Elsevier

Measurement

Volume 169, February 2021, 108529
Measurement

Electrical analogy approach to estimate material category from transient thermal response

https://doi.org/10.1016/j.measurement.2020.108529Get rights and content

Highlights

  • New approach of material category identification based on thermal properties is proposed.

  • Estimation of thermal properties from time delay using electrical analogy modelling.

  • Method uses transient temperature measurement.

  • Experiments and simulation demonstrate good efficacy of the proposed approach.

Abstract

Material identification based on thermal properties is a commonly used approach of classifying materials into different categories. In this paper, an analytical approach for estimation of two thermal properties under the transient condition and arbitrary ambient temperature is presented for material category identification. The thermal properties are estimated by exploiting the analogy between the one-dimensional thermal conduction in material with the voltage distribution in resistance-capacitance (RC) ladder network. The concept of time delay in the RC ladder network has been used in the thermal domain for deriving simple analytical expressions of thermal diffusivity and thermal conductivity. The proposed approach is simple, has less data related concerns and can identify the material category based on two thermal properties for better certainty. This approach has been verified by simulations and experiments performed on materials of different categories. The approach would be helpful in automated applications related to material identification or handling.

Introduction

Material category identification is an intelligent and automated way for fast classification and handling of materials based on the intrinsic material properties. It has applications in varied fields, including in robotics, haptics, automated waste segregators etc. [1], [2], [3]. Many authors have proposed solutions related to material category identification by different intrinsic properties like thermal, hardness, dielectric, ultrasonic, electrical, magnetic, etc. [4], [5], [6], [7], [8], [9]. Thermal property based material identification is a commonly used approach for material category identification [1], [2], [10], which is convenient to implement due to the ease of temperature sensing and low cost of instruments associated with temperature sensing. Thermal properties can be estimated either by the steady-state methods or transient methods. The steady-state methods determine thermal properties by establishing a temperature difference that does not change with time, whereas the transient methods measure the time-dependent temperature response of material [11]. The main advantage of the transient method over the steady-state method is the quickness of thermal property estimation, which is important in many applications [1], [2], [10].

In literature, many thermal methods have been reported for material identification by estimating one thermal property only [10], [12]. However, identification based on one property is not a very reliable way to distinguish between materials, as many materials have nearly the same thermal property, so estimation of two thermal properties can give certainty. Russell and Paoloni [13] have also used two thermal properties (thermal conductivity and thermal diffusivity) for material identification. In this work, an analytical solution of heat conduction in a composite media for identification of materials was proposed under steady-state method. Also, the approach was derived for a fixed ambient condition, which is difficult to implement in practice. To overcome these limitations, Cheol et al. [14] proposed an approach which could estimate thermal conductivity at arbitrary ambient temperature under transient condition using cubic spline interpolation. This approach was non-analytical, data-intensive and used only one thermal property for material identification. Moreover, the experimental data used for interpolation was prone to noise. In order to address the influence of noise on data, Ryoo et al. [10] proposed a material identification method based on curve fitting and fuzzy neural network. The method was also based only on one thermal property (thermal conductivity). Also, the neural network technique is time-consuming and requires much training to yield acceptable results. Campos et al. [12] proposed another analytical heat conduction model for thermal property estimation, which was devoid of extensive data handling and data training. However, the method assumed that both sensor and material under investigation are semi-infinite objects, which has limited practicability. It is evident from the literature that a method based on more than one thermal property, using a simple analytical approach which is applicable under transient condition and variable ambient temperature and also capable of coping with excessive data related problems could be advantageous for material category identification.

In this paper, an analytical method of estimating two thermal properties (thermal conductivity and thermal diffusivity) under the transient condition and arbitrary ambient temperature for material category identification is proposed, which is simple and could give better identification certainty. Moreover, this method does not require extensive data handling, fitting, data training and learning. The proposed analytical method is derived while considering the analogy between thermal and electrical quantities. This approach has also been used for the analysis of different heat conduction related applications [15], [16], [17], [18]. The paper is organized as follows. The subsequent section covers the methodology for estimation of two thermal properties using the electro-thermal analogy for material identification. Followed by details of the simulation approach and experimental setup, and lastly, the simulation and experimental results based on the proposed approach have been presented with discussion.

Section snippets

Methodology

Many reported literature used only one thermal property for estimation of material category identification. However, the use of two thermal properties can give better identification. In this work, two thermal properties: thermal conductivity and thermal diffusivity have been used for this purpose. Estimating these properties from the thermal response is mainly an inverse heat conduction problem. In this work, the analogy between electrical and thermal domain has been used for obtaining these

Simulation and experiment

Simulation have been performed to estimate the thermal response of 1-D heat conduction in material, using equivalent electrical model (as shown in Fig. 3(b)). This has been performed using commonly available circuit simulation software PSpice [32]. PSpice aids in modelling 1-D heat conduction using finite element approach. Initially, the number of elements in the RC ladder was obtained by considering the thermal properties of the material and then the number of elements were iteratively refined

Result and discussion

In this section, the results of simulations and experiments performed on different material using the proposed approach is presented. The thermal properties (thermal diffusivity and thermal conductivity) are estimated from the thermal delay (τTD) values i.e. the time taken by a material to reach 63% of the applied temperature, obtained from the measured thermal response of the material under investigation.

Conclusion

In this paper, an approach for identifying the material category using thermal properties estimated by exploiting the analogy between the thermal and electrical domain is proposed. The signal delay estimation approach in the RC network is used in thermal domain for obtaining simple analytical expressions of thermal properties under transient condition and variable ambient temperature. This approach can identify material category based on two thermal properties which would result in better

CRediT authorship contribution statement

Priyanka Jena: Conceptualization, Methodology, Investigation, Validation, Formal analysis, Resources, Writing - original draft, Visualization. Rajesh Gupta: Supervision, Conceptualization, Methodology, Writing - review & editing.

Declaration of Competing Interest

The authors declare no conflict of interest.

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