A big data-driven framework for sustainable and smart additive manufacturing

From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.     

Process energy analysis and optimization in selective laser sintering

Additive manufacturing (AM) processes are increasingly being used to manufacture complex precision parts for the automotive, aerospace and medical industries. One of the popular AM processes is the selective laser sintering (SLS) process which manufactures parts by sintering metallic, polymeric and ceramic powder under the effect of laser power. The laser energy expenditure of SLS process and its correlation to the geometry of the manufactured part and the SLS process parameters, however, have not received much attention from AM/SLS researchers. This paper presents a mathematical analysis of the laser energy required for manufacturing simple parts using the SLS process. The total energy expended is calculated as a function of the total area of sintering (TAS) using a convex hull based approach and is correlated to the part geometry, slice thickness and the build orientation. The TAS and laser energy are calculated for three sample parts and the results are provided in the paper. Finally, an optimization model is presented which computes the minimal TAS and energy required for manufacturing a part using the SLS process.     

The status,challenges, and future of additive manufacturing in engineering

Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a “positive feedback loop” effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the “print-it-all” paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration.     

Microfabrication and characterization of metal-embedded thin-film thermomechanical microsensors for applications in hostile manufacturing environments

Effective monitoring and diagnosis of manufacturing processes is of critical importance. If critical manufacturing process conditions are continuously monitored, problems can be detected and solved during the processing cycle. However, current technology still evidently lags behind practical needs. Microfabricated thin-film thermocouples and strain gauges are attractive for their small size and fast response. It is challenging to fabricate and embed these sensors into metallic components that are widely used in manufacturing. This paper investigates the fabrication, embedding, and characterization of metal embedded thin-film thermocouples and strain gauges. The materials (dielectric and metallic) constituting a complete microsensor were characterized and optimized. The results obtained from characterization and optimization of materials are presented and discussed. Thin-film thermocouples on stainless steel substrates (before and after embedding) were calibrated to elevated temperatures. The behavior of thin-film strain gauges was also studied. The metal embedded sensors demonstrated good accuracy, sensitivity, and linearity that matched the performance of commercial thermocouples and strain gauges well. The metal embedded microsensors are promising for in situ monitoring in hostile manufacturing environments.     

In-situ point cloud fusion for layer-wise monitoring of additive manufacturing

Additive manufacturing (AM) has received an increasing attention in the manufacturing sector, owing to its high-level design freedom and enhanced capability to produce parts with complex geometries. With advances in AM technologies, the role of AM has been shifting from rapid prototyping to viable production-worthy manufacturing of functional parts. However, AM processes are highly inconsistent, and the lack of quality assurance significantly hampers the broader adoption of AM. Most existing techniques for AM online monitoring focus on the detection of conspicuous defects, such as under-fills and cracks. They are limited in their ability to detect layer surface variations induced by miniature process shifts. The objective of this study is to develop a new layer-wise monitoring framework for AM quality assurance based on in-situ point cloud fusion. Specifically, online 3D structured-light scanning is used to capture the surface morphology from each printed layer. The collected point cloud is partitioned, and the morphological patterns in local regions are delineated with a new affinity measure to evaluate the conformity to the reference. A deep cascade model is further introduced to leverage the local affinities for the identification of abnormal patterns on the printed layers. Finally, a statistical control chart is constructed for process monitoring and the identification of miniature shifts. Simulation and real-world case studies using the fused filament fabrication (FFF) process are conducted, and experimental results have demonstrated the effectiveness of the developed framework. It has a great potential to be implemented in diverse AM processes with a wide variety of materials for mission-critical applications.     

JSTAMP/NV在多工位级进模冲压仿真中的应用

级进模作为一种复杂、精密的冲压模具,具有高效率、高精度和高寿命的优点,广泛应用于各个行业的冲压生产中。随着技术的发展,对级进模的成形质量要求越来越高,但传统的级进模设计中主要依靠经验,在制造过程需要反复修模,产品制造周期长。本文简述了级进模设计的特点,并对多工位级进模冲压成形数值模拟的方法及操作流程进行了探讨。最后,以某汽车结构件的多工位级进冲压为例,应用JSTAMP/NV软件对其成形过程进行数值模拟,并将该仿真结果与试模结果进行比较,结果表明仿真结果与试模结果吻合良好。     

Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing

Environmental impact assessment of design and manufacturing decisions have received significant attention in the recent years. Researchers have not only focused on industrial waste minimization and chemical substitution in processes or products, but also on the effect of product design decisions on the environment during the manufacturing, in-use and end-of-life stages of the product. This research investigates the applicability of Bayesian decision networks to study the impact of design decisions on the life cycle performance, including environmental friendliness, of a product. Bayesian decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. A framework for integrated analysis of the product life cycle is presented. We discuss the specification of domain models for wide range of processes, such as manufacturing, recycling and disposal, an action model, and an utility model.     

机床产品制造系统能效的最优控制

本文结合机床产品制造系统的能量流特性,研究机床产品制造系统能效的最优控制.首先,利用无线传感器网络,构建制造系统能效感知网络,并设计了网络能量高效的通信协议,实时获取制造系统的能效数据.进而,利用能效感知数据,分别从单机设备局部优化与综合资源全局优化两方面,设计能效优化控制算法.根据单机设备任意两工步间空载能耗特性,给出单机设备空载能效最优控制模型.同时,建立以缩短生产周期、减少机器空转时间、提高产品合格率为优化目标的综合生产资源能效多目标优化方案.考虑到所论综合资源能效优化问题是离散组合优化问题,本文提出了文化基因支配排序粒子群算法进行求解,并采用层次分析(analysis hierarchy process,AHP)决策方法从Pareto解集中选取最优综合能效的优化策略.最后,利用实例与仿真相结合的方法,验证了文中所提方法的有效性.     

Applications of additive manufacturing (AM) in sustainable energy generation and battle against COVID-19 pandemic: The knowledge evolution of 3D printing

Sustainable and cleaner manufacturing systems have found broad applications in industrial processes, especially aerospace, automotive and power generation. Conventional manufacturing methods are highly unsustainable regarding carbon emissions, energy consumption, material wastage, costly shipment and complex supply management. Besides, during global COVID-19 pandemic, advanced fabrication and management strategies were extremely required to fulfill the shortfall of basic and medical emergency supplies. Three-dimensional printing (3DP) reduces global energy consumption and CO₂ emissions related to industrial manufacturing. Various renewable energy harvesting mechanisms utilizing solar, wind, tidal and human potential have been fabricated through additive manufacturing. 3D printing aided the manufacturing companies in combating the deficiencies of medical healthcare devices for patients and professionals globally. In this regard, 3D printed medical face shields, respiratory masks, personal protective equipment, PLA-based recyclable air filtration masks, additively manufactured ideal tissue models and new information technology (IT) based rapid manufacturing are some significant contributions of 3DP. Furthermore, a bibliometric study of 3D printing research was conducted in CiteSpace. The most influential keywords and latest research frontiers were found and the 3DP knowledge was categorized into 10 diverse research themes. The potential challenges incurred by AM industry during the pandemic were categorized in terms of design, safety, manufacturing, certification and legal issues. Significantly, this study highlights the versatile role of 3DP in battle against COVID-19 pandemic and provides up-to-date research frontiers, leading the readers to focus on the current hurdles encountered by AM industry, henceforth conduct further investigations to enhance 3DP technology.     

微电网中混合储能系统的规划运行一体化配置方法

目前针对微电网中储能容量配置的经济优化算法中,大多都是直接以储能系统的全寿命周期成本作为目标函数.然而微电网作为一个整体,不同的能量调度方案会导致不同的储能配置需求,因此微电网的运行目标也会间接影响到储能系统的全寿命周期成本.本文提出的规划运行一体化配置方法,是从微电网的整体经济效益出发,将储能系统全寿命周期成本的直接和间接影响因素都考虑在内,建立了计及微电网经济运行的储能全寿命周期成本复合模型.通过算例验证以及与已有方法优化结果的对比分析,说明了所提方法在提升微电网整体经济效益上的有效性,可以为评估微电网中混合储能系统的配置效果提供更全面的参考依据.     

Multi-source data analytics for AM energy consumption prediction

The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches.     

协同创新设计在实体造型中的应用*

敏捷制造是设计制造业为了应对日益加剧的市场竞争而提出的一个制造理念,通过增强对产品的设计、制造、后期销售各个阶段的管理来增强企业的竞争力。鉴于产品的设计阶段在其整个生命周期的关键地位,从两个方面对以往的设计模式加以改进,从而提高设计速度。提取目标产品的关键特征,利用进化计算对其进化,产生多个具体模型,为设计者提供初步的设计思路;多个设计者同时对同一目标展开协同设计,加快设计进度。     

Understanding life cycle social impacts in manufacturing: A processed-based approach

Developing sustainable products and processes is growing in importance due to increasing regulation, consumer interest, access to information, and competitive forces. In order to adequately evaluate the sustainability of products and processes, there is a need to consider the impacts from all three pillars of sustainability – society, environment, and economics. There are substantial challenges to identifying and understanding the social impacts associated with manufacturing activities. This paper provides a framework for characterizing the social impacts of manufacturing throughout the life cycle of a product or process. Social impacts occur on various scales in manufacturing, from the level of a unit process to the level of the enterprise. Additionally, manufacturing activities impact consumers, communities, and larger political/spatial realms. This paper identifies key characteristics of social impacts associated with manufacturing that should be considered to more effectively address the social dimension of sustainability for products and processes. Examples involving a typical manufacturing process – welding – are presented to illustrate the utility of the framework.     

A survey on the methods and tools of concurrent new product development and agile manufacturing

Companies in either manufacturing or servicing have to be restructured or re-organized in order to overcome with challenges of the 21st century in which customers are not only satisfied but also delighted. In this competitive environment, organizations should use a flexible, adaptive and responsive paradigm that can be entitled by a unique term: agile manufacturing (AM). An AM system is able to develop a variety of product at low cost and in a short time period. For this, it has some of useful enabling technologies and physical tools. Among these, concurrent engineering (CE) is a systematic approach to the integrated, concurrent design of product and their related processes, including manufacture and support. It is then a useful and beneficial approach to reduce the development time and manufacturing cost, while simultaneously improving the quality of a product in order to better respond to the customer expectations. The aim of this study is to underline the synergistic impact of new product development (NPD) and CE, (which can be called CNPD), and to survey their methods and tools in association with the AM.     

An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform

Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.     

An additive manufacturing filter for topology optimization of print-ready designs

Additive manufacturing (AM) offers exciting opportunities to manufacture parts of unprecedented complexity. Topology optimization is essential to fully exploit this capability. However, AM processes have specific limitations as well. When these are not considered during design optimization, modifications are generally needed in post-processing, which add costs and reduce the optimized performance. This paper presents a filter that incorporates the main characteristics of a generic AM process, and that can easily be included in conventional density-based topology optimization procedures. Use of this filter ensures that optimized designs comply with typical geometrical AM restrictions. Its performance is illustrated on compliance minimization problems, and a 2D Matlab implementation is provided.     

Elements in a new sustainable industrial culture Environmental assessment in product development

In the last few years the environmental focus in the manufacturing industry has shifted from the manufacturing processes to the products themselves, as these are accountable for the environmental impacts in all life cycle phases. The paper describes for three industrial cases how a newly developed LCA methodology can assist the product developer in development of more environmentally friendly products. Finally, common experience gained will be discussed.     

Combined optimization of part topology,support structure layout and build orientation for additive manufacturing

Additive manufacturing (AM) enables the fabrication of parts of unprecedented complexity. Dedicated topology optimization approaches, that account for specific AM restrictions, are instrumental in fully exploiting this capability. In popular powder-bed-based AM processes, the critical overhang angle of downward facing surfaces limits printability of parts. This can be addressed by changing build orientation, part adaptation, or addition of sacrificial support structures. Thus far, each of these measures have been studied separately and applied sequentially, which leads to suboptimal solutions or excessive computation cost. This paper presents and studies, based on 2D test problems, an approach enabling simultaneous optimization of part geometry, support layout and build orientation. This allows designers to find a rational tradeoff between manufacturing cost and part performance. The relative computational cost of the approach is modest, and in numerical tests it consistently obtains high quality solutions.     

An object-oriented constraints-based system for concurrent product development

This research work aims to develop an intelligent constraint-based system that enables designers to consider at the early stages of the design process all activities associated with product’s life cycle. One of the most important aspects of these activities is the evaluation and optimisation of manufacturing processes that require various type of information from the different aspects of product’s life cycle. This research article discusses the development of a prototype system for manufacturing process optimisation using a combination of both mathematical methods and constraint-programming techniques. This approach enables designers to evaluate and optimise feasible manufacturing processes in a consistent manner as early as possible during the design process. This helps in avoiding unexpected design iterations that wastage a great amount of time and effort, leading to longer lead-time. The development process has passed through the five major stages: Firstly, an intelligent constraint-based design system for concurrent product and process design has been developed. Secondly, a manufacturing process optimisation module has been constructed. Thirdly, the product features, processes, cost, time and constraints to be used for carrying out various design tasks has been represented in the format of constraints, frames, objects, and rules. Fourthly, the process optimisation and evaluation rules for the selection of feasible processes for complex features, and finally, the information management system that ensures consistency in information exchange and decision making activities have been developed.