Monitoring, Diagnostics, and Prognostics for Robot Tool Center Accuracy Degradation

Over time, robots degrade because of age and wear, leading to decreased reliability and increasing potential for faults and failures; this negatively impacts robot availability. Economic factors motivate facilities and factories to improve maintenance operations to monitor robot degradation and detect faults and failures, especially to eliminate unexpected shutdowns. Since robot systems are complex, with sub-systems and components, it is challenging to determine these constituent elements’ specific influence on the overall system performance. The development of monitoring, diagnostic, and prognostic technologies (collectively known as Prognostics and Health Management (PHM)), can aid manufacturers in maintaining the performance of robot systems by providing intelligence to enhance maintenance and control strategies. This paper presents the strategy of integrating top level and component level PHM to detect robot performance degradation (including robot tool center accuracy degradation), supported by the development of a four-layer sensing and analysis structure. The top level PHM can quickly detect robot tool center accuracy degradation through advanced sensing and test methods developed at the National Institute of Standards and Technology (NIST). The component level PHM supports deep data analysis for root cause diagnostics and prognostics. A reference data set is collected and analyzed using the integration of top level PHM and component level PHM to understand the influence of temperature, speed, and payload on robot’s accuracy degradation.

Towards Continuous Production of Shaped Honeycombs

Honeycomb sandwich panels are widely used for high performance parts subject to bending loads, but their manufacturing costs remain high. In particular, for parts with non-flat, non-uniform geometry, honeycombs must be machined or thermoformed with great care and expense. The ability to produce shaped honeycombs would allow sandwich panels to replace monolithic parts in a number of high performance, space-constrained applications, while also providing new areas of research for structural optimization, distributed sensing and actuation, and on-site production of infrastructure. Previous work has shown methods of directly producing shaped honeycombs by cutting and folding flat sheets of material. This research extends these methods by demonstrating work towards a continuous process for the cutting and folding steps of this process. An algorithm for producing a manufacturable cut-and-fold pattern from a three-dimensional volume is designed, and a machine for automatically performing the required cutting and parallel folding is proposed and prototyped. The accuracy of the creases placed by this machine is characterized and the impact of creasing order is demonstrated. Finally, a prototype part is produced and future work is sketched towards full process automation.

A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Throughput bottlenecks define and constrain the productivity of a production line. Prediction of future bottlenecks provides a great support for decision-making on the factory floor, which can help to foresee and formulate appropriate actions before production to improve the system throughput in a cost-effective manner. Bottleneck prediction remains a challenging task in literature. The difficulty lies in the complex dynamics of manufacturing systems. There are multiple factors collaboratively affecting bottleneck conditions, such as machine performance, machine degradation, line structure, operator skill level, and product release schedules. These factors impact on one another in a nonlinear manner and exhibit long-term temporal dependencies. State-of-the-art research utilizes various assumptions to simplify the modeling by reducing the input dimensionality. As a result, those models cannot accurately reflect complex dynamics of the bottleneck in a manufacturing system. To tackle this problem, this paper will propose a systematic framework to design a two-layer Long Short-Term Memory (LSTM) network tailored to the dynamic bottleneck prediction problem in multi-job manufacturing systems. This neural network based approach takes advantage of historical high dimensional factory floor data to predict system bottlenecks dynamically considering the future production planning inputs. The model is demonstrated with data from an automotive underbody assembly line. The result shows that the proposed method can achieve higher prediction accuracy compared with current state-of-the-art approaches.

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

Design of a Flexure-Based Active Fixture System for Precision Robotic Deburring

This paper presents on the design, prototyping and testing of a flexure-based active workpiece fixture system for precision robotic deburring. Current industrial robotic manipulators suffer from poor positioning accuracy, which makes precision tasks such as deburring, polishing and grinding challenging. Together, the robotic manipulator and the proposed active work fixture will create a dual-stage positioning system for precision tasks where position/force control is crucial. The main application is robotic deburring, which demands positioning accuracy and high compliance over large cutting forces. This first prototype active fixture system is designed as a planar motion table that is supported by parallel flexures, driven by voice-coil actuators, and uses high-resolution laser displacement pickups facilitate accurate motion generation with great backdrivability for force control. The theory behind the proposed design is shown, and a prototype is then used to validate performance. Overall the prototype flexure stage achieves a total stroke of 1 mm and a bandwidth of 21 Hz.

Study on Improving Accuracy in Robotic Milling of Aluminum Alloy

Nowadays, the application of using industrial robots in manufacture is a diminutive due to its own low rigidity and low stiffness. This leads to high level of vibrations that limits the quality and the precision of the workpiece. So they are usually used for welding, grinding and paint shop. However, the potential of industrial robot applications in machining has be realized. The volume of monolithic components is large and there are many issues in machining process such as geometric tolerance and quality of machined surface. In such cases the traditional CNC machine is replaced by industrial robots, which will reduce the production cost, reduce labor and increase the efficiency. In this paper, the milling experiment of 7050-T7451 aeronautical aluminum alloy was carried out by using industrial robot KR210 R2700. In addition, the experiment was employed to study the influence of milling speed, feed-rate, cutting depth and cutting width on vibrations, surface roughness was also measured to evaluate the machining quality. Besides, the axis of angle was changed which led to the different industrial robot’s postures. The vibration signal of different postures was acquired, which was used to analysis the optimal workspace of industrial robot. The best process parameters were obtained, which will play a guiding significance on the actual production.

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

Integrating Impedance Control and Learning Based Search Scheme for Robotic Assemblies Under Uncertainty

Using fixtures for assembly operations is a common practice in manufacturing processes with high production volume. For automated assembly cells using robotic arms, trajectories are programmed manually and robots follow the same path repeatedly. It is not economically feasible to build fixed fixtures for small volume productions as they require high accuracy and are part specific. Moreover, hand coding robot trajectories is a time consuming task. The uncertainties in part localization and inaccuracy in robot motions make it challenging to automate the task of assembling two parts with tight tolerances. Researchers in past have developed methods for automating the assembly task using contact-based search schemes and impedance control-based trajectory execution. Both of these approaches may lead to undesired collision with critical features on the parts. Our method guarantees safety for parts with delicate features during the assembly process. Our approach enables us to select optimum impedance control parameters and utilizes a learning-based search strategy to complete assembly tasks under uncertainties in bounded time. Our approach was tested on an assembly of two rectangular workpieces using KUKA IIWA 7 manipulator. The method we propose was able to successfully select the optimal control parameters. The learning-based search strategy successfully estimated the uncertainty in pose of parts and converged in few iterations.

Analysis of Production System Real-Time Dynamics and Downtime Transient Impact on System Future Production

Downtime has been considered as the most significant contributor to system inefficiency in manufacturing industry. During a downtime, the stoppage at the downtime machine will lead to impact on system production. In addition, it will also change system buffer levels, and thus alter system future behavior, which may lead to impact on system production in future time. For each downtime, a complete evaluation of its production impact during and after the downtime is important for deciding where to allocate limited resources for improving system production. However, the evaluation of downtime impact on system future production has not been studied. Motivated by this need, we investigate how each downtime would change the buffer levels and impact system production in the future. The concept of downtime settling time is proposed to reflect such impact. Given the downtime list, an analytical method is developed to estimate this impact of each downtime. Case studies are conducted to demonstrate the accuracy and effectiveness of the proposed methods.

A Standards and Technology Roadmap for Scalable Distributed Manufacturing Systems

The increasing decentralization of manufacturing has contributed to the growing interest in scalable distributed manufacturing systems (DMSs). The emerging body of work from smart manufacturing, Industrie 4.0, Industrial Internet of Things (IIoT), and cyber-physical systems can enable the continued development of scalable DMS, particularly through the digital thread. However, significant challenges exist in understanding how to apply the digital thread most appropriately for scalable DMS. This paper describes these major challenges and provides a standards and technology roadmap developed from the digital thread viewpoint and consensus-built industrial standards to realize scalable DMS. The goal of this roadmap is to guide research that enables manufacturers to take advantage of opportunities provided by scalable DMS, including improved agility, flexibility, traceability, dynamic decision making, and utilization of manufacturing resources.