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This study evaluates the biaxial Mooney-Rivlin coefficient for silicone sheets made by additive manufacturing (AM) for custom wearable assistive devices and soft robotics. The extrusion-based layer-by-layer technique is applied for AM of moisture cured silicone sheets. A progressive cavity pump controls the precise, continuous flow of high viscosity silicone for low void AM of the cruciform shaped specimen. A biaxial tensile test apparatus with four independent linear actuators was built for testing the cruciform specimens. Digital Image Correlation method was utilized to calculate the distribution of strain. This study show the value of the biaxial Mooney-Rivlin coefficient for the AM silicone sheet is independent of the thickness and in the range of 114.7 kPa to 132.1 kPa for C 10 and –17.9 kPa to –9.04 kPa for C 01 . The geometry of the specimen is shown to affect the biaxial quality of the specimen.

This study develops an optical measurement method to investigate the tissue deformation in needle insertion. Accurate needle insertion with sub-mm accuracy to the target position is desired in various clinical procedures but challenging in practice. One of the main challenges is tissue deformation caused by needle insertion force, introducing the undesired target movements. The double-layered tissue phantom is explored to enable the optical measurement of tissue deformation. Images of tissue deformation were analysed by Kanade–Lucas–Tomasi feature tracking algorithm and digital image correlation technique to measure tissue movement and strain, respectively. This method was further utilized to compare the tissue deformation under different needle insertion speeds. Results show that higher insertion speed increases the tissue deformation due to the increased friction force while reducing the strain perpendicular to the insertion direction near the needle tip. The findings offer insights on tissue deformation and needle-tissue interaction. This study demonstrates the potential of this method to evaluate tissue deformation for various needle designs and insertion motions.

This paper describes how to find phrases related to syntactic category using the chunking method. Phrases syntactic category which is formed associated with prosodic phrases, in order to make this syntax category can be used for prosody generation of Text to Speech System (TTS) systems. Chunking method requires a grammar rule and post tagger. Set of grammar rule is built in a regular expression to classify the words forming phrases using category of the syntax of a sentence based on Part of Speech (POS) information. Hidden Markov Model (HMM) based POS tagger for Indonesian language [10] is used to tagging POS for each word in the sentence. Tests performed on a single set of sentences and compound sentences. Single sentence comprising 31 sentences with the average number of words per sentence was 4.74 words, compound sentences comprising80 sentences with the average number of words per sentence 15.74 words.The measurement results obtained testing precision and recall values for single sentences are 88.75 and 78.89 with the F value is 83.53. As for the complex sentence obtained assessed precision and recall was 80.57 and 84.72 with F valueis 82.60.