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Selective laser melting (SLM) is generally characterized by the brief laser-powder interaction time, highly localized heat intensity and input distributions, steep temperature gradients and high cooling rate. It was admitted that the mechanical properties of as-built SLMed components exhibit significantly anisotropic due to different heat history and microstructures in different directions. Post-processing of additively manufactured parts is generally considered an essential operation due to poor surface quality and dimensional accuracy. In this paper, comprehensive research is conducted about the influence of material anisotropy of additively manufactured Ti-6Al-4V alloys on machining performance in ultra-precision micromachining. The machining performance of SLMed Ti-6Al-4V alloys was derived from two cutting directions (parallel/perpendicular to the laser scanning direction), two machining surfaces (top/front surface) and three laser scanning strategies (0°-line scanning, 67.5°/90°- rotation scanning). Surface topography, roughness, microstructure and microhardness were assessed to characterize the material anisotropy of SLMed titanium alloy. The machining results showed that the anisotropic mechanical properties and microstructure feature of the Ti-6Al-4V alloys are the fundamental reasons for the anisotropic machining performance in terms of cutting force and surface roughness. The machining properties of SLMed Ti-6Al-4V alloys are affected by the microstructure, machining surfaces, cutting directions and cutting parameters. Both the magnitude and fluctuation of dynamic cutting force produced on the front surfaces are obviously larger than those generated on the top surfaces. Significant improvement in surface roughness ranging from Sa = 80–100 nm could be achieved along the laser scanning direction for 0°-line SLMed Ti-6Al-4V sample when cutting depth was from 1 to 15 μm at vc = 50 mm/min. In addition, some typical SLMed defects appear on the machined surface and produced chips. This research is applicable to assist manufacturers in choosing the appropriate machining method for SLMed components in ultra-precision machining.

The effects of trace MoSi2 addition on the microstructure evolution, mechanical properties and fracture behavior of Ti–6Al–4V wall via cold metal transfer additive manufacturing (CMTAM) were investigated. After the addition of trace MoSi2 powder, trace (β-Ti, Mo) phase and SiO2 particles were obtained and partly prior-β→α’ phase transformation was suppressed. Compared with the as-built Ti–6Al–4V wall, the prior-β grains changed from columnar to the equiaxed or near-equiaxed after the addition of trace MoSi2. The tensile analysis result revealed that the addition of trace MoSi2 could improve the average strain while reduce anisotropy. All specimens showed dimples corresponding to ductile failure mode. The possible thermal cycles for the trace-MoSi2-addition specimens during the CMTAM process were analyzed.

3D printing is one of the most innovative technologies in the current era, while 3D bioprinting is revolutionizing the medical technology industry. Bioprinting technology could help overcome the limitations of the current tissue engineering methods, including the problem of longer waiting times for treatment (especially with organ transplants). While fighting infectious diseases had been the main focus of medicine in the past, dealing with the consequences of a predominantly ageing population will be the priority in the future and bioprinting is a promising technology to tackle this challenge effectively. Bioprinting will not only cater the needs of ageing population but also in the field of paediatrics, where the bioprinted tissue or organ should possess the capability to grow with the patient. As researchers around the world are working on 3D bioprinting of tissues and organs, companies are burgeoning all over, making and marketing new bioprinters. While the research and commercialization are moving at such a rapid pace, the issues surrounding the technology, in terms of ethics, policies, regulations and social acceptance, are not addressed in commensurate. Identifying the ELSA (Ethical Legal and Social Aspects) concerns of this technology at an early stage is not only part of our social responsibility but also in the interest of the future of the technology itself. This paper reviews and foresees these challenges with pragmatism, thereby creating awareness to the researchers and policy makers and to urge a positive course of action in the foreseeable future. The significance of this work will be to address a broad audience, associated with this technology, from scientists to businessmen, engineers to clinicians, laymen to lawmakers. A ‘complete’ policy approach for this technology is recommended rather than a ‘piecemeal’ approach of the various constituents of this technology. An effective course of action will be to setup a multi-disciplinary international panel to work on the policy framework, which will look in to both ‘hard’ and ‘soft’ impacts of 3D bioprinting, the associated ethical challenges, legal measures including patenting and effective controls to prevent the misuse, as well as the social aspects encompassing the cultural and religious differences which accounts for the success of this technology. Setting up national level panels to assess the risk-benefit analysis, taking into consideration the cultural and religious view of its population and other legal and social aspects, might be a good starting point.

Soaring energy price, increasing environmental concerns, and stringent legislations have forced enterprises to reduce energy consumption in discrete parts manufacturing. In order to achieve this reduction, accurate estimation of energy consumption at the manufacturing planning stage is needed. In this paper, a new model for energy consumption prediction in machining processes is proposed and validated in experiments. Using the established cutting force model, cutting power at the tool tip is obtained analytically. Further, the relationship between total power consumed by the machine tool and cutting power at the tool tip is empirically characterized. To demonstrate the new model, this hybrid approach has been applied for machine power prediction in a slot milling process. A comparison study has also been conducted between the proposed model and existing models under various cutting conditions. The results show that the proposed model could achieve better prediction accuracy than other models. Moreover, a major limitation of the existing models is successfully addressed. By considering cutting power at the tool tip, the proposed hybrid model could reflect the nature of material removal and is able to provide valuable information regarding the impact of specific cutting parameters on power consumption. This knowledge is important in evaluating operation parameters, process plans, and even production schedule in terms of energy efficiency.

This study investigated the effects of dendrite axis and fusion boundary (FB) on the stress corrosion cracking (SCC) of 308 L/304 L welds by using an alternating current potential drop (ACPD) technique to measure the SCC growth rates in a simulated boiling water reactor environment. The results show that the ferrite/austenite interfaces are more susceptible to SCC in the SS 308 L welds. Furthermore, the angle between the applied load and the dendrite axis affects the SCC growth rate, and as this angle approached 90°, the SCC crack growth rate increases. The SCC cracks in the heat affected zone (HAZ) are mainly intergranular and some cracks propagate transgranularly. Furthermore, the primary crack in the HAZ propagates along the FB; when reaching FB, some secondary cracks are arrested at the FB. The residual strain caused by weld shrinkage is the crucial factor for the increase in SCC susceptibility in HAZ.

Abrasive grit blasting process is widely used in many industries. Cleanliness level of the blasted surface is a critical criterion in abrasive blasting process. Modelling of abrasive blasting process is important to understand the effect of blasting parameters, such as moving speed of the blasting gun, on blasting productivity and quality. Based on the assumption that blasting process is the kinematic energy exchange between the abrasive grits and the removed material from workpiece surface, cleanliness level is modelled by the total energy consumed on the elemental blasted surface for given blasting parameters. Furthermore, as the moving speed affects the energy distribution on the surface, the effect of moving speed of the blasting gun on cleanliness level is analysed. According to the required surface cleanliness level (For example, the required surface quality in shipyard is SA 2.5), the effective productivity is calculated, which can guide the user to select the proper moving speed to improve the productivity and reduce the wastage of the abrasive grits. Finally, the proposed model is validated by experiments, and the result shows a good agreement between the predicted and measured results.

Surface blasting operation, for many years, have been an essential step in surface maintenance of a ship hull. Ships in preparation of fresh coat of paint requires its surface to be cleared of contaminants to allow good adherence of paints. In many shipyards, the operation is carried out manually by workers standing at elevated platform dozens meters high with boom lifts or rigging platforms. The working environment is extremely hazardous as other workers are also exposed to air pollutants and sound hazard due to grits impacting the surface. This paper proposes an inexpensive semi-automated grit-blasting system mountable on the boom lift or other platforms that can be moved up and down to perform blasting operations in an enclosed chamber to prevent dispersion of grits and dusts into the atmosphere. The system contains a mechanical blasting module, which performs the blasting operation, and a vision module, which is the ‘eye’ of the system. The vision module can detect the rusted area and implement adaptive path planning for higher blasting efficiency and less grits wastage. To continue the current work, the vision module will be improved to be applicable on surfaces with any color and defects with degree of rusts. The system is also versatile to be used for other cleaning operations, such as water jet cleaning.

Nerve guidance conduits (NGCs) are tubular tissue engineering scaffolds used for nerve regeneration. The poor mechanical properties and porosity have always compromised their performances for guiding and supporting axonal growth. Therefore, in order to improve the properties of NGCs, the computational design approach was adopted to investigate the effects of different NGC structural features on their various properties, and finally, design an ideal NGC with mechanical properties matching human nerves and high porosity and permeability. Three common NGC designs, namely hollow luminal, multichannel, and microgrooved, were chosen in this study. Simulations were conducted to study the mechanical properties and permeability. The results show that pore size is the most influential structural feature for NGC tensile modulus. Multichannel NGCs have higher mechanical strength but lower permeability compared to other designs. Square pores lead to higher permeability but lower mechanical strength than circular pores. The study finally selected an optimized hollow luminal NGC with a porosity of 71% and a tensile modulus of 8 MPa to achieve multiple design requirements. The use of computational design and optimization was shown to be promising in future NGC design and nerve tissue engineering research.

Lattice structures are broadly used in lightweight structure designs and multifunctional applications. Especially, with the unprecedented capabilities of additive manufacturing (AM) technologies and computational optimization methods, design of nonuniform lattice structures has recently attracted great research interests. To eliminate constraints of the common “ground structure approaches” (GSAs), a novel topology optimization-based method is proposed in this paper. Particularly, the structural wall thickness in the proposed design method was set as uniform for better manufacturability. As a solution to carry out the optimized material distribution for the lattice structure, geometrical size of each unit cell was set as design variable. The relative density model, which can be obtained from the solid isotropic microstructure with penalization (SIMP)-based topology optimization method, was mapped into a nonuniform lattice structure with different size cells. Finite element analysis (FEA)-based homogenization method was applied to obtain the mechanical properties of these different size gradient unit cells. With similar mechanical properties, elements with different “relative density” were translated into unit cells with different size. Consequently, the common topology optimization result can be mapped into a nonuniform lattice structure. This proposed method was computationally and experimentally validated by two different load-support design cases. Taking advantage of the changeable surface-to-volume ratio through manipulating the cell size, this method was also applied to design a heat sink with optimum heat dissipation efficiency. Most importantly, this design method provides a new perspective to design nonuniform lattice structures with enhanced functionality and manufacturability.

Cellular structures are broadly applicable to lightweight design and multifunctional applications. Especially, with unprecedented fabrication freedom provided by additive manufacturing (AM), design and optimization of nonuniform cellular structures have recently attracted great research interests. Topology optimization is one of the most powerful tools to obtain the optimized material distribution, and much research have been conducted to optimize cellular structures with the help of this optimization technique. In general, the optimized cellular structure is generated based on a predefined ground structure, and thickness of each strut is then decided based on the optimization result. However, many existing studies did not consider the constraints of AM processes, such as some generated struts may be too thin to be manufactured. Besides, only load support structure was considered in these studies. Other applications, such as heat dissipation or energy absorption, were rarely researched. In this paper, a novel cellular structure design method, which considered both functionality and manufacturability, is proposed. Different from other methods, wall thickness of the structure was set as a constant. To get the optimized material distribution, variable cell sizes were applied. Because of uniform wall thickness, the smaller the unit cell is, the higher its volume fraction will be. By mapping small unit cells to high density area and large cell to low density area, the final optimized cellular structure can be generated. In addition, because smaller unit cells have higher surface-to-volume ratio, this method can also be applied to solve heat transfer problem. Two examples, minimum compliance design of a cantilever beam and maximum heat dissipation efficiency design of a CPU heat sink, were conducted to validate the proposed method.

Stent implantation at a highly curved artery has always been a challenge, considering the relatively high chance of in-stent restenosis (ISR) caused by severe straightening effect and high strain energy over the vessel wall. In this paper, a novel optimization based design method was proposed to manipulate the deformation behavior of the common ring-and-link stent. By changing the location of the connection point between rings and links, traditional ring-and-link structure was modified to achiever tunable Poisson's ratio (PR). With the nonuniform cellular structure design method proposed in a previous study, PR distribution of the stent structure was optimized to achieve the desired curvature. As a result, the obtained stent structure with nonuniform PR could perfectly fit into the curved artery after expansion, without causing any obvious vessel straightening. To validate the proposed method, two different vessel models were introduced. Firstly, a short vessel with a constant curvature was set as the design objective, and both numerical and experimental tests were conducted. Further, a patient-specific vessel was applied. Both test results showed that optimized stents would cause much smaller vessel straightening. Moreover, vessels stented by the optimized structures had much lower stress concentration and strain energy. All those properties will decrease the possibility of ISR significantly.

Design documents and design project footprints accumulated by corporate information technology systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Most of the existing design information extraction systems focus on either organizing design documents for efficient retrieval or extracting relevant product information for product optimization. Different from traditional systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. Particularly different from conventional techniques that deal with timestamps or event logs only, a new process mining approach that is able to directly process textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is analyzed to extract multifaceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multiyear multidisciplinary design project.

Drop-on-demand (DOD) printing is widely used in bioprinting for tissue engineering because of little damage to cell viability and cost-effectiveness. However, satellite droplets may be generated during printing, deviating cells from the desired position and affecting printing position accuracy. Current control on cell injection in DOD printing is primarily based on trial-and-error process, which is time-consuming and inflexible. In this paper, a novel machine learning technology based on Learning-based Cell Injection Control (LCIC) approach is demonstrated for effective DOD printing control while eliminating satellite droplets automatically. The LCIC approach includes a specific computational fluid dynamics (CFD) simulation model of piezoelectric DOD print-head considering inverse piezoelectric effect, which is used instead of repetitive experiments to collect data, and a multilayer perceptron (MLP) network trained by simulation data based on artificial neural network algorithm, using the well-known classification performance of MLP to optimize DOD printing parameters automatically. The test accuracy of the LCIC method was 90%. With the validation of LCIC method by experiments, satellite droplets from piezoelectric DOD printing are reduced significantly, improving the printing efficiency drastically to satisfy requirements of manufacturing precision for printing complex artificial tissues. The LCIC method can be further used to optimize the structure of DOD print-head and cell behaviors.

Several attempts have been made to fabricate esophageal tissue engineering scaffolds. However, most of these scaffolds possess randomly oriented fibres and uncontrollable pore sizes. In order to mimic the native esophageal tissue structure, electro-hydrodynamic jetting (e-jetting) was used in this study to fabricate scaffolds with aligned fibres and controlled pore size. A hydrophilic additive, Pluronic F127 (F127), was blended with polycaprolactone (PCL) to improve the wettability of the scaffolds and hence the cell adhesion. PCL/F127 composite scaffolds with different weight ratios (0–12%) were fabricated. The wettability, phase composition, and the mechanical properties of the fabricated scaffolds were investigated. The results show that the e-jetted scaffolds have controllable fibres orientated in two perpendicular directions, which are similar to the human esophagus structure and suitable pore size for cell infiltration. In addition, the scaffolds with 8% F127 exhibited better wettability (contact angle of 14°) and an ultimate tensile strength (1.2 MPa) that mimics the native esophageal tissue. Furthermore, primary human esophageal fibroblasts were seeded on the e-jetted scaffolds. PCL/F127 scaffolds showed enhanced cell proliferation and expression of the vascular endothelial growth factor (VEGF) compared to pristine PCL scaffolds (1.5- and 25.8- fold increase, respectively; P

3D bioprinting is a pioneering technology that enables fabrication of biomimetic, multiscale, multi-cellular tissues with highly complex tissue microenvironment, intricate cytoarchitecture, structure-function hierarchy, and tissue-specific compositional and mechanical heterogeneity. Given the huge demand for organ transplantation, coupled with limited organ donors, bioprinting is a potential technology that could solve this crisis of organ shortage by fabrication of fully-functional whole organs. Though organ bioprinting is a far-fetched goal, there has been a considerable and commendable progress in the field of bioprinting that could be used as transplantable tissues in regenerative medicine. This paper presents a first-time review of 3D bioprinting in regenerative medicine, where the current status and contemporary issues of 3D bioprinting pertaining to the eleven organ systems of the human body including skeletal, muscular, nervous, lymphatic, endocrine, reproductive, integumentary, respiratory, digestive, urinary, and circulatory systems were critically reviewed. The implications of 3D bioprinting in drug discovery, development, and delivery systems are also briefly discussed, in terms of in vitro drug testing models, and personalized medicine. While there is a substantial progress in the field of bioprinting in the recent past, there is still a long way to go to fully realize the translational potential of this technology. Computational studies for study of tissue growth or tissue fusion post-printing, improving the scalability of this technology to fabricate human-scale tissues, development of hybrid systems with integration of different bioprinting modalities, formulation of new bioinks with tuneable mechanical and rheological properties, mechanobiological studies on cell-bioink interaction, 4D bioprinting with smart (stimuli-responsive) hydrogels, and addressing the ethical, social, and regulatory issues concerning bioprinting are potential futuristic focus areas that would aid in successful clinical translation of this technology.

Mechanical systems with adaptable functions are versatile in functionalities and compact in physical structure than ordinary ones, hence are useful in many industrial and agricultural applications. One aspect of design analysis for these systems is the analysis of functional coupling degree among the adaptable functions, because this is a measurement of how compact and material-effective (thus cost-effective) the systems are. Existing work on functional coupling is mainly oriented to multiple-function systems rather than adaptable-function systems, where the former owes multiple functions without the need of adjustment or replacement of system components. The purpose of this paper is to investigate the methodologies for the evaluation of functional coupling and component importance of adaptable-function systems, so as to provide support for the development of this kind of mechanical systems. To this end, the function-component dependency is investigated first by exploiting the methods for design change propagation, whereby a change possibility matrix is proposed and calculated, and a function-component incidence matrix is developed. Based on the incidence matrix, the functional coupling degree is evaluated to measure the coupling between individual functions, and a comprehensive coupling degree is evaluated to measure the functional coupling of the entire system. These results are then used for the evaluation of component utilization and component importance. Finally, a design case of an adaptable-function juicing machine is studied to illustrate the proposed methods as well as the related concepts.

The slow strain rate tests (SSRT) at a nominal strain rate 1× 10−6/s were conducted to evaluate the mechanical properties of CF8A cast austenitic stainless steel (CASS) in air and high-temperature water environments. CF8A CASS is mainly composed of a matrix of γ phase dispersed with δ ferrites and is affected slightly by aging heat treatment. The hardness value of γ phase did not change with aging time, while that of δ ferrite increased with increasing the aging time. The SSRT results showed that a loss in elongation of a high δ-ferrite specimen was more significant than that of the low δ-ferrite specimens. The SCC paths of the low δ-ferrite specimen were along the links between δ ferrites dispersed in the matrix. For the high δ-ferrite specimen, most of the cracks were observed to propagate through the chromium depletion regions in the ferrite grains, which were preferentially oxidized during the SSR test in water. However, the elongation did not decrease further after aging time was longer than 2,400 h,...

As closed-loop production becomes an increasingly viable strategy for product life cycle management, cost modelling and analytical tools for closed-loop production systems will become increasingly important. Existing tools based on life cycle costing (LCC) methods are lacking an integrated life cycle perspective in their approach – a perspective that cannot be overlooked when modelling closed-loop production systems. In this paper, we aim to solve this lack of integrated life cycle approach by proposing the product structure-based integrated life cycle analysis (PSILA) technique. It allows us to streamline the process of modelling a closed-loop production system for a complex product based on its product structure. Through this technique, we developed a cost model of a closed-loop production system for a flat-panel (FPD) monitor case study. In this case study, the results simulated by the cost model show an increase in the economic performance when closed-loop production is adopted for the FPD monitor. Deeper analysis of the results in the case study indicate that in order to improve the economic performance of the closed-loop production system for the FPD monitor, the best strategy is not to merely focus on the reutilisation of materials. Instead, the option to remanufacture the FPD monitor for the secondary market should be explored to maximise the value reclaimed from the monitors in the EoL phase. The case study demonstrates that the cost model developed through the PSILA technique has the ability to function as a tool for analysing the economic performance of closed-loop production systems. More importantly, it has the ability to provide us with strategic insights on the cost feasibility of adopting closed-loop production strategies.

Sustainable product development has become an important consideration by many product manufacturers in recent years. Manufacturers are adopting the life cycle approach in the development of products by incorporating the End-of-Life stage considerations at the design stage. To achieve this, however, there is the need to fill up the knowledge gap between the End-of-Life stage and design stage. Tools and methodologies for Design for End-of Life are required to assist designers to design products with better End-of-Life performance. In this paper, a novel Design for End-of-Life methodology is proposed. The methodology captures, represents and analyses the knowledge from the End-of-Life stage into a product End-of-Life Index. The index enables designers to make informed decision on design alternatives for optimal product End-of-Life performance using information from the End-of-Life stage. A case study illustrating the application of this End-of-Life Index on a power tool is subsequently presented. From the discussion, it was shown that the index was able to help to assess the End-of-Life performance of the design and also the changes made subsequently. In essence, this work done here has been demonstrated to be of assistance to designer to adopt Design for End-of-Life.

This paper proposes concept to decide end-of-life (EoL) product recovery option, followed by methods to quantify product condition. A case study is presented using refrigerator crankshaft to illustrate the implication of product condition on product recovery decision making. The product condition comprises wear-out life of the product, change of dimension and cleanliness level. The advantage from the proposed concept is twofold - firstly, knowledge learned and embedded resources from EoL products able to get back to the product life cycle chain as a closed loop and, secondly, the most favourable EoL product recovery option can be made wisely. With thorough understanding of EoL product condition, it enables original equipment manufacturers (OEMs) to make quick and informed decision.

In tissue engineering, cell-laden scaffold has gradually replaced cell-less scaffold due to better biological performance. However, manual pipetting, the traditional cell seeding for cell-laden scaffold, leads to an imprecise and inhomogeneous cell distribution. As an alternative, micro-extrusion of cell-laden hydrogel achieves homogenous cell distribution, but causes high shear stress which is harmful to cells. To address this challenge, the objective of this study is to print cells on porous scaffold precisely without causing high shear stress to produce homogeneous cell-laden hybrid scaffold. Porous polycaprolactone scaffold fabricated through electro-hydrodynamic jetting was used as a representation. To improve scaffold hydrophilicity for better cell adhesion, 6% (w/w) Pluronic F127 was blended with polycaprolactone. HeLa cells, as a demonstration, were ejected on the scaffold fibers through piezoelectric inkjet printing. As a result, inkjet printing showed a more precise and homogeneous cell distribution and enhanced cell proliferation compared to manual pipetting (1.34- fold increase after 7 days). Furthermore, due to the low viscosity of cell solution, the average shear stress caused during inkjet printing was 1.79 kPa as opposed to 18 kPa of micro-extrusion, which is friendly to cells. In summary, through inkjet printing, homogeneous cell-laden hybrid scaffold could be fabricated with lower shear stress.

This paper investigates the feature sampling strategies for 3D partial shape retrieval using bag-of-words model. The SHREC 09’ parts query models [3] are tested for comparison. These parts models are obtained by cutting parts from complete models, which are different from range scans. Dense sampling and pyramid sampling are proposed to extract local salient features from the depth images of the 3D models. Bag-of-words model is used to represent of both of parts query and complete target models. The optimal sampling configurations for the proposed feature extraction strategies are obtained by comparing the retrieval accuracy using maximum histogram intersection distance (MHID). The results suggest that extracting more features does not guarantee better retrieval accuracy using the bag-of-words model. The feature sampling configurations also have significant impacts on the retrieval accuracy.

It is widely agreed that environmental issues, from climate change to energy and water scarcity, to over exploitation of natural resources, represent critical challenges for the society. Thereby, industry has progressively adopted lean manufacturing by reducing amount of energy and materials used in the production. However, manufacturer has limitation towards improving product efficiency without feedback from user as a close loop. Knowing the complexity of environmental issues and economy changes, this paper introduces a new system framework that takes the holistic approach by capturing information at end-of-life (EoL) and feeding back to design or manufacturing in making a greener product. With complete understanding of EoL product status, it enables OEMs to develop technology diversely and at the same time rapidly incorporating environmental concept in part of the product chain. For instance, the valuable knowledge learned in EoL product put forward a more efficient way for product maintenance by cutting down time, cost and negative impact to the environment. Moreover, a greater reliable product could be achieved by utilizing appropriate material or design in particular product. This resulted in improved product design as an inherent loop in product life cycle, therefore producing greener product for better reusability, remanufacturability and recyclability. Overall, efficient EoL product recovery system enables sustainable and competitive manufacturing, and thus drives the economy growth.

To ensure that the economic and environmental impact over the entire product life cycle is taken into consideration during the design process, an integrated life cycle approach to Design for Environment (DfE) must be taken. In this paper, we describe the application of the product structure-based methodology for integrated life cycle analysis as a DfE decision-support tool. While maintaining an integrated perspective of the product life cycle, this methodology modularises the product life cycle model to the individual parts of the product structure; thus, allowing product designers to analyse the parts independently from the rest of the product system and enabling them to systematically evaluate the design alternatives. Using a case study on a dive torchlight, we demonstrate how through our proposed methodology, design alternatives for the product can be evaluated based on the life cycle eco-efficiency.

This paper presents a discretised musculoskeletal multi-body spine model using the LifeMOD Biomechanics Modeller. This was obtained by refining spine segments in cervical, thoracic and lumbar regions into individual vertebra segments, using rotational joints representing the intervertebral discs, building various ligaments between vertebrae and implementing necessary lumbar muscles. To validate the model, two comparison studies were made with in vivo intradiscal pressure measurements of the L4-L5 disc as well as extension moments, axial force and shear force around L5-S1 obtained from spine models available in the literature. The results indicated that the present model is in good correlation with both cases and matches well with experimental data which found that the axial forces are in the range of 3929-4688 N and shear forces up to 650 N. This study provides a preliminary overview of our ongoing work towards building bio-fidelity discretised multi-body spine models for investigating various medical applications. These models can be useful for incorporation into design tools for wheelchairs or other seating systems which may require attention to ergonomics as well as assessing biomechanical behaviour between natural spines and spinal arthroplasty or spinal arthrodesis. Furthermore, these models can be combined with haptic-integrated graphic environments to help surgeons to examine kinematic behaviours of scoliotic spines and to propose possible surgical plans before spine correction operations.

The placement of micro-implants is a common but relatively new surgical procedure in clinical dentistry. This paper presents a haptics-based simulation framework for the pilot drilling of micro-implants surgery to train orthodontists to successfully perform this essential procedure by tactile sensation, without damaging tooth roots. A voxel-based approach was employed to model the inhomogeneous oral tissues. A preprocessing pipeline was designed to reduce imaging noise, smooth segmentation results and construct an anatomically correct oral model from patient-specific data. In order to provide a physically based haptic feedback, an analytical drilling force model based on metal cutting principles was developed and adapted for the voxel-based approach. To improve the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. A prototype system has been developed based on the proposed framework. Preliminary results show that, by using this framework, proper drilling force can be rendered at different tissue layers with reduced cycle time, while the visual display has also been enhanced.

The haptic playback system is important to the training of sensorimotor skills. With the intent for training medical students in surgery, a real-time incision training system with haptic playback is developed in this paper. This system has three modes. Mode I sets the standard cutting trajectory of the incision operation carried out by an expert. Mode II allows students to track the trajectory by haptic guidance to feel the expert's motion. Mode III lets students experience the force feeling of the expert by following the trajectory via the visual cue. To accomplish these tasks, an adjustment proportional-derivative (PD) controller and a cutting model with real-time haptic feedback are devised. The cutting model adopts the mesh adaption method for real-time simulations of progressive cutting operations. These devised methods have been configured with a haptic device PHANToM Desktop to validate the capability of the proposed training system.

In complex manufacturing processes, large amounts of process parameters are monitored and recorded, creating a high-dimensional and heterogonous data warehouse. In order to improve process yield and ensure product quality, comprehensive knowledge of the process should be acquired and critical features should be identified. However, in modern industry production, big data has become quite common; online monitoring and prediction is also usually required. Therefore, an effective feature selection algorithm for industry applications should be fast and robust. Traditional feature selection methods often fails to deal with such demands very well. In this paper, a modified approach for feature selection based on ReliefF is proposed for modelling and analysis of complex manufacturing processes, with improved speed and stability. Randomized k-d tree search is introduced to speed up the feature selection algorithm. The proposed method is also tested with two datasets from real industry process.

The increasing design documents created in the design process provide a useful source of process-oriented design information. Hence, the need for automated design information extraction using advanced text mining techniques is increasing. However, most of the existing text mining approaches have problems in mining design information in depth, which results in low efficiency in applying the discovered information to improve the design project. With the aim of extracting process-oriented design information from design documents in depth, this paper proposes a layered text mining approach that produces a hierarchical process model which captures the process behavior at the different level of details. Our approach consists of several interrelated algorithms, namely, a content-based document clustering algorithm, a hybrid named entity recognition (NER) algorithm and a frequency-based entity relationship detection method, which have been integrated into a system architecture for extracting design information from coarse-grained views to fine-grained specifications. To evaluate the performance of the proposed algorithms, experiments were conducted on an email archive that was collected from a real-life design project. The results showed an increase in the detection accuracy for the process-oriented information detection.

Battery/Ultracapacitor (UC) Hybrid Energy Storage Systems for Electric Vehicles have been proposed to increase battery cycle life and driving range. Existing works focus on power management strategies of the battery and UC, while energy management strategies are still relatively empirical. We propose a more rigorous method of setting the target UC energy level using a speed-dependent band. This allows the UC to contain sufficient energy for future accelerations, and have sufficient capacity to store energy from future regenerative braking. Furthermore, by adjusting the band height, the UC size in terms of energy stored can be reduced significantly. We show other works cannot achieve both goals simultaneously unless their UCs are sized larger (up to three times).

Digital prototypes (DPs) allow designers to communicate design concepts to users by rendering physical characteristics of the concepts. To enhance user understanding of the concepts, it is important that the users be able to make better estimates of the values of the characteristics. Correctness of the estimates can depend on two crucial factors: the ability of DPs to render the physical characteristics and the way the DPs are used to communicate the physical characteristics. However, little attention has been paid to the latter. A DP can be used in different ways, e.g., it can be projected on different backgrounds, or be manipulated using different input/output devices. Hence, it is important to identify an effective way of using DPs, via an assessment of the effectiveness of various ways. This paper introduces a methodology for evaluating the effectiveness of communication of physical characteristics to users using DPs. The methodology is used to assess the degree to which users can correctly and quickly estimate the values of the characteristics through interactions with DPs. Such assessments are then analyzed with statistical methods and hypothesis tests to reveal the effectiveness. To validate the proposed methodology, the size of hand-held electronic consumer products, such as smartphones, is considered in a case study. In the study, the effectiveness of two communication setups is evaluated. The same DP is used in both setups, while the environments and input devices are different. The experimental results show that the evaluated effectiveness can reflect how successful the setups are, and can help select the best way of using the DP, i.e., by providing a better environment, a better input device, or a combination of both.

Object proposal is essential for current state-of-the-art object detection pipelines. However, the existing proposal methods generally fail in producing results with satisfying localization accuracy. The case is even worse for small objects which however are quite common in practice. In this paper we propose a novel Scale-aware Pixel-wise Object Proposal (SPOP) network to tackle the challenges. The SPOP network can generate proposals with high recall rate and average best overlap (ABO), even for small objects. In particular, in order to improve the localization accuracy, a fully convolutional network is employed which predicts locations of object proposals for each pixel. The produced ensemble of pixel-wise object proposals enhances the chance of hitting the object significantly without incurring heavy extra computational cost. To solve the challenge of localizing objects at small scale, two localization networks which are specialized for localizing objects with different scales are introduced, following the divide-and-conquer philosophy. Location outputs of these two networks are then adaptively combined to generate the final proposals by a large-/small-size weighting network. Extensive evaluations on PASCAL VOC 2007 show the SPOP network is superior over the state-of-the-art models. The high-quality proposals from SPOP network also significantly improve the mean average precision (mAP) of object detection with Fast-RCNN framework. Finally, the SPOP network (trained on PASCAL VOC) shows great generalization performance when testing it on ILSVRC 2013 validation set., accepted by IEEE Transactions on Image Processing

This paper presents a novel approach to the 3D CAD model retrieval, whereby the 3D models are treated and matched as undirected graphs. While there is much success made in the matching of graphs based on their spectral decomposition, most of these approaches consider smooth surfaces and are not suitable for CAD models because of their complex topology and singular structure. In the proposed approach, the models are simplified based on the piecewise flat properties of the surfaces first, and a perturbed Laplacian spectrum approach is then applied to characterize the shape. These spectral values are used as samples for spectral distribution estimation. The perturbed spectral distributions of different models are then compared by their KL-divergence for model retrieval. The proposed approach is tested with models from known 3D CAD database for verification.

Based on compute analysis engineering (CAE), the performances of acoustic and fluent for muffler comprehensively were analyzed and optimized in the paper. First, aiming at the heavy exhaust noise of diesel engine, bench experiments of measuring independently were designed and carried out, the frequency spectrum of exhaust noise was achieved, combining with simulation analysis of the finite element, acoustic and fluid performances of the original muffler and respective muffling cavities were analyzed, whose shortage such as transmission loss, pressure loss and flow regeneration noise were achieved, the muffler would be re-designed by CAE. Simulation shows the performances of improved mufflers were better than the original one. Eventually, insertion loss of experiment for new muffler is more 10dB(A) above than the original one, and power loss met requirement.

For the relatively strong vibration caused by a three-wheeled motorcycle exhaust system and raised the level of the noise in the whole motorcycle, this paper combines the experimental modal analysis method and finite element analysis (FEA) method, establishes finite model of the exhaust system, finishes the experimental modal analysis and transient response analysis based on Hyperworks software and laboratory equipment, and obtains six natural frequencies and mode shapes. Comparison has been made between acceleration RMS changes before and after the mounting points changed with the transient excitations. At the end, the document also verifies better results after the improvement and found the basement of reducing noise in the next step.

The main sound source of motorcycle noise is engine exhaust noise in the paper. Installing muffler can effectively reduce the exhaust noise, in the other word, if the acoustic performance of muffler can be improved, exhaust noise of engine will effectively be suppressed. Based on the FEM, establishing the acoustic simulation model, the performance of muffler relates to acoustic is studied. Based on muffling theory, the optimal projects of the interior structure for muffler are proposed. Through doing experiments of insertion loss for muffler and the pass-by noise for motorcycle, these confirmed that the improved muffler can reduce the motorcycle’s noise.

Singapore is a diamond-shaped island with several surrounding smaller islets. It has a flat coastline with a land area of 710 km2 in 2009. With a highly urbanized city and limited land space, Singapore has been faced with problems of road congestion and rapid growth in car population. Electric vehicles (EVs) provide low emission urban transportation. Even taking into account the emissions from power plants needed to fuel EVs, the use of EVs still reduce carbon dioxide emissions significantly. From the energy aspect, EVs are efficient. EVs are promising alternative fuel vehicles that can reduce energy consumptions and carbon dioxide emissions in Singapore. A life cycle cost model was built to calculate life cycle costs of EVs and internal combustion engine cars in Singapore. It was found that EV is the most expensive car under current Green Vehicle Rebate scheme. The EV will be economically viable in Singapore if there is a breakthrough at batteries to cut EV prices.

Developing new generation wearable electronics with skin-like stretchability and conformability has become the research hotspot in many areas, such as bio-sensoring, communication and robotics. In this study, a novel design and manufacturing method was proposed to develop wearable electronics, which could perform a similar deformation as the target skin surface during the rotation of joint. Considering the nonuniform auxetic deformation behaviour of the skin around joint areas, the designed electronics should also have the same Poisson's ratio (PR) distribution. Re-entrant honeycomb structure was chosen to obtain tuneable PR, by changing the interior angel of each unit cell. By mapping unit cells with different PR to the target surface, the obtained structure would have the same mechanical property and deformation behaviour as the covered skin. Consequently, there would be little sheer stress at the connection areas between wearable electronics and human skin, which would surely increase the attachment stability. Besides, the conformability of the device could also be generated. PloyJet process was introduced to fabricate the flexible substrate, and silver ink was printed directly on the substrate with direct-ink-writing (DIW) technology. Conductive wire with 'wavy' structure was manufactured by this PolyJet-DIW process, and it had shown a much better stretchability than the common straight-line structure. Furthermore, wearable electronics that suitable for a person-specific elbow was developed. Both the conformability and conductivity were tested, and all results had shown much better performance than traditional wearable electronics with uniform structures.

In product design process, when dealing with technical problems or initiating a new design, R&D personnel would often turn to technical database to seek inspiration. The building of a database with such documents has not been systematically dealt with. In this paper, several issues on how to build up a product design database are investigated: input source, sampling scheme and quality control. A case study of building a database for robotic design is used to demonstrate the concept. It is an archive of more than 1500 relevant technical papers. A total of 16 graduates are employed as operators in the labeling process and subsequently the hypothesis tests are utilized to process the labeling results. To ensure this database quality, the labeling consistency of each operator and the understanding of each category are tested. With the use of statistical methods, this work proposes a feasible and practical way to create such a database for product design.

The matching of particular types of CAD models to existing physical models can provide invaluable support to the process of CAD design and reuse. To meet the demand for fast and robust algorithms to detect predefined models in database, an local invariant model matching approach is proposed in this paper. It first maps the 3D CAD model to 2D principal image plane by its first two principal components, and then finds affine invariant key points in the 2D image. The CAD model matching problem is implemented as key points matching. Experimental results show the proposed 3D model retrieval method performs fairly well in retrieving similar models from a database of 3D CAD models.

Platform-based product family has been proven an effective strategy to economically provide wide market coverage. However, two practical platform design issues need to be properly handled: (1) commonized components require over-design of features that drives capability waste and additional costs with regard to the lower end product variants, and (2) commonization pattern of components is constrained by couplings of the base platform architecture. This research attempts to present a generic approach for the optimal platform design that treats commonization of components in a hierarchic manner under hybrid modular architecture. Three major sections are included: (1) identification of hidden interface costs defined by the architectural couplings, (2) modeling of hierarchic sharing relationship of commonized components across product variants, and (3) configuration of the hierarchic components platform for the lowest total production cost. Finally, a family of cordless drills targeting a series of rated output torques is employed to demonstrate the proposed approach.

This paper presents a product structure model with a semantic representation technique that make the product structure extensible for developing product lifecycle management (PLM) systems that is flexible for make-to-order environment. In the make-to-order business context, each product could have a number of variants with slightly different constitutions to fulfill different customer requirements. All the variants of a family have common characteristics and each variant has its specific features. A master-variant pattern is proposed for building the product structure model to explicitly represent common characteristics and specific features of individual variants. The model is capable of enforcing the consistency of a family structure and its variant structure, supporting multiple product views, and facilitating the business processes. A semantic representation technique is developed that enables entity attributes to be defined and entities to be categorized in a neutral and semantic format. As a result, entity attributes and entity categorization can be redefined easily with its configurable capability for different requirements of the PLM systems. An XML-based language is developed for semantically representing entities and entity categories. A prototype as a proof-of-concept system is presented to illustrate the capability of the proposed extensible product structure model.

PurposeProduct configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.Design/methodology/approachGiven a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product components, as well as the correlation among product components. A configuration was developed which takes XML‐based requirement specification as input and bases on a constraint knowledge base to produce product configuration as output consisting of a list of selected components and the structure and topology of the product. Three modules are developed, namely product data modelling, configuration knowledge generation and product configuration generation module. The proposed approach is implemented in the configuration knowledge generation module. The configuration generation module realizes a resolution of constraint satisfaction problem to generate the output configuration.FindingsThe significance and effectiveness of the proposed approach is demonstrated by its incorporation in our configuration system prototype. A case study was conducted and experimental results show that the approach is promising in finding constraints with given sufficient data.Originality/valueNovel knowledge generation approach is proposed to assist constraint generation for Constraint‐based product configuration system.