Sensor-based Modeling Research Articles

Sensor-based modeling of problem-solving in virtual reality manufacturing systems

Problem-solving is the process to achieve a goal when the solution path is uncertain. Recently, technological advancements have changed problems’ characteristics and their solutions in engineering fields. Strong problem-solving skills are essential to allow engineers to assess new problems and quickly implement solutions. Engineering problem-solving skills are first educated in schools and usually evaluated through written exams. However, high grades in exams do not represent sufficient problem-solving skills in real-world engineering problems. Decision making with insufficient problem-solving skills in real world may result in costly consequences. Therefore, it is imperative to evaluate and reinforce problem-solving skills of engineering students in real-world problems. With the rapid technological advancements, availability of virtual reality (VR) and eye-tracking facilitates the study of engineering problem-solving. The immersive environment created by VR enables students to better understand and solve real-world engineering problems. On the other hand, eye-tracking allows for studying fundamental cognitive processes during information processing. It is critical to integrate VR simulation with data-driven modeling of eye movements to evaluate and enhance engineering problem-solving skills. In this paper, we integrate sensing technology (i.e., eye-tracking) and virtual reality (VR) to model problem-solving in manufacturing systems. A novel data-driven model that integrates signal detection theory (SDT) with Conflict & Error (C&E) is developed to quantify engineering problem-solving skills. First, we simulate a manufacturing system in a VR game environment. Students are given an assembly problem to produce a car toy that satisfies some particular requirements in the VR manufacturing system while eye-tracking data are collected throughout the assembly process. Second, eye-tracking data are analyzed with a SDT model to quantify problem-solving skills. Third, a joint SDT-C&E model is developed to analyze eye-tracking data and benchmark with results generated from the SDT model. Experimental results show that the joint SDT-C&E model is more effective to quantify problem-solving skills of engineering students than the SDT model.

Sensor-based skeleton modeling and MVEEs approach of the redundant manipulator for reaction motion

With the wide application of redundant manipulators, sharing a working space with humans and dealing with uncertainty seems an inevitable problem, especially in the dynamic and unstructured domain. How to deal with obstacle avoidance is of particular importance that robots and humans/environments are safe interactions to fulfill the complex cooperating tasks. This paper aimed at solving the problem of multiple points avoidance for the reaction motion based on the skeleton algorithm in unstructured and dynamic environments. A method named “sensor-based skeleton modeling and MVEEs approach of the redundant manipulator for the reaction motion” is proposed. The extraction of skeleton information from image is obtained to calculate the distances of the multiple control points and establish the repulsion in this method. Afterward, the force Jacobian related to the priority weighting factors is calculated and then a reaction force with damping term is established, which is corresponding nominal torque commands. For the redundant manipulator, the joint angles are obtained through torque iteration instead of inverse kinematics to reduce calculation cost. Finally, the method was tested by a 7-DOF manipulator in the ROS framework. The obtained results indicate that the method in this method can realize dynamic obstacle avoidance and time cost reduction.

Combining sensor-based measurement and modeling of PM2.5 and black carbon in assessing exposure to indoor aerosols.

Accurate, cost-effective methods are needed for rapid assessment of traffic-related air pollution (TRAP). Typically, real-time data of particulate matter (PM) from portable sensors have been adjusted using data from reference methods such as gravimetric measurement to improve accuracy. The objective of this study was to create a correction factor or linear regression model for the real-time measurements of the RTI's Micro Personal Exposure Monitor (MicroPEM™) and AethLab's microAeth® black carbon (AE51) sensor to generate accurate real-time data for PM2.5 (PM2.5RT) and black carbon (BCRT) in Cincinnati metropolitan homes. The two sensors and an SKC PM2.5 Personal Modular impactor were collocated in 44 indoor sampling events for 2 days in residences near major roadways. The reference filter-based analyses conducted by a laboratory included particle mass (SKC PM2.5 and MicroPEM™ PM2.5) and black carbon (SKC BC); these methods are more accurate than real-time sensors but are also more cumbersome and costly. For PM2.5, the average correction factor, a ratio of gravimetric to real-time, for the MicroPEM™ PM2.5 and SKC PM2.5 utilizing the PM2.5RT and was 0.94 and 0.83, respectively, with a coefficient of variation (CV) of 84% and 52%, respectively; the corresponding linear regression model had a CV of 54% and 25%. For BC, the average correction factor utilizing the BCRT and SKC BC was 0.74 with a CV of 36% with the associated linear regression model producing a CV of 56%. The results from this study will help ensure that the real-time exposure monitors are capable of detecting an estimated PM2.5 after an appropriate statistical model is applied.

Constrained Markov Decision Process Modeling for Sequential Optimization of Additive Manufacturing Build Quality

Additive manufacturing (AM) provides a greater level of flexibility to produce a 3-D part with complex geometries directly from the design. However, the widespread application of AM is currently hampered by technical challenges in process repeatability and quality control. To enhance the in-process information visibility, advanced sensing is increasingly invested for real-time AM process monitoring. The proliferation of in situ sensing data calls for the development of analytical methods for the extraction of features sensitive to layer-wise defects, and the exploitation of pertinent knowledge about defects for in-process quality control of AM builds. As a result, there are increasing interests and rapid development of sensor-based models for the characterization and estimation of layer-wise defects in the past few years. However, very little has been done to go from sensor-based modeling of defects to the suggestion of in situ corrective actions for quality control of AM builds. In this paper, we propose a new sequential decision-making framework for in situ control of AM processes through the constrained Markov decision process (CMDP), which jointly considers the conflicting objectives of both total cost (i.e., energy or time) and build quality. Experimental results show that the CMDP formulation provides an effective policy for executing corrective actions to repair and counteract incipient defects in AM before completion of the build.

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

Urbanization is causing a significant increase in the amount, diversity, and complexity of human activities, all of which have a substantial impact on energy consumption. Current approaches to predicting energy demand at different spatiotemporal levels are functions of the characteristics of either individual buildings or cities and their occupancy levels, or are data-driven, typically taking the form of sensor-based modeling. Nevertheless, accounting for both the spatial and temporal effects of heterogeneous human behavior patterns on buildings’ energy use at the city level remains a challenge. In this paper, we examine the temporal manifestation of the fluctuations of energy use in urban buildings driven by spatial mobility patterns of the population. We then present an urban-level spatiotemporal approach for predicting buildings’ energy demand. Using a full year of individual positional records from an online social networking platform (Twitter), we introduce a multivariate autoregressive model in reduced principle component analysis space to create monthly predictions of residential building electricity demand generated across 801 spatial divisions that account for 68% of the electricity used by buildings in the City of Chicago, through a spatial autoregressive model. This model represents an important step forward, incorporating the spatiotemporal energy use fluctuations of urban population activities to create more reliable predictions of demand in future cities.

Predicting the Parts Weight in Plastic Injection Molding Using Least Squares Support Vector Regression

To achieve the desired quality in plastic injection molding, advanced monitoring techniques are often recommended in the workshop. Unfortunately, the signal in plastic injection modeling process such as nozzle pressure that is relevant to part quality is not easy to obtain because of the cost of sensors. The sensor-based modeling idea is therefore adopted. In this paper, a new method for predicting the parts weight in plastic injection molding using least squares support vector regression (LS-SVR) is proposed, which is composed of two steps. The first step is to estimate the nozzle pressure with the hydraulic system pressure using an LS-SVR model. The second step is to predict product weight using the estimated nozzle pressure, which is done using another LS-SVR model. The experimental results show that the new method is very effective.

Sensor-based modeling of slurry chemistry effects on the material removal rate (MRR) in copper-CMP process

Material removal rate (MRR) and surface quality in copper-chemical mechanical planarization (Cu-CMP) process are highly sensitive to slurry chemistry parameters, namely, pH, and concentrations of complexing, corrosion inhibiting, and oxidizing agents. Capturing the effects of these slurry parameters on MRR and surface quality in real time through the use of sensor signals is key to ensuring an efficient Cu-CMP process. In this paper, vibration sensor signals collected from the Cu-CMP experiments are used to capture the variations in various slurry parameters as well as their influence on the MRR. Two sensors, namely, a wired accelerometer (Kistler Model 8728A500, sampling at 5 kHz) and a single channel wireless accelerometer (Tmote Sky sensor node, sampling at 500 Hz) are mounted at two distinct locations on a LapMaster-12 bench-top polishing machine. Various statistical features related to time and frequency domain characteristics of the sensor signals are extracted. It was found that principal component regression models relating these features to MRR are significantly more accurate than the conventional statistical regression models that use process parameters (slurry chemistry settings) only to estimate MRR.

Sensor-based deflection modeling and compensation control of flexible robotic manipulator

A sensor-based technique is proposed for the modeling and control of the manipulator positioning errors caused by structural deflections of the flexible links. A laser–optical sensor system is specially designed for measuring the deflections of each flexible link, and the robot positioning inaccuracies are thus deduced through the link deflections measured and, finally, compensated for in real time by adjustment of the joint variables. The principle of measurement is described, the sensor-based modeling and compensation algorithms of the robot positioning errors are presented, and the simulation results are given in the paper.