Achieving remanufacturing inspection using deep learning

Abstract

Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

“…The input training image data in the DC-GAN [ 23 ] model is normalized in the range [−1,1] before giving to the discriminator. Hence, in the last Deconv layer of the generator, tanh activation [ 31 ] is utilized to set the generated data range to [−1,1].…”

A Holistic Approach to Identification of Covid-19 Patients from Chest X-Ray Images utilizing Transfer Based Learning

“…The capacity is appended to every neuron in the system and decides if it ought to be actuated or not, founded on whether every neuron’s input is significant for the model’s expected output. In this investigation, we utilized two individual activation functions, for example, Sigmoid [26] and ReLU [23] activation function. We utilized Sigmoid in Base CNN model and ReLU in rest of different models as initiation work.…”

“…Sigmoid: The sigmoid activation function is here and there alluded to as the strategic capacity or crushing capacity in some literatures [26]. The Sigmoid is a non-direct enactment work utilized for the most part in feedforward neural systems.…”

“…The sigmoid capacity shows up in the yield layers of the deep learning structures, and they are utilized for anticipating likelihood-based yield and has been applied effectively in binary characterization issues, demonstrating strategic relapse undertakings just as other neural system areas [26].…”

ABLE: Attention Based Learning for Enzyme Classification

“…The hidden layers in the CNN model used a Rectified Linear Unit (ReLU) [44] activation function whereas the sequence based networks, LSTM, BiLSTM and GRU, used a Hyperbolic Tangent [45] activation function for the hidden layers. The final output fully connected layer for all the deep learning models used a Softmax activation function.…”