Manufacturing enterprises today are forced to face radical challenges in the disruptive concepts of Smart Manufacturing (SM) and Industry 4.0 to stay competitive. Most Multinational Enterprise (MNEs) have initiated their journey towards adopting SM. As a mainspring of many manufacturing economies, Small and Medium-Enterprise (SMEs) are still struggling to understand the complexity offered in SM, and many of them are not ready to embrace the concept of SM. To overcome this, SMEs first need to assess their readiness and maturity before embarking on an SM journey. The existing available readiness assessment model seems to be suitable for MNEs, and there is still a lack of tailored models that suit SMEs. This paper sought to pinpoint the conceptual framework from the review of the existing readiness-maturity assessment and identify the gap of existing model as well as proposed a tailored model framework that are suitable for SMEs. Ultimately, this model will be used to pursue a comprehensive scholarly study across Malaysia. The proposed model is enhanced with 4M attributes as the dimension and embedded with the characteristic of Industry 4.0 build component to help the SME’s overcome the possible uncertainties in adopting SM concept.

Underwater cables or pipelines are commonly utilized elements in ocean research, marine engineering, power transmission, and communication-based activities. Their performance necessitates regularly conducted inspection for maintenance purposes. A vision system is commonly used by autonomous underwater vehicles (AUVs) to track and search for underwater cable. Its traditional methods are characteristically applicable in AUVs, wherein they are equipped with handcrafted features and shallow trainable architectures. However, such methods are subpar or even incapable of tracking underwater cable in fast-changing and complex underwater conditions. In contrast to this, the deep learning method is linked with the capacity to learn semantic, high-level, and deeper features, thus rendering it recommended for performing underwater cable tracking. In this study, several deep Convolutional Neural Network (CNN) models were proposed to classify underwater cable images obtained from a set of underwater images, whereby transfer learning and data augmentation were applied to enhance the classification accuracy. Following a comparison and discussion regarding the performance of these models, MobileNetV2 outperformed among other models and yielded lower computational time and the highest accuracy for classifying underwater cable images at 93.5%. Hence, the main contribution of this study is geared toward developing a deep learning method for underwater cable image classification.

For some highly nonlinear problems, the general second-order response surface method (RSM) cannot satisfy the accuracy requirement. To improve accuracy, the highest order number has to be determined in advance. Thus, a progressive trigonometric mixed response surface method (PTMRSM) was proposed to enhance the approximation accuracy and define the highest order number, rather than determining it in advance. After that, a double-loop interval optimization process could be constructed using this PTMRSM to save time while maintaining accuracy when compared to other experimental or computational methods. Unfortunately, the traditional double-loop interval optimization method had issues with the probability of reliable constraints. Then, for the construction of this double-loop interval optimization process, the modified reliable constraints were introduced. A more reliable and effective double-loop interval optimization was introduced for addressing practical engineering problems using the effective approximate method of the PTMRSM and the amended reliable constraints. Two numerical test functions and a composite submersible hull were performed to verify the accuracy and effectiveness of the PTMRSM and the double-loop interval optimization.