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Refereed/Peer-reviewed Flapping wing systems are being developed by various institutions and research groups around the world with many systems developed that are capable of full flight. However, while instrumentation has been developed that is capable of measuring some of the characteristics of these systems, there is no complete solution. This paper seeks to take the first step toward instrumentation that could be applied to any flapping wing system. This first step is to identify and characterize the forces that are operating on flapping wing systems. This paper presents, in premiere, a systematic analysis of all cases that can create useful or parasitic aerodynamic loads along with the other major loads that would be experienced by these cases and methodology for how these can be measured with the ambition that it can become a framework to be used to characterize any flapping wing system.

In this study, computational fluid dynamics analysis was performed on a three-dimensional model of a Libellulidae wing to determine aerodynamic performance in gliding flight. The wing is comprised of various corrugated features alongside the spanwise and chordwise directions, as well as twist. The detailed features of real 3D dragonfly wing models, including all the corrugations through both span and chord, have not been considered in the past for a detailed aerodynamic analysis. The simulations were conducted by solving the Navier-Stokes equations to demonstrate gliding performance over a range of angles of attack at low Reynolds numbers. The numerical model was validated against experimental data obtained from a fabricated corrugated wing model using particle image velocimetry. The numerical results demonstrate that bio-inspired wings with corrugations compared to flat profile wings generate more lift with lower drag, trapping the vortices in the valleys of wing corrugation leading to delayed flow separation and delayed stall. The experimental and numerical results demonstrate that the methodology presented in this study can be used to measure bio-inspired 3D wing flow characteristics, including the influence of complex corrugations on aerodynamic performance. These findings contribute to the advancement of knowledge required for designing an optimized bioinspired micro air vehicle.

With the increased demands of smarter manufacturing approaches around the world, the process of industrial digital transformation is being pushed in and by both industry and academia. Learning factories and testing laboratories have been developed for decades for teaching and training purposes in Academia. Nowadays, as the future trend in industry, Industry 4 is being merged into the latest development of learning factories and testing laboratories. This paper presents the development and implementation of a bi-directional digital twin application in an Industry 4 testing laboratory at University of South Australia. The solution is based on the establishment of OPC UA connection between two cobots of different brands, the use of NX Siemens as a CAD simulation platform and a SCADA system from Inductive Automation. Due to differences between system interfaces, communication between different modules was challenging. Python OPC UA servers were developed. The digital twin replicates the physical system and is driven by inputs from the assembly cell. Refereed/Peer-reviewed

In this paper, an experimental study was performed to examine the flow characteristics of fabricated micro corrugated wings by fully mimicking the real 3D Odonata wing. For this purpose, a true scale hind wing from the Orthetrum caledonicum species with a semi span length of 60 mm was reconstructed by non-destructive close-range photogrammetry and fabricated with an advanced 3D printer. The accuracy of the proposed reconstruction technique was evaluated and compared with a 3D model of the same wing created using a Micro-Computed Tomography (CT) scanning technique to show that the close-range photogrammetry method was able to predict the pattern of micro corrugation of the wings with satisfactory fidelity. To do that, the corrugation patterns of both reconstructed wings were compared at different sections of the wings. Then, high-resolution Particle Image Velocimetry was used to investigate the flow field of the wing during gliding flight at three low Reynolds numbers Re = 5 × 103, Re = 8 × 103 and Re = 12 × 103, and angle of attack 10°. The results include free stream velocity, vorticity distribution, boundary layer, and flow visualization. The velocity contour and vorticity boundary layer of both wings were compared experimentally. The flow behavior around the corrugated patterns reconstructed from both methods were compared with satisfactory agreement. The results support that the corrugations of the wing act as turbulators to generate unsteady vorticity to transition the boundary layer from laminar to turbulent quickly, leading to delayed stall and improved aerodynamic performance. Moreover, this study shows the application of the presented photogrammetry method for corrugated wing reconstruction, which is fast, low-cost, non-destructive, with high replication accuracy for the next generation of micro air vehicles. Refereed/Peer-reviewed

This paper presents the multi-objective optimization in injection molding. The aim of this study is to determine the best alternative for the best moldability index and better alternatives which are close to the best alternative for an injected part in injection molding technology. Consideration of Taguchi and fuzzy analytic hierarchy process (FAHP) with technique for the order performance by its similarity to the ideal solution (TOPSIS) offers an efficient use approach to evaluate different objectives. Three common defects which reduce the final quality of the injected parts are short shot possibility, shrinkage rate and warpage. The selected material for injection is polypropylene. L18 orthogonal array of Taguchi is selected and for gaining accurate results, Finite Element Analysis (FEA) is applied. To validate the simulation result, the experiment is carried out for two circular flat plates with thickness of 1. mm. Finally, the highest moldability index is related to trial 2 for both simulation and experimental results. This study outcome shows instead of having one trial number which has the highest moldability index, there are 10 trial numbers with high moldability index between 0.6-1 for simulation results and 5 trial numbers with high moldability index between 0.6-1 for experimental results. It is clear, by using Taguchi with TOPSIS and FAHP if the best alternative is not possible to maintain, there are other alternatives which are very close to the best alternative with high moldability index. The main advantage of the proposed method is to increase the number of alternatives which in turn increases the moldability of the final product in injection molding. Refereed/Peer-reviewed

Industrial robots have served industry for decades, especially the automotive industry. The traditional industrial robotics have shown their limits in task management due to the rigid and time-consuming programming and task planning. In recent years, to achieve the Industry 4 transformation, a broad range of collaborative robots (cobots) have been employed for autonomously executing tasks in different domains. Cobots, mainly used in manufacturing, were extensively developed, and integrated into real production contexts to optimise processes and improve productivity. However, motion control and programming for cobots remain an obstacle to achieve their potential as the procedure still takes time and disrupts processes. This paper critically reviews current mainstream control methods for industrial robotics to execute tasks and proposes an approach for rapid task re-planning for cobots in a flexible assembly workcell. The proposed method allows cobots to execute flexible assembly tasks for small batch production/assembly, or even single products and to re-plan the new task without interruption. Its advantages and limitations are also discussed Refereed/Peer-reviewed

Industry 4 laboratories, Learning Factories and Testlabs are vital facilities for the development and dissemination of the new industrial revolution. However, as they are implemented and reported in the literature at present, they are lacking the specific constituents and their combination and integration that would qualify them as revolutionary – i.e. being clearly distinctive from carefully implemented state of the art technology that has been the norm in the last decades. An essential attribute of Industry 4 systems, as presented in the literature, is the capacity for collection, processing and recording of critical data that describe and characterizes their state and the use of information extracted from the data to monitor, control and optimize the operations. Industry 4 laboratories need to provide real life context for teaching, research, business and industry purposes. This paper discusses the minimal required infrastructure for establishing relevant Industry 4 laboratories Refereed/Peer-reviewed

This paper presents the analysis of short shot possibility in injection molding. The aim of this research is to evaluate process and geometric parameters which increase the possibility of short shot in an injected part based on the proposed method. The analysis of short shot possibility was conducted via SolidWorks Plastics and Taguchi method for orthogonal array experiment of L18 to find the significant process and geometric parameters. Finite element method (FEM) is employed in SolidWorks Plastics for simulation. To validate the simulation result, an experimental study was conducted for two circular flat polypropylene of 1-mm thickness. Filling time, part cooling time, pressure holding time, and melt temperature were selected as process parameters, and gate type was selected as a geometric parameter. A new method of analysis for short shot defect is proposed herein to predict the possibility of short shot in injection molding before it occurs. The significance rate of each parameter in both experiments and simulation result was very close together which signifies the robustness of proposed method in evaluation of short shot possibility. Melt temperature was the most influential parameter with a contribution of 74.25 and 75.04%, and filling time with a contribution of 22 and 20.19% followed by gate type with a contribution of 3.69 and 3.93% for simulation and experimental results, respectively. Hence, based on response table of S/N ratio, the optimum levels of each parameter which leads to reduction in possibility of short shot are gate type at level 1, filling time at level 3, and melt temperature at level 3. Finally, melt temperature, filling time, and gate type considered as significant parameters which affect the possibility of short shot in injected parts. Refereed/Peer-reviewed

To our knowledge, this is first study that addresses dynamic scheduling in RFACs.Cell utilisation and due date tightness are the significant scheduling factors.Percentage of prediction error (9%) confirms the efficient of proposed methodology.The results of confirmation run improved by 11% compared to initial experiments. This paper presents Taguchi method coupled with fuzzy logic for dealing with multi-objective optimisation problems for dynamic scheduling in robotic flexible assembly cells (RFACs). This is the first study to address these particular problems. In this study, Taguchi optimisation method has been applied to reduce the number of experiments required for scheduling RFACs. The experiments are implemented with four different scheduling factors, namely sequencing rule, dispatching rule, cell utilisation and due date tightness. These factors are difficult to optimise considering the objectives of multiple functions instead of a single objective. Therefore, a multiple performance characteristics index (MPCI) based fuzzy logic approach has been developed to derive the optimal solution. The predicted results of MPCIs have been verified via a confirmation test. Results of the confirmation test show significant improvement in MPCI using the optimal levels of the scheduling factors.

Virtual computer-integrated manufacturing (VCIM) is a global integrated manufacturing system, capable of exploiting the locally as well as globally distributed resources. Production scheduling is one of the critical factors to the success of VCIM systems. In this paper, a VCIM integrated production scheduling model is proposed which allows optimisation of the production scheduling of simultaneous multiple product orders. In the model, two major issues of the production scheduling, namely agent selection and collaborative shipment scheduling for multiple product orders, are fully integrated together to take advantage of the shipment collaboration. The effectiveness of the proposed model is demonstrated by a comprehensive case study with the aid of Genetic Algorithm solver in Matlab. The achievements of this study can serve as the fundamental steps towards the developing a decision support system capable of helping decision makers to operate VCIM systems more effectively. Refereed/Peer-reviewed

Virtual computer-integrated manufacturing (VCIM) is a promising manufacturing strategy in today’s global market because it is capable of exploiting distributed resources effectively, both locally as well as globally. Resource scheduling is critical to the operation of VCIM systems. In this article, a novel model for resource scheduling in VCIM systems is developed. By integrating collaborative transportation scheduling into manufacturing resource scheduling, the proposed model can provide a better solution because of reduction in the fixed transportation cost. Performance of the developed model is verified by computer simulation for a case study producing computer hard disks. The proposed model serves as a fundamental step towards global optimisation of VCIM systems. Refereed/Peer-reviewed

Genetic Algorithm (GA) is one of the most general global optimisation solution methods used in countless number of works. However, like other search techniques, GA has weak theoretical guarantee of global optimal solution and can only offer a probabilistic guarantee. Having a GA capable of searching for the global optimal solution with very high success probability is always desirable. In this paper, an innovative structure of GA, in which adaptive restarting and chromosome elite transferring strategies are harmoniously integrated together, is proposed to improve the success rate of achieving global optimal solution of the algorithm. The robustness of the proposed GA structure is demonstrated through a number of case studies. Refereed/Peer-reviewed

Specialised laboratories and learning factories can play a significant role in developing and advancing the Industry 4 concept and production systems. However, there are many challenges that need to be addressed before creating qualified laboratories that have true Industry 4 characteristics. Some of those challenges originate from the varied definitions of Industry 4, from what different institutions and research groups consider critical/trivial to implement in a laboratory, from Universities' policies and regulations, and even taxation rules. Many challenges derive from the complexity of the Industry 4 eco-system and its continuous evolution. The objective of this paper is to provide a systematic analysis of the challenges in implementing viable Industry 4 laboratories in academia. These challenges need to be defined explicitly before they can be addressed, and relevant laboratories developed to maximise the success of what can be significant investment. Refereed/Peer-reviewed

Virtual computer-integrated manufacturing (VCIM) is a global integrated manufacturing system which can exploit locally as well as globally distributed manufacturing resources. Production scheduling plays an important role in the success of a VCIM system. In this paper, an innovative genetic algorithm (GA) is developed to search for optimal/sub-optimal solutions to the production scheduling problem in VCIM systems. The developed GA has a unique chromosome representation, two modified crossovers, three modified mutations, dynamic ranking selection, adaptive stop-and-restart-with-memory mechanism, and a parameter set tuned by the response surface method. The effectiveness of the developed GA is validated through a comprehensive case study. The computational data from the case study show that the developed GA outperforms three commercial optimisation solvers. The outcomes of this research serve as a foundation towards a global decision support system that can help decision makers to operate VCIM systems more effectively. Refereed/Peer-reviewed

This paper addresses the problem of simultaneously minimising the total costs of the final product produced by systems adopting a JIT approach. A cost which incorporates both the cost of production processes and the cost arising from the many potential risks associated with any reduction. A robust genetic approach is proposed in order to optimise the novel mathematical model published in [1]. Genetic operators adopted to improve the genetic search algorithm are introduced and discussed. Experiments are conducted to evaluate the performance of the proposed algorithm and an illustrative example is given. A comparison of the genetic operators used is made by means of evaluating different rates, to define the most suitable rate of crossover and mutation. The findings illustrate the effectiveness of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction.

Virtual computer-integrated manufacturing (VCIM) is a new manufacturing concept aimed at exploiting distributed manufacturing resources, both locally as well as globally. Recently, an innovative model for resource scheduling in VCIM systems, in which manufacturing scheduling and collaborative transportation scheduling are integrated together, has been proposed. In this paper, an innovative global optimisation method based on genetic algorithm (GA) is developed to optimise the integrated manufacturing–transportation scheduling problem recently raised in VCIM literature. The proposed GA with unique chromosome representation,modified genetic operators, and novel algorithm structure is capable of searching for the global optimal solution with very high success rate. The results achieved from 10 instances of a comprehensive case study have confirmed that the proposed GA outperforms three popular commercial optimisation solvers. Refereed/Peer-reviewed

This paper presents a new cross sectional shape of runner system in the injection molding. The aim of the new geometry is to reduce the scrap and cycle time and also the easier ejection of runner system from mold tools. Short shot defect in the plastic part during the injection molding process is analyzed by Solid Works Plastic to validate the new proposed geometry. Runner system with elliptical cross section is proposed with different ratio for two circular flat plates with thickness of 1mm. Finite Element Method (FEM) is employed in Solid Works Plastic for simulation of injected part. Filling time, melt temperature, mold temperature, pressure holding time, and pure cooling time are chosen as input for the injection machine. The contribution of this study is the design of new geometry of cold runner system for reduction in scrap and cycle time and also easier ejection of runner system in injection molding. This study outcomes show no short shot defect with the new geometry. Also, it was shown the significant reduction in scarp and cooling time. 25% and 2.5% reduction in scrap and cooling time were achieved compared to round cross section, respectively. Reduction in contact surface of runner system with mold walls improved the easier ejection of runner system out of the cavity as well. Refereed/Peer-reviewed

This paper introduces a new geometry of the gate system in injection moulding process. The corners of existing edge gates resultsin the turbulence of molten plastic into the cavity which leads to internal and external defects. The aim of the new geometry is toreduce the internal and external defects of injected parts. Also easier de-gating of final part for the new geometry which decreasesthe visible blemish of final part after de-gating is a target of this study. A new design of edge gate is suggested for two circularflat plates with a thickness of 1mm. Internal and external defects in the plastic part are analysed with SolidWorks Plastics tovalidate the proposed geometry. The contribution of this research is to modify the geometry of existing edge gate by removingthe corners of rectangular edge gate to reduce the scrap rate of injected parts. Also, a smooth flow of molten plastic into thecavities reduces the internal and external defects. The outcome demonstrates filling the cavities with no short shot. Also, no weldlines, meld lines or sink marks were detected with new design of the edge gate. Finally, the injected part with new design of theedge gate has less visible blemish in comparison with existing edge gate. Refereed/Peer-reviewed

Virtual Computer-Integrated Manufacturing (VCIM) is a new manufacturing system, developed to help small and medium-size enterprises survive and thrive in today’s competitive global market. VCIM systems are still, conceptually, being developed. In this chapter, the latest developments in VCIM systems are presented. There are two important developments in the systems, i.e. VCIM production scheduling model and VCIM production scheduling optimization approach. Current progress in the scheduling model as well as the optimization approach is presented herein. Refereed/Peer-reviewed

Just-In-Time (JIT) as a lean manufacturing approach plays a significant role in minimising costs and performances of products and services supplied to the global marketplace. However, there are many potential risks that cause significant disruptions to all supply chain members. This study proposes a genetic approach for optimising a novel mathematical model for simultaneously minimising the total cost of a final product and the potential risks related to these benefits. Specifically, it demonstrates the effectiveness of a genetic algorithm in optimising the JIT model developed in our previous paper. Genetic operators adopted to improve the genetic search algorithm are introduced and discussed. Experiments are carried out to evaluate the performance of the proposed algorithm using a simplified example. Comparison of four selection methods is done to define the best method that can be used in the proposed GA. The findings demonstrate the superiority of the proposed approach in the JIT system with focus on simultaneous cost-risk reduction. Refereed/Peer-reviewed

Due to increasing competition in the developing global economy, today's companies are facing greater challenges than ever to employ flexible manufacturing systems (FMS) capable of dealing with unexpected events and meeting customers' requirements. One such system is robotic flexible assembly cells (RFACs). There has been relatively little work on the scheduling of RFACs, even though overall scheduling problems of FMS have attracted significant attention. This paper presents Taguchi optimisation method in conjunction with simulation modelling in a new application for dynamic scheduling problems in RFACs, in order to minimise total tardiness and number of tardy jobs (NT). This is the first study to address these particular problems. In this study, Taguchi method has been used to reduce the minimum number of experiments required for scheduling RFACs. These experiments are based on an L9 orthogonal array with each trial implemented under different levels of scheduling factors. Four factors are considered simultaneously: sequencing rule, dispatching rule, cell utilisation and due date tightness. The experimental results are analysed using an analysis of mean to find the best combination of scheduling factors and an analysis of variance to determine the most significant factors that influence the system's performance. The resulting analysis shows that this proposed methodology enhances the system's scheduling policy. Refereed/Peer-reviewed

Due to the complexity of scheduling flexible manufacturing systems, the generation of production schedules requires an intelligent technique. Many artificial intelligence techniques such as fuzzy logic, genetic algorithms and neural networks have been successfully applied to the scheduling of advanced manufacturing systems. One such system is Robotic Flexible Assembly Cells (RFACs). Few studies have addressed the problem of scheduling RFACs. The major limitation is that these studies are limited to the assembly of only one product type. The objective of this study is to propose a new intelligent model of scheduling RFACs in a multi-product assembly environment, using fuzzy logic. Refereed/Peer-reviewed

This study describes the application of the developed model to the scheduling of the Robotic Flexible Assembly Cells (RFACs). The present study is a continuation of the paper titled "Development of a Fuzzy-Simulation Model of Scheduling Robotic Flexible Assembly Cells". This new model for multi-objective scheduling problems in RFACs was based on combining a fuzzy-based mathematical model with simulation software tools. In this study, a hypothetical case study of RFACs is presented to demonstrate the effectiveness of the developed model and then compare the results that are obtained by the new methodology with the common scheduling rules. The simulation results show that the performance of the proposed methodology outperforms the most popular scheduling rules from previous research. Refereed/Peer-reviewed

This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA. Refereed/Peer-reviewed

This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature. Refereed/Peer-reviewed

Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to “learn” from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers. Refereed/Peer-reviewed

This paper, presents an innovative methodology foranalysing and designing a class of large-scale, complex,constrained, stochastic and dynamic systems known as Multi-Product, Multi-Period Consumer Supply Network (MPMPCSN).The proposed method is embodied with both simulation andoptimisation kernels in an integrated framework. A mixedinteger non-linear programming (MINLP) model is developed,simulated and optimised by Genetic Algorithms (GA). Inaddition, the performance of the solution algorithm is evaluatedthrough tuning the proposed GA parameters using Taguchimethod coupled with a regression analysis. The impact of theproblem’s attributes (e.g. product families, consumers, planninghorizons), as well as the search algorithm parameters (e.g.population size, recombination rate, and mutation rate) and theinteraction among them on the quality of the solution, weredemonstrated in a representative case study Refereed/Peer-reviewed

Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GA's success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GA's exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GA's exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature. Refereed/Peer-reviewed

Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example. Refereed/Peer-reviewed

In this article, a bibliometric analysis of Genetic Algorithms (GA) throughout the history is conducted. A big picture of publications associated with GA is created. A number of dominant statistics of GA publications by years, research fields, document types, source titles, countries, institutions and authors are provided herein. In addition, some insights as well as future perspectives of publications associated with GA are discussed. Refereed/Peer-reviewed

Diesel exhaust particulate matter (PM) is deadly to humans, animals and plants so the future of diesel engine is uncertain. Alternative powered vehicles have major limitations and costly. Recent developments to limit PM emissions have significant disadvantages to the point where they cannot be considered to be reliable long term technical and economical solutions. Electrostatic filtration could be used together with existing filters or as a standalone system. The most popular method of decreasing PM emissions is by the use of ceramic diesel particulate filters which is not efficient at filtering ultrafine particulates. Electrostatic filtration is a promising approach which can capture ultrafine particulates which could be used in conjunction with ceramic DPFs, metallic flow through filters (FTF) or, ideally as a standalone system. Development of prototype electrostatic diesel particulate filtration systems (EDPS) requires reliable testing. Prototyping needs quick, repeatable and affordable results to validate theories and a solution had to be developed. This paper presents the development of the EDPS prototype testing procedures and equipment with preliminary test results. By repurposing proven test equipment for the use of exhaust sampling, a test rig and a repeatable procedure for testing prototype filters were developed with low initial and ongoing costs. Refereed/Peer-reviewed

This paper presents an evaluation of a plastic defect in injection molding before it occurs in the process; it is known as the short shot defect. The evaluation of different parameters which affect the possibility of short shot defect is the aim of this paper. The analysis of short shot possibility is conducted via SolidWorks Plastics and Taguchi method to determine the most significant parameters. Finite Element Method (FEM) is employed to analyze two circular flat polypropylene plates of 1 mm thickness. Filling time, part cooling time, pressure holding time, melt temperature and gate type are chosen as process and geometric parameters, respectively. A methodology is presented herein to predict the possibility of the short-shot occurrence. The analysis determined melt temperature is the most influential parameter affecting the possibility of short shot defect with a contribution of 74.25%, and filling time with a contribution of 22%, followed by gate type with a contribution of 3.69%. It was also determined the optimum level of each parameter leading to a reduction in the possibility of short shot are gate type at level 1, filling time at level 3 and melt temperature at level 3. Finally, the most significant parameters affecting the possibility of short shot were determined to be melt temperature, filling time, and gate type., {"references":["Shen, C., L. Wang, W. Cao, and L. Qian, Investigation of the Effect of Molding Variables on Sink Marks of Plastic Injection Molded Parts Using Taguchi DOE Technique. Polymer-Plastics Technology and Engineering, 2007. 46: p. 219-225.","Oktem, H., T. Erzurumlu, and I. Uzman, Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Materials and Design, 2007. 28: p. 1271-1278.","Chen, W.-C., P.-H. Liou, and S.-C. Chou, An integrated parameter optimization system for MIMO plastic injection molding using soft computing. Int J Adv Manuf Technol, 2014. 73: p. 1465–1474.","Mok, C.K., K.S. Chin, and J.K.L. Ho, An Interactive Knowledge-Based CAD System for Mould Design in Injection Moulding Processes. Int J Adv Manuf Technol, 2001. 17: p. 27–38.","Modrak, V., J. Mandulak, and D. Marton, Investigation of the influence of technological parameters on surface color of plastic parts. Int J Adv Manuf Technol, 2013. 69: p. 1757–1764.","Zhou, H., Computer Modeling for Injection Molding. 2013: A John Wiley & Sons, Inc., Publication.","Chiang, K.-T. and F.-P. Chang, Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology. International Journal of Advanced Manufacturing Technology, 2007. 35: p. 468-479.","Hassan, H., An experimental work on the effect of injection molding parameters on the cavity pressure and product weight. Int J Adv Manuf Technol, 2013. 67: p. 675–686.","Zhao, P., H. Zhou, Y. He, K. Cai, and J. Fu, A nondestructive online method for monitoring the injection molding process by collecting and analyzing machine running data. Int J Adv Manuf Technol, 2014. 72: p. 765–777.\n[10]\tGoodship, V., Troubleshooting Injection Moulding. Vol. 15. 2004.\n[11]\tHuang, M.-C. and C.-C. Tai, The Effective Factors in the Warpage Problem of an Injection-Molded Part with a Thin Shell Feature. Journal of Materials Processing Technology, 2001. 110: p. 1-9.\n[12]\tKitayama, S., R. Onuki, and K. Yamazaki, Warpage reduction with variable pressure profile in plastic injection molding via sequential approximate optimization. Int J Adv Manuf Technol, 2014. 72: p. 827–838.\n[13]\tLi, X.-P., G.-Q. Zhao, and C. Yang, Effect of mold temperature on motion behavior of short glass fibers in injection molding process. Int J Adv Manuf Technol, 2014. 73: p. 639–645.\n[14]\tMoayyedian, M., K. Abhary, and R. Marian. New Design Feature of Mold in Injection Molding for Scrap Reduction. in 2nd International Materials, Industrial, and Manufacturing Engineering Conference, MIMEC2015, 2015. Bali Indonesia: Procedia Manufacturing Elsevier.\n[15]\tMoayyedian, M., K. Abhary, and R. Marian. Improved gate system for scrap reduction in injection molding processes. in 2nd International Materials, Industrial, and Manufacturing Engineering Conference, MIMEC2015. 2015. Bali Indonesia: Procedia Manufacturing Elsevier.\n[16]\tMoayyedian, M., K. Abhary, and R. Marian, Gate design and filling process analysis of the cavity in injection molding process. Advances in Manufacturing: p. 1-11."]}

This study aims to develop an efficient rule for scheduling robotic flexible assembly cells (RFACs). The proposed scheduling rule is called fuzzy sequencing rule (FSR) which is constructed by combining different input variables: processing time, due date, batch size and number of required assembly stations, using the fuzzy logic (FL) technique. Two independent performance measures are considered: makespan and maximum of tardiness. Simulation software named SIMPROCESS is used to examine the performance of FSR compared with existing scheduling rules. Simulation results show that the proposed rule outperforms the common scheduling rules. Refereed/Peer-reviewed

In this article, a new stochastic production scheduling model for Virtual computer-integrated manufacturing (VCIM) systems is developed. This is an integrated VCIM production scheduling model with uncertainties, in which two sub-problems, namely agent selection and collaborative shipment scheduling, are fully integrated together to explore the opportunity for reducing the overall cost of products in a VCIM system. First, an explicit mathematical formulation of the proposed model is presented and then an innovative method to solve the problem, based on Monte Carlo simulation and optimisation solver, is proposed. Next, a comprehensive case study is provided to demonstrate the effectiveness of the proposed model. Finally, several aspects of the proposed model and optimisation solution method are discussed. Refereed/Peer-reviewed

This paper presents a new cross sectional shape of the runner system in the mold design of the injection molding process. The aim of the new geometry is to reduce scrap, cycle time and ease the ejection of runner system from mold tools. An elliptical cross sectional shape of runner with different ratios was proposed for two circular flat plates with thickness 1 mm. Finite element method (FEM) is employed in Solid Works Plastic for simulation of the injected part. Short shot defect in the plastic part during the injection molding process is analyzed by Solid Works Plastic to validate the new proposed geometry. An experimental study of the injection molding process of polypropylene circular flat plates is conducted for the new geometry. The input machine parameters selected are filling time, melt temperature,mold temperature, pressure holding time, and pure cooling time. The research outcomes show no short shot defect associated with the new geometry and also significant 25 and 2.5 % reduction in scrap and cooling time respectively compared to round cross sections. Reduction in contact surface of the runner system with mold walls improved the ease of ejection of runner system out of the cavity as well. The contribution of this study is to design a new geometry of a cold runner system to reduce scrap, cycle time and also provide easy ejection of runner system in the injection molding. Refereed/Peer-reviewed

This paper presents a new geometry of an edge gate in the injection molding process called modified edge gate. The corners of current edge gate result in the turbulence of molten plastic into the cavity which leads to internal and external defects. The aim of the new geometry is to reduce the internal and external defects of injected part such as sink marks and short shot. The easier de-gating analysis for the new geometry to decrease the visible blemish of the injected part was proposed in this study. A modified edge gate was proposed for two circular flat plates with thickness 1 mm. Finite element method (FEM) was used along with SolidWorks plastic. Internal and external defects of the injected part were analyzed via SolidWorks plastic. To validate the new geometry, an experimental study was implemented for two polypropylene (PP) circular flat plates with modified edge gate to confirm the simulation results. The contribution of this research is to modify the geometry of current edge gate by removing the corners of rectangular edge gate which leads to the reduction of scrap. The new geometry presents easier degating of gate and less visible blemish of the injected part. A smooth flow of molten plastic into the cavities reduces the internal and external defects. The outcome of this study demonstrates filling of the cavities with no short shot. In addition, no sink marks were detected with modified edge gate. Finally, the modified edge gate has less visible blemish on the injected part in comparison with current edge gate. Refereed/Peer-reviewed

This paper describes a three-dimensional (3D) printing machine with two nozzles that start from different locations and cover all task locations in a short span of time. Many studies have developed algorithms for reducing the total path distance of single-nozzle 3D printers. The present research proposes a hybrid methodology that can balance workloads by equalising task allocations and nozzle path lengths for both nozzles simultaneously. This multiagent task allocation approach can increase the productivity of microfactories that use dual-nozzle 3D printers to fabricate multilayer printed circuit boards. The robustness and solution quality of the method are evaluated using the standard very large-scale integration benchmark data-sets. Refereed/Peer-reviewed

This paper attempts to formalise, solve and optimise (S/O) the Assembly Sequence Planning Problem (ASPP), a large scale, highly constrained combinatorial problem. Due to the complexity of the subject and the number of related matters to be considered/adapted/solved prior to S/O the ASPP, the paper is split in two, self-contained, parts: Part I—Automatic Generation of Feasible Assembly Sequences and Part II—optimisation of assembly sequences using Genetic Algorithms. The first part deals with formalising the ASPP—modelling and representation issues—and generating feasible assembly sequences (solving the ASPP). The second part is concerned with the optimisation of the ASPP and will present in detail the Genetic Algorithm designed to optimise it, the genetic operators that compose the algorithm and the definition of the fitness function (optimisation function). The ASPP is considered here as a full-scale, unabridged problem.