Riveting is a prominent joining method due to the capability of easily assembling an innovated material (e.g., dissimilar materials with light-weight and stronger performance) to an enhanced structure (e.g., body-in-white). It receives attention recently in the transportation industry and the packing manufacturing industry (e.g., white goods). The quality prediction of riveting can increase the efficiency of the riveting process and design. Even though there is much potential to data mining technique for the prediction, the mining approach is rarely used for riveting. At this perspective, we carried out a survey to make an accessible bibliography of riveting prediction for helping researchers. In this research, we searched for the past 25 year's works of literature related to the quality measure and prediction method of riveting with special interests to self-piercing riveting (SPR). To do this, we retrieve research papers indexed at Engineering Village Database. Firstly, we categorize riveting-quality measures and prediction methods. Secondly, we analyze the topic and trend of riveting research from the collected papers. Finally, we conclude with the remaining challenges for the riveting quality prediction as well as in a data mining perspective.

Purpose The purpose of this paper is to propose a procedure to construct the membership functions for a one-unit repairable system, which has both active and standby redundancy. The coverage factor is the same for the operating and standby unit failure. Design/methodology/approach The α-cut approach is used to extract a family of conventional crisp intervals from the fuzzy repairable system for the desired system characteristics. This can be determined with a set of non-linear parametric programing using the membership functions. Findings When system characteristics are governed by the membership functions, more information is provided to use by management. On the other hand, fuzzy theory is applied for the redundant system; therefore, the results are more useful for designers and practitioners. Originality/value Different from other studies, the authors’ model provides more accurate estimation compared to uncertain environments based on fuzzy theory. The research would help managers and manufactures to make a better decision in order to have the optimal maintenance strategy based on the desired mean time to failure and availability of the systems.

BACKGROUND:Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.METHODS:Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied.RESULTS:The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.CONCLUSION:This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.

Predicting reliability of new products at their early life time is one of the important issues in the field of reliability. Lack of data in this period of life time causes prediction to be very hard and inaccurate. This paper proposes a model for predicting non repairable product's reliability early after its production and introduction to the market. It is assumed that time to failure of this product has a Weibull distribution with known shape parameter but the scale parameter is a random variable that could have different distributions like gamma, inverted gamma and truncated normal. Bayesian statistics is used to join prior information on past product failure and sparse few field data on current product's performance to overcome lack of data problem which is a major problem in the early reliability prediction of new products. The Bayesian model provides a more accurate and logical prediction compared to classical methods and indications are favorable regarding the model's practicality in industrial applications. This model has managerial usefulness because of giving more accurate predictions. In all previous studies, there is no comprehensive and precise model for reliability prediction. Different from other studies, we present a definite form for scale parameter of different prior distributions. We use a special form of Weibull distribution which leads us to this definite form. This model provides a suitable estimation value from uncertain environments of parameters because it uses more information for prediction.

Despite the immense technology advancement in the surgeries the criteria of assessing the surgical skills still remains based on subjective standards. With the advent of robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and autonomous skill assessment is introduced. Previous works in this area are mostly based on structured-based method such as Hidden Markov Model (HMM) which need enormous pre-processing. In this study, in contrast with them, we develop a new shaped-based framework for automatically skill assessment and personalized surgical training with minimum parameter tuning. Our work has addressed main aspects of skill evaluation; develop gesture recognition model directly on temporal kinematic signal of robotic-assisted surgery, and build automated personalized RMIS gesture training framework which . We showed that our method, with an average accuracy of 82% for suturing, 70% for needle passing and 85% for knot tying, performs better or equal than the state-of-the-art methods, while simultaneously needs minimum pre-processing, parameter tuning and provides surgeons with online feedback for their performance during training.

Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of {\emph{``at-risk''}} students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods.

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.

Robotic-assisted surgery holds significant promise to improve patient treatment by allowing surgeons to perform many types of complex operations with greater precision and flexibility than before. In order to facilitate automation of robotic surgery and more practical training for surgeons, more detailed comprehension of the surgical procedures is needed. In this regard, a key step is to develop techniques that segment and recognize surgical tasks intelligently. Surgeries involve complex continuous activities that may contain superfluous, repeated actions, and temporal variation. Therefore, any segmentation approach that has the capability to account for all these characteristics is of increased interest. Toward this goal, we develop a new segmentation algorithm, namely soft-boundary unsupervised gesture segmentation (Soft-UGS), to segment the temporal sequence of surgical gestures and model gradual transitions between them using fuzzy membership scores. The proposed framework is evaluated using a real robotic surgery dataset. Our extensive set of experiments and evaluation metrics show that the proposed Soft-UGS method is able to match manual annotations with upto 83% sensitivity, 81% precision, and 73% segmentation score. The results show that the proposed soft boundary approach can provide more insight into the surgical activities and can contribute to the automation of robotic surgeries.

Despite the immense technology advancement in the surgeries the criteria of assessing the surgical skills still remains based on subjective standards. With the advent of robotic-assisted surgery, new opportunities for objective and autonomous skill assessment is introduced. Previous works in this area are mostly based on structured-based method such as Hidden Markov Model (HMM) which need enormous pre-processing. In this study, in contrast with them, we develop a new shaped-based framework for automatically skill assessment and personalized surgical training with minimum parameter tuning. Our work has addressed main aspects of skill evaluation; develop gesture recognition model directly on temporal kinematic signal of robotic-assisted surgery, and build automated personalized RMIS gesture training framework which . We showed that our method, with an average accuracy of 82% for suturing, 70% for needle passing and 85% for knot tying, performs better or equal than the state-of-the-art methods, while simultaneously needs minimum pre-processing, parameter tuning and provides surgeons with online feedback for their performance during training.

New product reliability prediction has been center of research studies for many years. Reliability prediction focuses on developing a proper reliability model based on available data. Early life reliability prediction is challenging due to the lack of available data thus it is important to use a proper method for reducing this uncertainty. Bayesian method has been used widely in different area of studies to model uncertainty exist in a probabilistic way. This paper proposes a novel model to facilitate reliability prediction of non-repairable evolutionary product early after production using Bayesian method. The proposed Bayesian model joins prior information on past product failure and sparse few field data on new product’s performance. This approach helps us to overcome one of the important obstacle in the new product early reliability prediction which is lack of data. This study shows that Bayesian model outcomes are more accurate and logical compared to classical methods and indications are favorable regarding the model’s practicality in industry applications.