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Fused deposit modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts. This paper presents a solution to the problem of detecting defects on the outer surface in the additive process of FDM.

A multi-view and all-round vision detection method is introduced where the detection field of view is changing with the vector of the outer surface during the printing process on the six degrees of freedom robot FDM printer.

After the image is preprocessed, this paper can identify the defects effectively according to its laminate structure, and introduces a mathematical matrix to represent the defects which will be classified into three typical types according to the geometry shape and area distribution.

This research only focuses on the feasibility of the defects detection method. To create the object of high quality, more research is needed to account for the process parameters which significantly cause the defects.

This work will help to detect the defects online, monitor the printing quality of the outer surface, reduce the waste of printed filaments, etc.

This study is among the first to present a multi-view and all-round vision detection method to detect defects on the outer surface in the additive process of FDM; proposes a means of identifying defects according to its laminate structure; and introduces a mathematical matrix to represent the defects which may be used in quality assessment.

Combined defects in ball bearings may be caused during the use or manufacturing process, which can significantly affect their vibration characteristics. The previous defect models in the literature can only describe single defects such as the surface waviness and localized defect. This paper aims to propose an in-depth understanding of radial vibrations of a ball bearing with the combined defect.

A dynamic model for a ball bearing with the combined defect including the surface waviness and localized defect on its races is proposed. The effects of the combined defect sizes on the radial bearing vibrations are investigated. The results from the proposed model considering the combined defect are compared with the available results from the previous methods considering the single defects.

The acceleration amplitude is significantly affected by the surface waviness, localized defect and the combined defect on its races. The effect of the combined defect on the acceleration amplitude is larger than that of the single defect. The amplitude and peak frequency of the spectrum of acceleration for the combined defect increases with the defect sizes. The RMS value of the accelerations for the combined defect increases with the combined defect sizes.

Consequently, the proposed model can predict more accurate and in-depth understanding of the radial vibrations caused by the combined defect in the ball bearing.

This work aims to demonstrate the vibration suppression of the rotor system with localized defects on bearing using an integral squeeze film damper (ISFD).

Experiments were carried out to study the vibration characteristics of the rotor system with ISFD mounted on fault deep groove ball bearings. Three fault bearings including bearing with outer race defect, inner race defect and ball defect have been used in this paper. The results were compared by use of vibration acceleration level, continuous wavelet transform and envelope spectrum.

It was found that ISFD shows excellent damping and vibration attenuation characteristics of the rotor system with defective bearing. The fault bearing rotor system with external ISFD considerably reduces the vibration energy and amplitude compared with the system without ISFD.

There is a dearth of experimental research pertaining to vibration characteristics of rotor system support by defective bearings with ISFD. Besides, the test provides evidence for the application of ISFD in vibration control of the rotor system with incipient defects on bearing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0144/

In industry applications, polymer hybrid bearings have become widespread in recent years owing to the lack of lubricant requirements, particularly in areas requiring hygiene. The additive manufacturing method gives significant advantages to have complex machinery parts, and it has become popular in the industry in recent years. However, it has some inherent disadvantages caused by layered deposition/addition of the materials, and the probability of the localized defect is much higher than in the conventional manufacturing methods. This study aims to investigate the effect of the outer race defect on the characteristics of vibration and service lifetime of hybrid polymer ball bearings produced with the stereolithography (SLA) additive manufacturing method.

In this study, polymer bearings’ races were produced with the additive manufacturing SLA method, and the outer race defect was analyzed with measured vibrations.

The results show that the additive manufacturing method suggests a practical solution for producing a polymer hybrid ball bearing. On the other hand, the hybrid three-dimensional-printed bearing, which has an outer race defect, worked for approximately 8 h without any problems under a 1 kg load and a shaft speed of around 1,000 rpm. In addition, when there is a defect in the outer and/or inner race of the ball bearing, the crest factor and kurtosis of the vibration are higher than faultless ball bearing, as expected.

This paper provides valuable information on the lifetime and vibration characteristics of polymer hybrid ball bearing produced by means of additive manufacturing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2023-0183/

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

For many applications, including space applications, the usability and performance of a component is dependent on the surface topology of the additively manufactured part. The purpose of this paper is to present an investigation into minimizing the residual surface roughness of direct metal laser sintering (DMLS) samples by manipulating the input process parameters.

First, the ability to manipulate surface roughness by modifying processing parameters was explored. Next, the surface topography was characterized to quantify roughness. Finally, microthruster nozzles were created both additively and conventionally for flow testing and comparison.

Surface roughness of DMLS samples was found to be highly dependent on the laser power and scan speed. Because of unintended partially sintered particles adhering to the surface, a localized laser fluence mechanism was explored. Experimental results show that surface roughness is influenced by the varied parameters but is not a completely fluence driven process; therefore, a relationship between laser fluence and surface roughness can be incorporated but not completely assumed.

This paper serves as an aid in understanding the importance of surface roughness and the mechanisms associated with DMLS. Rather than exploring a more common global energy density, a localized laser fluence was initiated. Moreover, the methodology and conclusions can be used when optimizing parts via metal additive manufacturing.

This paper aims to use 3D laser sensors to collect high‐density data that are required for defect detection and localization at high‐production rates in manufacturing facilities.

The high‐speed sensors use Ethernet communications to transport large amounts of data and resolve any synchronization issues.

Modern laser sensor technology provides the ability to detect and quantify defects in high‐volume manufacturing, wherever defects are located. Laser line sensors provide high speed, high‐density data for full surface inspection. Synchronization and communications issues are simplified by the FireSync™ platform, making system integration straightforward, and maximizing reliability.

This paper provides detailed 3D data at high speed and uses multiple (binocular) scanners to overcome problems of occlusion.

Introduction Laser‐based inspection systems originated in the late 1960s to automate the inspection task. Applied primarily to defect detection in flat, homogeneous products, these systems gained widespread use in the 1970s and through the 1980s. The bright, coherent light source provided the means to capture a range of defect optical effects including absorption, reflectivity, transmissivity, distortion and scattering. Unfortunately, early laser systems employed extremely inefficient light collection techniques. Much of the defects' optical information was lost. Fifteen to 20 years on, almost all commercial laser systems continue to employ this technology.

The “chiralisation” of Euclidean polygonal tessellations is a novel, recent method which has been used to design new auxetic metamaterials with complex topologies and improved geometric versatility over traditional chiral honeycombs. This paper aims to design and manufacture chiral honeycombs representative of four distinct classes of 2D Euclidean tessellations with hexagonal rotational symmetry using fused-deposition additive manufacturing and experimentally analysed the mechanical properties and failure modes of these metamaterials.

Finite Element simulations were also used to study the high-strain compressive performance of these systems under both periodic boundary conditions and realistic, finite conditions. Experimental uniaxial compressive loading tests were applied to additively manufactured prototypes and digital image correlation was used to measure the Poisson’s ratio and analyse the deformation behaviour of these systems.

The results obtained demonstrate that these systems have the ability to exhibit a wide range of Poisson’s ratios (positive, quasi-zero and negative values) and stiffnesses as well as unusual failure modes characterised by a sequential layer-by-layer collapse of specific, non-adjacent ligaments. These findings provide useful insights on the mechanical properties and deformation behaviours of this new class of metamaterials and indicate that these chiral honeycombs could potentially possess anomalous characteristics which are not commonly found in traditional chiral metamaterials based on regular monohedral tilings.

To the best of the authors’ knowledge, the authors have analysed for the first time the high strain behaviour and failure modes of chiral metamaterials based on Euclidean multi-polygonal tessellations.