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The objective of this article is to evaluate and compare the performance of two machine learning (ML) algorithms, i.e. support vector machines (SVMs) and random forests (RFs), when classifying seven states of operation of an electric motor using the Mel-frequency cepstral coefficients (MFCCs) as extracted representative features.

The extracted MFCCs are calculated using the motor’s vibration and audio signals separately.

After the training, the SVM model obtained a mean accuracy of 100% for the MFCCs obtained from database vibration signals and 69.6% for the database of audio signals.

The ML strategies and results reported are limited to the well-known data for industrial electric motors used in the evaluations, although it was performed tests and cross-validations with unseen data and the information from the confusion matrix.

The success of these methodologies in defect classification, where the RF presented a mean accuracy of 99.15% for the vibration signals and 63.82% for the audio signal, enables the use of this ML and extracted features as a predictive tool for failure and anomaly detection, lifetime predictions and online real-time monitoring.

It is the first time that the MFCCs are being used for anomaly detection in vibration and audio signals for electrical motors, as this extracted feature is usually used for human speech identification in the literature.

Many studies only take into account the ply stacking sequence as the design variable to determine the optimal ply drop-off location; however, it is necessary to optimize other parameters that have a direct influence on the ply drop-off site such as which plies should be dropped and in which longitudinal direction. That way, the purpose of this study is to find the most significant design variables relative to the drop-off location considering the transversal and longitudinal positions, seeking to achieve the optimal combination of ply drop-off locations that provides excellent performance for the laminate plate.

This study aims to determine the optimal drop-off location in a laminate plate using the finite element method and an approach statistical with design of experiments (DOE).

The optimization strategy using DOE revealed to be satisfactory for analyzing laminate structures with ply drop-offs, demonstrating that not all design factors influence the response variability. The failure criterion response variable revealed a poor fit, with an adjusted coefficient of determination lower than 60%, thus demonstrating that the response did not vary with the ply drop-off location. Already the strain and natural frequency response variables presented high significance. Finally, the optimization strategy revealed that the optimal drop-off location that minimizes the strain and maximizes the natural frequency is the ply drop-off located of the end plate.

It was also noted that many researchers prefer evolutionary algorithms for optimizing composite structures with ply drop-offs, being scarce to the literature studies involving optimization strategies using response surface methodology. In addition, many studies only take into account the ply stacking sequence as the design variable to determine the optimal ply drop-off location; however, in this study, the authors investigated other important parameters that have direct influence on the ply drop-off site such as which plies should be dropped and in which longitudinal direction.

This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.

The present paper aims at the multi-objective optimization of a reentrant hexagonal cell auxetic structure. In addition, a parametric analysis will be carried out to verify how each of the design factors impact each of the responses.

The multi-objective optimization of five different responses of an auxetic model was considered: mass, critical buckling load under compression effort, natural frequency, Poisson's ratio and failure load. The response surface methodology was applied, and a new meta-heuristic of optimization called the multi-objective Lichtenberg algorithm was applied to find the optimized configuration of the model. It was possible to increase the failure load by 26.75% in compression performance optimization. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively. This paper addresses something new in the scientific world to date when evaluating in a multi-objective optimization problem, the compression and modal performance of an auxetic reentrant model.

It was possible to find multi-objective optimized structures. It was possible to increase the critical buckling load by 42.82%, and the failure load in compression performance by 26.75%. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%, and decrease the mass by 15.97%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, increase the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively.

There is no work in the literature to date that performed the optimization of 5 responses simultaneously of a reentrant hexagonal cell auxetic structure. This paper also presents an unprecedented statistical analysis in the literature that verifies how the design factors impact each of the responses.

Aircraft wings, one of the most important parts of an aircraft, have seen changes in its topological and design arrangement of both the internal structures and external shape during the past decades. This study, a numerical, aims to minimize the weight of multilaminate composite aerospace structures using multiobjective optimization.

The methodology started with the determination of the requirements, both imposed by the certifying authority and those inherent to the light, aerobatic, simple, economic and robust (LASER) project. After defining the requirements, the loads that the aircraft would be subjected to during its operation were defined from the flight envelope considering finite element analysis. The design vector consists of material choice for each laminate of the structure (20 in total), ply number and lay-up sequence (respecting the manufacturing rules) and main spar position to obtain a lightweight and cheap structure, respecting the restrictions of stress, margins of safety, displacements and buckling.

The results obtained indicated a predominance of the use of carbon fiber. The predominant orientation found on the main spar flange was 0° with its location at 28% of the local chord, in the secondary and main web were ±45°, the skins also had the main orientation at ±45°.

The key innovations in this paper include the evaluation, development and optimization of a laminated composite structure applied to a LASER aircraft wings considering both structural performance and manufacturing costs in multiobjetive optimization. This paper is one of the most advanced investigations performed to composite LASER aircraft.

Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives.

This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed.

Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition.

The proposed methodology is verified through a real experimental setup.

This study aims to analyze the organizational learning capability relationship with operational performance and frugal innovation across Brazilian companies.

Quantitative research was performed using collected data from 154 firms, which were analyzed using structural equation modeling.

The results showed that organizational learning capability is an antecedent of frugal innovation. The results also predict a better operational performance for companies that actively innovate cost-effectively. Another result was the positive relationship between the organizational learning capability and the operational performance. The authors found that the indirect and positive relationship between organizational learning capability, frugal innovation and operational performance was confirmed, reinforcing the literature.

A theoretical implication of this study can be seen in the establishment of the relationship between organizational learning capability, frugal innovation and operational performance since no studies linking these variables together were found. Therefore, the organizational learning capability and the frugal innovation can be considered facilitators of the operational performance.

Managers should consider organizational learning and frugal innovation when thinking about firms’ operational performance. In this way, to facilitate and achieve higher performance, it was found that organizational learning capability and frugal innovation have a great deal of impact on operational performance.

At frugal innovation, the needs of citizens are prioritized. It is a great instrument to face crises since it consists of developing simpler and cheaper products and services quickly, making them accessible to a larger group of consumers.

This study seeks to understand whether Brazilian companies are moving toward a more frugal innovation strategy. The study opens the possibility of showing whether the organizational learning capability has also impacted this change.

Literature has long been discussing indigenous forms of informal practices whose cultural origins are concealed. We first seek to provide a contextualization of the importance of an informal practice that is salient within the Brazilian culture – the Brazilian jeitinho. We then provide a historical background of the jeitinho, as well as an attempt to come up with a definition of the construct, which is by no means a definitive one. We explore how the jeitinho plays a role in the Brazilian organizational scenario, which may be useful for international companies aiming to do business in the country. Finally, we present a set of recommendations as how to deal with jeitinho in organizational-related occasions, drawing on the Brazilian historian Sérgio Buarque de Holanda's “cordial man” concept, which suggests that the roots of Brazilian culture lie in the patriarchal environment of the colonial period. We do not have in this chapter the intention of characterizing the multiplicity of Brazilian business practices, what would be an impossible task to accomplish in light of the enormous diversity of social contexts in Brazil. What we present in the chapter are some concepts and tools for working with and, fundamentally, understanding the organizational and social process the Brazilian context, which we hope may be useful for those interested in doing business in or with Brazil.

This study aims to develop a numerical identification and characterization of crack propagation through the use of a new optimization metaheuristics called Lichtenberg optimization.

The damage-identification problem is treated as an inverse problem, which combines finite element methods with intelligent computational methods to obtain the best possible response. To optimize the objectives, the Lichtenberg algorithm is applied, which includes concepts of random cluster growth in nature.

The simulations show that it is possible to determine the Lichtenberg spectrum algorithm a part of the structure to be removed and replaced in this case to stop the propagation.

The results show a very good crack identification in plates-like structures using the Lichtenberg algorithm (LA) based only in strain fields. Although many studies have reported on damage-identification-based optimization methods, very few have focused on the crack tip modeling and LA as the main solver.

Learning capability improves knowledge resources fosters innovative capabilities and firm competitiveness. The study aims to examine the human resource management (HRM) practice and employee creativity relationship using organizational learning capability (OLC) as a mediating variable.

Data were collected from 67 small-sized and 96 medium-sized firms. Confirmatory factor analysis was applied to establish construct validity and reliability. Structural equation modeling was used to evaluate the proposed model and hypotheses.

The results show that performance appraisal and employee creativity were positively related. Employee participation and employee creativity were positively related. Compensation and employee creativity were nonsignificantly related. OLC mediates the performance appraisal and employee creativity relationship. Similarly, OLC mediates the employee participation and employee creativity relationship. However, OLC did not mediate the compensation and employee creativity relationship.

Due to the research’s SME focus and cross-sectional data, the finding’s generalizability will be constrained.

The findings of the study would be useful to policymakers, stakeholders and management of SMEs in developing a supportive learning climate that promotes experiential and continuous learning cultures to ensure strategic capabilities, sustainable competitive advantage and innovativeness.

The study contributes to the extant literature on OLC, HRM practices and employee creativity by empirically evidencing that OLC mediates the performance appraisal, employee participation and employee creativity relationship.