Abstract
The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.
Highlights
Furniture manufacturing industries usually perform operations in the form of multiple independent stages
The motivation for wooden panel defect detection using deep learning (WDD-DL) is how to achieve high accuracy for edge-glued wooden defect detection in the furniture industry? Chen et al (2018) published a paper on edge detection based on machine vision, which describes the use of edge filter, canny operator and pixel-wise width calculation, but the results provided for accuracy were quite inefficient
WDD-DL has better defect detection achieved by Harris corner and Gabor filter based on the results demonstrated in the experiment section
Summary
Furniture manufacturing industries usually perform operations in the form of multiple independent stages. Each stage requires a particular operation that includes traditional ways of human skills based tasks, i.e., cutting, edge banding, border smoothing, etc. Process automation will help with quality control (Q.C.) checking for edge defect problems with better accuracy and less human dependency. The furniture manufacturing process can be detailed as initially importing raw material depending on the type of wood requirements. A wooden panel is produced with proper cutting and polishing, a defect-free panel is passed on to the stage. Selection of wooden panel is based on the respective furniture parts specification, which is passed through an edge-glued banding machine. A traditional approach shows the oversized band is cut manually for border smoothing
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.