Assessment of Abrasion-Induced Visual Defects in Twin Screw Wet Granulation Using Wall Friction Measurements

Starting point of the presented study were abrasion effects occurring during a twin screw wet granulation (TSG) process of a new chemical entity (NCE) formulation, resulting in gray spots on the final tablets. Several actions and systematic changes of equipment and process parameter settings of TSG process were conducted which reduced the visual defect rate of the tablets, i.e., gray spots on the surface, below the specification limit. To understand the rationale and mechanism behind these improvements, correlations of defect rates and wall friction measurements using a Schulze ring shear tester were evaluated. To check the suitability of the method, a broad range of wall materials as well as powder formulations at various moisture levels were investigated with regard to their wall friction angle. As differences in wall friction angle could be detected, further experiments were conducted using wall material samples made out of different screw materials for TSG. Evaluation of these screw wall material samples gave first hints, which screw materials should be preferred in regard of friction for TSG process. In the finally presented case study, wall friction measurements were performed using the above mentioned NCE formulation with known abrasion issues at TSG processing. The results confirmed that changes which led to a reduced visual defect rate of tablets correlated with a decreased wall friction angle. The results suggest wall friction measurements as a potent tool for equipment selection and establishment of a suitable process window prior to conducting TSG experiments. Graphical abstract

Non-Visual Defect Identification by Dopant Analysis Method in FinFET Devices

For a specific IDDQ failure only around SRAM cell boundary, we conducted a systematic investigation in the lab involving electrical, physical, and chemical analysis. Following electrical test locating the failure area according to PEM (photon emission microscopy) and physical defect analysis resulting in NDF (no defect found), we explored an alternative method to define the failure. In this paper, we demonstrated the success of using tunneling AFM (TUNA) in diagnosing such an IDDQ failure occurring in FinFET devices. AFM (TUNA) analysis was able to visualize clearly the dopant discrepancies in comparison between the IDDQ fail and pass references in FinFET transistors. The dopant abnormalities indicated the current IDDQ fail was caused by processes that impaired the dopant implantation.

Development of a User-Centric Bridge Visual Defect Quality Control Assisted Mobile Application: A Case of Thailand’s Department of Highways

Digital innovations have changed the way many industries operate, but the construction industry has been slow to adopt these technologies. However, challenges such as low productivity, project overruns, labor shortages, and inefficient performance management have motivated Thailand’s Department of Highways to adopt digital innovations to build a competitive advantage. Because this industry requires a large work force, obstacles to collaboration can result in ineffective project management. We aimed to improve collaboration on bridge inspections that typically requires the involvement of many people, personal judgement, and extensive travel to survey bridges across the country. One major challenge is to standardize human judgement. To address these challenges, we developed a user-centric bridge visual defect quality control mobile application to improve collaboration and assist field technicians to conduct visual defect inspection. Our results can be used as a case study for other construction firms to embrace digital transformation technologies. This research also demonstrates the new-product development process using the new technology in known markets innovation development and technology acceptance model. We offer several recommendations for future research, including other infrastructure applications.

Prevalence of the use of Hearing Aid Compared to the use of Seeing Aid among Nigerian Youths

Needed attention has not been paid to hearing defects and the use of hearing aids compared to the enormous attention given to visual problems. This study aimed to compare the prevalence of visual defect to hearing defect and by extension the frequency of use of medicated glasses compared to the use of hearing aid. A total of 500 questionnaires were distributed randomly among students of Nnamdi Azikiwe University, Nnewi campus. Four hundred and seventy eight was retrieved, out of which 11 were disqualified due to improper filling, leaving us with a total of 467 properly filled questionnaires (representing 93.4% of the total number). Results were computed in simple percentages. Our results show that only 1.5% of the study population reported positive for hearing defect. Out of this number, only one respondent uses a hearing aid (representing 0.21% of the population). This is unlike seeing defect in which 117 (25.05% of total population) reported positive and 41.81% of them attested to the use of medicated glasses (representing 15.85% of the total population). All subjects who had hearing defects are aged between 15 and 25 (15-20 = 57.14%; 21-25 = 42.86%). This is similar to the case of seeing defects where the majority (70%) is aged 15-20 and 26% are aged 21-25, totally 96% of cases. We believe the enormity of persons subscribing to seeing aids are so because of massive awareness and affordability of medicated glasses, which in some cases is made available at no cost to the patient. The lack of data on persons with hearing defect in the country as well as the lack of publicity and investment in hearing aids has contributed greatly to the lack of interest and unwillingness on the part of patients to reach for hearing aids. Our results also show that females reported more both for hearing defects and seeing defect relative to their number. We therefore propose a better information system in which hearing aids are not only made available but also affordable and the masses are rigorously enlightened as to the health and social benefits.

About the Application of Autoencoders For Visual Defect Detection

Visual defect detection is a key technology in modern industrial manufacturing systems. There are many possible appearances of product defects, including distortions in color, shape, contamination, missing or superfluous parts. For the detection of those, besides traditional image processing techniques, convolutional neural networks based methods have also appeared to avoid the usage of hand-crafted features and to build more efficient detection mechanisms. In our article we deal with autoencoder convolutional networks (AEs) which do not require examples of defects for training. Unfortunately, the manual and/or trial-and-error design of AEs is still required to achieve good performance, since there are many unknown parameters of AEs which can greatly influence the detection abilities. For our study we have chosen a well performing AE known as structural similarity AE (SSIM-AE), where the loss function and the comparison of the output with the input is implemented via the SSIM instead of the often used L1 or L2 norms. Investigating the performance of SSIM-AE on different data-sets, we found that its performance can be improved with modified convolutional structures without modifying the size of latent space. We also show that finding a model with low reconstruction error during training does not mean good detection abilities and denoising AEs can increase efficiency.