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Product quality inspection is of importance in manufacturing industries to ensure that low quality or unqualified products are not delivered to the consumer. Human inspection has many limitations such as low accuracy or speed due to factors such as tiredness and boredom. Traditional 2D vision inspection also has limitations of product shape complexity or flexibility. Thus, automated 3D vision inspection is anticipated to meet the requirements of higher applicability. This paper seeks to address these issues.

In many product quality inspection problems, geometrical parameters of the industrial parts are commonly used as the basis of quality inspection. Machine vision is widely applied to acquire such kind of parameters. Comparing to traditional 2D vision, 3D vision can acquire 3D coordinates of the object directly, so that the inspection can be accomplished which is difficult to do with 2D vision. As an active vision technique, structure light system (SLS) is applied to acquire the 3D coordinate information of inspected object in this paper. On the basis of point cloud and regression analysis, features relative to quality are defined and extracted as the attributes for the product classification. Three data mining techniques are applied to accomplish the classification in this paper, which include decision trees, artificial neural networks and support vector machine.

A new intelligent automated 3D vision quality inspection for assembly lines has been developed, which comprises structure light system (SLS) and data mining approaches such as decision tree, artificial neutral networks and support vector machine.

The combination of structure light system (SLS) and data mining approaches makes the automated quality inspection available. The proposed system is easy to be implemented and flexible for different types of products.

The main cause of aseptic inflammation after an in vivo implantation is that Poly(L-lactide) (PLLA) and Poly(D-lactide) have a slower degradation and absorption rate, while Poly(D, L-lactide) (PDLLA) has a much faster degradation rate than PLLA because of its amorphous structure. Also, the hydrolyzate of Hydroxyapatite (HA) is alkaline, which can neutralize local tissue peracid caused by hydrolysis of Polylactic acid.

In this study, the selective laser sintering (SLS) technique was chosen to prepare bone scaffolds using nano-HA/PDLLA composite microspheres, which were prepared by the solid-in-oil-in-water (S/O/W) method. First, the SLS parameters range of bulk was determined by the result of a single-layer experiment and the optimized parameters were then obtained by the orthogonal experiment. The tensile property, hydrophobicity, biocompatibility, biological toxicity and in vitro degradation of the samples with optimized SLS parameters were characterized.

As a result, the samples showed a lower tensile strength because of the many holes in their interior, which was conducive to better cell adhesion and nutrient transport. In addition, the samples retained their inherent properties after SLS and the hydrophobicity was improved after adding nano-HA because of the OH group. Furthermore, the samples showed good biocompatibility with the large number of cells adhering to the material through pseudopods and there was no significant difference between the pure PDLLA and 10% HA/PDLLA in terms of biological toxicity. Finally, the degradation rate of the composites could be tailored by the amount of nano-HA.

This study combined the S/O/W and SLS technique and provides a theoretical future basis for the preparation of drug-loaded microsphere scaffolds through SLS using HA/PDLLA composites.

The purpose of this paper is to study the effect of feeding scheme on melt flow and temperature field during the steady-state of level-pour direct-chill (DC) casting of A390 alloy hollow billet and optimize the design of feeding scheme.

Melt flow and temperature field are investigated by numerical simulation, which is based on a three-dimensional mathematical model and well verified by experiments.

The numerical results reveal that both melt flow and temperature field are obviously affected by the feeding scheme. The homogeneity of melt flow and temperature field in hollow billet with the feeding scheme of modified four inlets are better than the other feeding schemes. Experimental results show that crack can be eliminated by increasing the number of feeding inlets. The primary Si size appears unaffected while the distribution of primary Si particles is highly affected by the change of feeding scheme. Only with the feeding scheme of modified four inlets can fine and uniformly distributed primary Si particles be achieved.

The paper includes implications for the design of feeding scheme in level-pour DC casting of hollow billet for practical use.

This paper develops different feeding schemes for level-pour DC casting of hollow billet and optimizes the design of feeding scheme.

The aim of this paper was to prepare a stable fluorescent disperse yellow paste by wet grinding process by adding naphthalene sulphonic derivative dispersing agent.

The dispersants 2-naphthalenesulphonic acid (NNO), naphthalene-sulphonic acid (MF) and benzyl naphthalene sulphonate formaldehyde condensate (CNF) were used to disperse the yellow dye. The particle size of the paste was characterised by particle size analyser. The paste centrifugal stability, diffusion properties, morphology and thermal properties were also tested for assessing its stability which could be helpful to prepare inks with good stability.

The particle sizes of dye pastes with dispersing agent NNO, MF and CNF were 161.1, 150.0 and 136.0 nm, respectively, after grinding for 6 h. The dye paste grinded with dispersing agent CNF presented good centrifugal and thermal properties. TEM images demonstrated that the morphologies of dye pastes grinded with dispersing agent MF and CNF were homogeneous nearly spherical nanoparticle and rarely generated agglomeration and precipitation.

The paste used for aqueous inkjet ink exhibited excellent thermal stability.

A disperse fluorescent yellow paste was mixed with a dispersant naphthalene sulfonic derivative via wet grinding process to prepare thermal transfer ink with good fluorescence. The paper aims to discuss these issues.

The surface tension, viscosity, pH value, zeta potential, stability and the morphology of ink samples were tested after the storing process.

The morphology of paste was homogeneous nearly spherical nanoparticles and the particle size was about 100 nm from the transmission electron microscopy (TEM), which was similar to the average particle size obtained from the particle size analyser.

The paste particle size was 126.8 nm after storing at 50°C for one week. The addition of diethylene glycol was conducive to high fluorescent reflectivity and gave good line image quality both in warp and weft directions due to the low viscosity. Inkjet printed polyester fabrics achieved excellent rubbing, laundering and thermal subliming fastnesses.

The polyester fabrics thermal transferred with the ink contained diethylene glycol represented higher fluorescent reflectivity and gave better line image quality both in warp and weft directions. The inkjet printed polyester fabrics showed excellent colour reproducibility and all the fastnesses, including rubbing, laundering and thermal subliming, were higher than Grade 4.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.