**1. Introduction**

The need for high-performance heat-dissipating devices is highly needed in today's rapidly changing power device and electronics markets [1, 2]. With worldwide movements on the implementation of Industry 4.0, we will see more radical changes in the way tangible products are manufactured [3]. At the same time, rapid product design cycles are becoming more of a standard rather than a demand. Thus, the need for automated design processes carried out with the use of computer as tools has never been so imperative. Computational design procedures have been more widely accepted during the past decades due to the improvements in computing technologies [4]. Together with this, rapid advancements in the algorithms and automated design procedures have flourished. Topology optimization can be viewed as one of the most promising automated design procedures, which has been an active topic of research for almost three decades.

Topology optimization is an automated, 'best material layout' process, which follows the governing equations of one or more physics taken into consideration under a user-defined set of

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conditions and limitations. Several methods and techniques are already well developed especially for the field of structural engineering. Topology optimization is slowly being used in mainstream design processes of tangible products due to the advancements in computational power of computers, the optimization methods, and techniques used in topology optimization itself.

Computational tools have been developed to aid and answer some of the engineering queries, but the main design of the structure is usually left to experienced and specialized professionals. Commonly applied modern-day topology optimization methods utilize finite element analyses (FEA) where each discretization is treated as a design variable. By choosing and varying the adequate material property related to the investigated case, we would iteratively investigate which element is helpful, thus material is 'allocated', and which ones are not, thus can be left as 'void', from the design space. We can also set areas that must be filled with material or areas where materials should not be placed. There are a number of learning materials for topology optimization, most are from one research group from Denmark. Among their developments are a free mobile app, TopOpt [5] and TopOpt3D [6], which can execute structural topology optimization and output. STL files ready for three-dimensional (3D) printing. The interface, some common definitions for structural topology optimization and an example are presented in **Figure 1**.

The earliest work related to topology optimization can be traced back to the ingenious Australian inventor who formulated Michell's truss theory [7] (named after inventor George Michell). The said theory dealt with the least-volume topology of trusses with a single load condition and a stress constraint. Not only was this imaginatively ingenious, it was also ahead of his time where almost nothing was known about the techniques of structural optimization.

**Figure 1.** TopOpt app [5] developed by DTU reflecting the essential elements of topology optimization.

His works were ignored for almost half a decade where it was rediscovered by Cox [8, 9] and Owen [10] in the 1960s, the same time when computers were acknowledged as automation tools. It was Hemp [11] and his co-workers who had spent most of their professional lives and comprehensively studied Michell structures. Modern-day computer-aided topology optimization can probably be traced back to the works of Bendsoe and Kikuchi [12] on homogenization who had also coined and popularized the term topology optimization. For the following decades, their works had sparked the interest of many researchers and might not have necessarily had any product-related applications. In the next section, we first briefly discuss the main methods commonly used in topology optimization.
