Low cost three-dimensional virtual model construction for remanufacturing industry

Abstract

Remanufactured products can save up to 80% of production and energy costs whilst generating lower CO 2 emissions. The key success factors for remanufacturing are quality, lead-time and cost. Extensive work within the industry and the detailed analysis of the remanufacturing process has shown that component inspection has significant bearing on overall productivity. Remanufacturing lacks automation because activities are predominantly manual. Automation of remanufacturing process will not only decrease the number of non-remanufacturable components, through decreasing cost and increasing consistency in quality, but also attract industries to design for remanufacture. A digital model of the component is required to automate the disassembly process and move towards industry 4.0 and cyber physical systems. There are several expensive techniques to create a digital model, which are not feasible for the remanufacturing industry. The research paper aims to check feasibility of using Visual Structure for Motion (VFM), a relatively low cost method, to develop a 3D digital model, for automation of the automotive engine (in as received condition) disassembly process using industrial robots. These experiments assess the scientific feasibility of using Videogrammetry to acquire pre-disassembly 3D model of the engine. Multiple 2D images were acquired and processed to find matching common features. The location of the camera was calculated through the matching features, producing a three-dimensional digital representation of the captured volume. A sparse point cloud was initially created and was then converted into a dense 3D point cloud. The 3D point cloud was converted into a meshed model. 2D images were stitched together to create a virtual model of the engine with surface texture and colour. Small features were clearly visible in the 3D model.

Assessing relations between Circular Economy and Industry 4.0: a systematic literature review

“…Wang, Zhang, and Zuo 2016) x (J. Xu 2009) x (Zallio and Berry 2017) x T o t a l 7 4 3 3 2 2 1 Thomas, and Trentesaux 2013) x (Deschamps et al 2018) x (He, Xu, and Xu 2010) x (Karastoyanov and Karastanev 2018) x (Kuik, Kaihara, and Fujii 2016) x x x x (Panarotto, Wall, and Larsson 2017) x (Rönnlund et al 2016) x (Schäfers and Walther 2017) x (Schroeder et al 2016b) x (Schroeder et al 2016a) x (Siddiqi et al 2017) x (Simons 2017) x (Sun and Wang 2011) x (Trentesaux and Giret 2015) x x (Turner et al 2016) x (van van Schaik and Reuter 2016) x (van Schalkwyk et al 2018) x (X. V. x x (Zhao, Dang, and Zhang 2011) x Total 6 6 5 4 2 2 1…”

“…Simulation was the prevalent technique, followed by AM, BDA, and the IoT. From one perspective, simulation (in the form of blockchain) was exploited for (i) auditing cooperative CE networks (Alexandris et al 2018), (ii) assessing the impact of business model changes (Simons 2017), for example, through agent-based or multi-method approaches , and (iii) assessing redistributed manufacturing or service-based design (Panarotto, Wall, and Larsson 2017) (French, Benakis, and Marin-Reyes 2017) x (Ge and Jackson 2014) x (Karastoyanov and Karastanev 2018) x (Kuik, Kaihara, and Fujii 2016) x (Lahrour and Brissaud 2018) x (Le, Paris, and Mandil 2017a) x (Le, Paris, and Mandil 2017b) x (Leino, Pekkarinen, and Soukka 2016) x (Liu et al 2016) x (Ruggeri et al 2017) x (Shanshan Yang et al 2018) x (Siddiqi et al 2017) x (Stark et al 2014) x (Tolio et al 2017) x (X. V. x (B. Xu 2016)…”

Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network

“…It is a rapid, economical, coherent and realistic method of examination and assessment [8]. It enhances the computer technology by significantly reducing the cost of digital imaging hardware and software [9], and the reduction of time [10] exceptionally.…”

Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review

“…The remanufacturing of automotive parts is a growing area of research interest (Govindan et al, 2016). Remanufacturing of parts may lead to lower overall CO 2 outputs along with reduced energy requirements (of up to 80%) in manufacturing (Siddiqi et al, 2019). Yang et al (2015) provide a methodology to assess the environmental impact of extending the lifecycle of products through remanufacture, proposing a framework to establish the environmental gain from this action at the design stage, along with automotive industry case study examples featuring alternators and engine blocks.…”