rapid pace, most product design data is archived in inaccessible silos. This is particularly acute in academic research laboratories and with data generated during product design and manufacturing courses. This project seeks to create an infrastructure that allow users (academia and the general public) to easily upload project data and related meta-data. Current manufacturing research must shift from siloed repositories of product manufacturing data to a federated, decentralized, open and inter-operable approach. In this regard, we build ‘FabWave’ a cyber-infrastructure tool designed to capture manufacturing data. In its first pilot implementation, we focused our attention to gathering information rich 3D Mechanical CAD data and related meta-data associated with them, with the intent to make it easier for users to upload and access product design data. We describe workflows that we have initially tested out within the two academic universities and under two different course structures. We have also developed automated workflows to gather license appropriate CAD assemblies from commercial repositories. Our intent is to create the only known largest available CAD model set within academia for enabling research in data-driven computational research in digital design, fabrication and quality control.

Localized Microstructures Fabrication Through Standing Surface Acoustic Wave and User-Defined Waveguides

Polymer-based substrates with patterned microstructure on the surfaces, e.g., cell culturing scaffolds, have been utilized in biomedical applications. This paper develops a novel method to fabricate the localized microstructure on the polymer-based substrate with the assistance of standing surface acoustic wave (SAW) and user-defined acoustic waveguides. The specific designed acoustic waveguides can localize the standing acoustic waves and transmit to the liquid film and excite patterned microstructures on the surface, then using ultraviolet (UV) to solidify the substrate with patterned microstructures. The structural design and fabrication of the SAW device and three different shaped acoustic waveguides are presented. Then, experimental setup and procedures to verify the polymer-substrate with localized microstructures fabrication are performed. By using the different shape of the acoustic waveguides, several types of patterned microstructures with different morphologies are successfully fabricated. Results demonstrated that the proposed fabrication method is an effective way to fabricate polymer-based substrate with localized patterned microstructures, which may have potential in the research on tissue engineering, cell-cell interaction, and other biomedical applications.

Determining Feasible Robot Placements in Robotic Cells for Composite Prepreg Sheet Layup

High-performance composites are widely used in industry because of specific mechanical properties and lightweighting opportunities. Current automation solutions to manufacturing components from prepreg (pre-impregnated precursor material) sheets are limited. Our previous work has demonstrated the technical feasibility of a robotic cell to automate the sheet layup process. Many decisions are required for the cell to function correctly, and the time necessary to make these decisions must be reduced to utilize the cell effectively. Robot placement with respect to the mold is a significant and complex decision problem. Ensuring that robots can collaborate effectively requires addressing multiple constraints related to the robot workspace, singularity, and velocities. Solving this problem requires developing computationally efficient algorithms to find feasible robot placements in the cell. We describe an approach based on successive solution refinement strategy to identify a cell design that satisfies all constraints related to robot placement.

Binder Jetting Additive Manufacturing of Ceramics: Feedstock Powder Preparation by Spray Freeze Granulation

Objective of this study is to prepare the binder jetting feedstock powder by spray freeze drying and study the effects of its parameters on the powder properties. Binder jetting additive manufacturing is a promising technology for fabricating ceramic parts with complex or customized geometries. However, this process is limited by the relatively low density of the fabricated parts even after sintering. The main cause comes from the contradicting requirements of the particle size of the feedstock powder: a large particle size (> 5 μm) is required for a high flowability while a small particle size (< 1 μm) for a high sinterability. For the first time, a novel technology for the feedstock material preparation, called spray freeze drying, is investigated to address this contradiction. Using raw alumina nanopowder (100 nm), a full factorial design at two levels for two factors (spraying pressure and slurry feed rate) was formed to study their effects on the properties (i.e., granule size, flowability, and sinterability) of the obtained granulated powder. Results show that high pressure and small feed rate lead to small granule size. Compared with the raw powder, the flowability of the granulated powders was significantly increased, and the high sinterability was also maintained. This study proves that spray freeze granulation is a promising technology for the feedstock powder preparation of binder jetting additive manufacturing.

Solar PV Power Generation Forecasting and O&M Management Applications: A Review

The growing resource shortage and environmental concerns have forced mankind to develop and utilize renewable energy sources. The penetration of solar photovoltaic (PV) power in the electricity market has been increasing over the past few decades due to its low construction costs, zero pollution nature, and enormous support from governments. However, the intermittency and randomness of PV power also cause huge grid fluctuations which limit its integration in the system. An accurate forecasting of solar PV power generation and optimization of operation and maintenance (O&M) management are essential for further development of the solar PV farms. A great number of related researches have been done in recent years. A review of PV power generation forecasting techniques together with their brief applications on the optimization of O&M management is presented in this paper. Machine learning methods are thought to be the most suitable at the present stage because of their ease of implementation and their capability in processing non-linear, complex data sets. Typical forecasting accuracy measures are summarized and further applications of PV power forecasting on the O&M management are also presented.

Conceptualization and Design of a Low-Cost MTConnect-Enabled Refractometer for Coolant Health Monitoring

Monitoring of the health of water-based coolant used for machining requires measurement of various parameters of the coolant, including refractive index, temperature, pH, and turbidity. One of the primary parameters that is used to determine the concentration of the coolant is the refractive index, which is typically measured manually by an operator at regular intervals during machine operation. This paper describes the conceptualization and preliminary design of a coolant health monitoring system that will automatically measure the refractive index of the coolant and will digitize the resulting measurement for communication to a factory supervisory control and data acquisition (SCADA) system. To enable rapid integration into a factory’s network architecture, the coolant concentration measurement will be transmitted by the monitoring system using the MTConnect format. Having an MTConnect-enabled sensor will allow the data to be remotely aggregated and compared to other machine data to help give a better understanding of overall machine health. The economical approach to its design allows the coolant health monitor to be realizable for both small manufacturing enterprises (SMEs) and large manufacturers alike. This widespread implementation will further benefit industry’s movement toward Internet-of-Things (IoT)-equipped manufacturing facilities.

Real-Time Prognostic and Dynamic Maintenance Window Scheme for Reconfigurable Manufacturing Systems

Real-time condition acquisition and accurate time-to-failure (TTF) prognostic of machines are both crucial in the condition based maintenance (CBM) scheme for a manufacturing system. Most of previous researches considered the degradation process as a population-specific reliability characteristics and ignored the hidden differences among the degradation process of individual machines. Moreover, existing maintenance scheme are mostly focus on the manufacturing system with fixed structure. These proposed maintenance scheme could not be applied for the reconfigurable manufacturing system, which is quite adjustable to the various product order and customer demands in the current market. In this paper, we develop a systematic predictive maintenance (PM) framework including real-time prognostic and dynamic maintenance window (DMW) scheme for reconfigurable manufacturing systems to fill these gaps. We propose a real-time Bayesian updating prognostic model using sensor-based condition information for computing each individual machine’s TTFs, and a dynamic maintenance window scheme for the maintenance work scheduling of a reconfigurable manufacturing system. This enables the real-time prognosis updating, the rapid decision making for reconfigurable manufacturing systems, and the notable maintenance cost reduction.

Sedimentation Study of Bioink Containing Living Cells

3D bioprinting has more and more applications in tissue engineering, in vitro drug testing, and regenerative medicine. The bioink consisting of the biocompatible polymer (as extracellular matrix) and the living cells is the starting material. Because the typical bioprinting process may take several hours, the suspended cells in the bioink sediment with time, which significantly affects the bioink stability as well as the following bioprinting quality and reliability. The cell sedimentation is determined by the integral effects of drag force and buoyancy and gravity. The gravitational force is related to the cells, and the drag force and buoyant force is related to the polymer concentration. This paper is the first paper to quantify the cell sedimentation process of the bioink within 0.5% and 1% (w/v) polymer concentrations respectively. The cell sedimentation phenomenon has been observed using the bioink within 0.5% and 1% (w/v) polymer concentrations. The cell sedimentation velocity has been estimated to be 1.18 μm/s with the polymer concentrations to be 0.5% (w/v) and 0.88 μm/s for the bioink with the polymer concentrations to be 1% (w/v). It is also found that the cell concentration increases significantly at the bottom of the bioink reservoir, resulting in cell aggregates due to cell-cell interaction.