Additive Manufacturing Technology for Spare Parts Application: A Systematic Review on Supply Chain Management

Additive manufacturing (AM) is gaining interest among researchers and practitioners in the field of manufacturing. One major potential area of AM application is the manufacturing of spare parts, which affects the availability of the operation and supply chain. The data show that the application and adoption of AM has contributed to a reduction in lead times and inventory, which also contributes to a reduction in holding costs. This paper provides a review of recent work on the application of AM technology specifically for spare parts. The review shows that there are supply chain opportunities and challenges to the adoption of AM in spare parts within various application sectors. Our research reviews both the quantitative and qualitative models used for analysis to meet the emerging needs of the industry. The review also shows that the development of technology and its application is still emerging; therefore, there will be further opportunities to develop better spare parts supply chains to support AM applications. This paper concludes with future research directions. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Acknowledgments: This study was made possible by the Qatar University grant# M?QJRC?2020?6. The APC was made possible through student grant #QUST?1?CENG?2022?302. The findings of this study are solely the responsibility of the authors.Scopus2-s2.0-8512929378

SUPPLY CHAIN MODELING OF ADDITIVELY MANUFACTURED VERSUS CNC-PRODUCED SPARE PARTS

This research proposes a generic BLOC-ICE-based framework that considers multiple aspects of the adoption of additive manufacturing (AM) in the spare part supply chain. It proposes also a multi-period multiple parts mixed-integer linear programming optimization model for the trade-off analysis spare parts supply through computer numerical control (CNC) manufacturing and AM. The multiple spare parts have different characteristics including volume, shape size, and geometry complexity. The model focuses on minimizing lead times and thus reducing downtime costs. Scenario analyses are developed for some parameters to test the robustness of the model. The analysis shows that the mix between AM-based spare parts and CNC-based spare parts is sensitive to changes in demand. For the given data, the findings demonstrate that AM is cost-effective with spare parts having high geometry complexity while CNC-based manufacturing is economically feasible for spare parts with low geometry complexity and large sizes. The proposed model can support decision-makers in selecting the optimal manufacturing method for multiple spare parts having different characteristics and attributes

An Intelligent Hybrid Experimental-Based Deep Learning Algorithm for Tomato-Sorting Controllers

Conventionally, the methods used for the sorting of tomatoes are manual. These methods are costly, non-productive, and their reliability is uncertain. With advancing technology, deep-learning, and artificial intelligence techniques are being utilized to develop fully automated system controllers. The primary reason behind using these techniques is their competitive performance in solving high nonlinear classification problems. Therefore, this paper investigates the performance and combination scenarios of a number of effective artificial intelligence techniques and strategies. Improving the classification accuracy of automated tomato-sorting controllers shall also be explored. Convolution neural network (CNN), artificial neural network (ANN), self-organizing map (SOM), learning vector quantization (LVQ), and support vector machine (SVM) are developed, optimized, assessed, and compared. In this paper, three main categories are considered, namely, unripe, ripe, and defective (overripe and rotten). Moreover, an experimental setup is designed, manufactured, and tested to verify the computational results obtained from the neural networks as well as to assess the real-time performance of the proposed algorithm. As per the research findings, the utilization of a hybrid CNN-ANN-based algorithm is favored, as it demonstrated a superior performance during validation and experimental testing. The CNN-ANN-based control algorithm yielded a theoretical classification performance of 100% for all classes while the experimental results produced 100% for unripe and ripe classifications and 90% for the ripe and defective (overripe and rotten) classifications. The results of this paper have the potential to improve the classification accuracy of similar fruit and vegetable sorting machines.Scopu

Anthropomorphism and Its Negative Attitudes, Sociability, Animacy, Agency, and Disturbance Requirements for Social Robots: A Pilot Study

A social robot that meets the acceptability requirements of the target end-users presents a significant challenge to robot designers. The design process is often iterative and requires continuous improvements and optimization over time. One key aspect in designing an acceptable social robot is anthropomorphism. Social roboticists have developed assessment tools to evaluate different aspects for the perception of the observer. In this study, we evaluated the attitude of children toward four robots with different degrees of anthropomorphic traits. Questionnaires based on the Negative Attitude toward Robots Scale (NARS) and the Human-Robot Interaction Evaluation Scale (HRIES) were used to acquire the responses of 33 participants. To identify any changes due to interactions, a pre-test questionnaire was given prior to the interaction with a robot. It was then followed by a post-test questionnaire. Statistical tests were used to analyze the effects of gender, test (i.e., pre-test vs post-test), and the four robots, on the observers’ perception. Statistical differences were found between the four robots in the subscales of HRIES, namely, Sociability, Animacy, and Disturbance. The preferences of the children were leaning toward the humanoid robot (i.e., Alpha) with the moderate anthropomorphic traits in the Disturbance subscale. Low to moderate correlations were found between the subscales of NARS and HRIES