Finite Element Modeling of Selective Laser Melting 316L Stainless Steel Parts for Evaluating the Mechanical Properties

Selective laser melting (SLM) is an additive manufacturing technique in which complex parts can be fabricated directly by melting layers of powder from a CAD model. SLM has a wide range of application in biomedicine and other engineering areas and it has a series of advantages over traditional processing techniques. A large number of variables including laser power, scanning speed, scanning line spacing, layer thickness, material based input parameters, etc. have a considerable effect on SLM process materials. The interaction between these parameters is not completely studied. Limited studies on balling effect in SLM, densifications under different processing conditions, and laser re-melting, have been conducted that involved microstructural investigation. Grain boundaries are amongst the most important microstructural properties in polycrystalline materials with a significant effect on the fracture and plastic deformation. In SLM samples, in addition to the grain boundaries, the microstructure has another set of connecting surfaces between the melt pools. In this study, a computational framework is developed to model the mechanical response of SLM processed materials by considering both the grain boundaries and melt pool boundaries in the material. To this end, a 3D finite element model is developed to investigate the effect of various microstructural properties including the grains size, melt pools size, and pool connectivity on the macroscopic mechanical response of the SLM manufactured materials. A conventional microstructural model for studying polycrystalline materials is modified to incorporate the effect of connecting melt pools beside the grain boundaries. In this model, individual melt pools are approximated as overlapped cylinders each containing several grains and grain boundaries, which are modeled to be attached together by the cohesive zone method. This method has been used in modeling adhesives, bonded interfaces, gaskets, and rock fracture. A traction-separation description of the interface is used as the constitutive response of this model. Anisotropic elasticity and crystal plasticity are used as constitutive laws for the material inside the grains. For the experimental verification, stainless steel 316L flat dog bone samples are fabricated by SLM and tested in tension. During fabrication, the power of laser is constant, and the scan speed is changed to study the effect of fabrication parameters on the mechanical properties of the parts and to compare the result with the finite element model.

Effects of Laser Shock Peening on the Wear and Degradation Behaviors of Magnesium Alloys

In this study, we investigated the mechanical properties of AZ31B Mg alloy before and after laser shock peening (LSP). The hardness of the AZ31B Mg alloy increased from 57 HV to 69 HV after LSP. The yield strength increased from 128 MPa to 152 MPa. Wear resistance was significantly improved after LSP. Immersion testing showed that LSP did not significantly increase the element release and weight loss in simulated body fluid. We have demonstrated that LSP is an effective way to improve the mechanical properties of the AZ31B Mg alloy.

Managing Complexity in Manufacturing Changeovers: A Sustainable Manufacturing-Oriented Approach and the Application Case Study

Increasing manufacturing complexity continues to be one of the most significant challenges facing the manufacturing industry today. Due to these rapid changes in manufacturing systems, one of the most important factors affecting production is recognized as the frequent production setup or changeovers, consequently affecting the overall production lead times and competitiveness of the company. Developing responsive production setup and process capability is increasingly important as product ranges and varieties in manufacturing companies are growing rapidly and, at the same time, production business models are operating more towards being customer-oriented. Furthermore, although different conventional methods have been used to manage complexity in production changeovers, sustainability and competitiveness development in a manufacturing company needs to be scientifically addressed by managing manufacturing complexity. In this paper, a sustainable manufacturing-oriented approach is presented in mind of managing manufacturing changeover complexities. A case study is carried out specifically concerning changeover complexity in a pharmaceutical company, aiming at minimizing complexities in production changeover and waste, increasing plant flexibility and productivity, and ultimately the sustainable competitiveness of the company in managing manufacturing changes.

Step Ring Based 3D Path Planning via GPU Simulation for Subtractive 3D Printing

In this paper, both software model visualization with path simulation and associated machining product are produced based on the step ring based 3-axis path planning to demo model-driven graphics processing unit (GPU) feature in tool path planning and 3D image model classification by GPU simulation. Subtractive 3D printing (i.e., 3D machining) is represented as integration between 3D printing modeling and CNC machining via GPU simulated software. Path planning is applied through material surface removal visualization in high resolution and 3D path simulation via ring selective path planning based on accessibility of path through pattern selection. First, the step ring selects critical features to reconstruct computer aided design (CAD) design model as STL (stereolithography) voxel, and then local optimization is attained within interested ring area for time and energy saving of GPU volume generation as compared to global all automatic path planning with longer latency. The reconstructed CAD model comes from an original sample (GATech buzz) with 2D image information. CAD model for optimization and validation is adopted to sustain manufacturing reproduction based on system simulation feedback. To avoid collision with the produced path from retraction path, we pick adaptive ring path generation and prediction in each planning iteration, which may also minimize material removal. Moreover, we did partition analysis and g-code optimization for large scale model and high density volume data. Image classification and grid analysis based on adaptive 3D tree depth are proposed for multi-level set partition of the model to define no cutting zones. After that, accessibility map is computed based on accessibility space for rotational angular space of path orientation to compare step ring based pass planning verses global all path planning. Feature analysis via central processing unit (CPU) or GPU processor for GPU map computation contributes to high performance computing and cloud computing potential through parallel computing application of subtractive 3D printing in the future.

Modeling Evolution of Microstructures Beneath Topographically Textured Surfaces Produced Using Shear Based Material Removal

Tool chatter is envisaged as a technique to create undulations on fabricated biomedical components. Herein, a-priori designed topographies were fabricated using modulate assisted machining of oxygen free high conductivity copper. Subsequently, underpinnings of microstructure evolution in this machining process were characterized using electron back scattered diffraction based orientation imaging microscopy. These underpinnings were related to the unsteady mechanical states present during modulated assisted machining, this numerically modeled using data obtained from simpler machining configurations. In this manner, relationships between final microstructural states and the underlying mechanics were found. Finally, these results were discussed in the context of unsteady mechanics present during tool chatter, it was shown that statistically predictable microstructural outcomes result during tool chatter.

A Comparison Between Porous NiTi and Ti-6Al-4V Fixation Hardware on Bone Remodeling After a Reconstruction Surgery

The aim of this article is to investigate the effect of two different fixation hardware materials on bone remodeling after a mandibular reconstruction surgery and to restore the mandible’s function, healthy appearance, mastication, swallowing, breathing, and speech. The hypothesis is that using fixation hardware with stiffness close to that of the surrounding bone will result in a more successful healing process in the mandible bone. The finite element model includes the material properties and forces of the cancellous bone, cortical bone, ligaments, muscles, and teeth. The reconstruction surgery is modeled by including the fixation hardware and the grafted bone. In the sectioned mandible, to best mimic the geometry of the mandible, two single barrel grafts are placed at the top of each other to form a double barrel graft set. Two different materials were used as the mandibular fixation parts, stiff Ti-6Al-4V, and porous superelastic Nickel-Titanium (NiTi) alloys. A comparison of these two alloys demonstrates that using porous NiTi alloy as the fixation part results in a faster healing pace. Furthermore, the density distribution in the mandibular bone after the healing process is more similar to the normal mandible density distribution. The simulations results indicate that the porous superelastic NiTi fixation hardware transfers and distributes the existing forces on the mandible bone more favorably. The probability of stress shielding and/or stress concentration decrease. This type of fixation hardware, therefore, is more appropriate for mandible bone reconstruction surgery.

Implementing the ISO 15746 Standard for Chemical Process Optimization

This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization — and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times — each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.

3D Printing in Cloud Manufacturing: Model and Platform Design

Although 3D printing has attracted remarkable attention from both industry and academia society, still only a relatively small number of people have access to required 3D printers and know how to use them. One of the challenges is that how to fill the gap between the unbalanced supply of various 3D printing capabilities and the customized demands from geographically distributed customers. The integration of 3D printing into cloud manufacturing may promote the development of future smart networks of virtual 3D printing cloud, and allow a new service-oriented 3D printing business model to achieve mass customization. This paper presents a primary 3D printing cloud model and an advanced 3D printing cloud model, and analyzes the 3D printing service delivery paradigms in the models. Further, the paper proposes a 3D printing cloud platform architecture design to support the advanced model. The proposed advanced 3D printing cloud model as well as the architecture design can provide a reference for the development of various 3D printing clouds.

Knowledge-Based Production Planning Within the Reference Planning Process Supporting Manufacturing Change Management

The manufacturing of a product takes place in several partial steps and these mostly in different locations to save tax or to use the best providers. Therefore, in the era of Internet of Things (IoT) and modern Intelligent Production Environments (IPE) are going to be inevitably based on a cloud-based repository and distributed architecture to make data and information accessible everywhere as well as development processes and knowledge available for worldwide cooperation. Semantic approaches for knowledge representation and management as well as knowledge sharing, access, and re-use can support Collaborative Adaptive Production Process Planning (CAPP) in a flexible, efficient, and effective way. Thus, semantic representations of such CAPP knowledge integrated into a machine readable process formalization is a key enabling factor for sharing such knowledge in cloud-based knowledge repositories supporting CAPP scenarios as required for e.g., Small and Medium Enterprises (SMEs). When such contributors work together on a product component production planning, they exchange component production and manufacturing change information between different planning subsystems which require, e.g., a standardized product-feature- and production-machine feature representation. These data exchanges are mostly based on applying the already established Standard for the Exchange of Product model data (STEP) for the computer-interpretable representation and exchange of product manufacturing information. Furthermore, the planning process can be supported by so-called Function Block (FB) based knowledge representation models, serving as a high-level planning-process knowledge-resource template. Web-based and at the same time Cloud-based tool suites, which are based on process-oriented semantic knowledge-representation methodologies, such as Process-oriented Knowledge-based Innovation Management (German: Wissens-basiertes Prozess-orientiertes Innovations Management, WPIM) can satisfy the needs of representing such planning processes and their knowledge resources. In this way, WPIM can be used to support the integration and management of distributed CAPP knowledge, as well as its access and re-use in Manufacturing Change Management (MCM) including Assembly-, Logistics and Layout Planning (ALLP). Therefore, also a collaborative planning and optimization for mass production in a machine readable and integrated representation is possible. On the other hand, that knowledge can be shared within a cloud-based semantic knowledge repository. To integrate all these functionalities, this paper introduces a new method, called Knowledge-based Production Planning (KPP) and outlines the advantages of integrating CAPP with Collaborative Manufacturing Change Management (CMCM). In this way, an enabling basis for achieving ALLP interoperability in Distributed Collaborative Manufacturing and Logistics will be demonstrated.

Roll-to-Roll Manufacturing System Modeling and Analysis by Stream of Variation Theory

A multistage system that consists of multiple stages for sequential operations to finish products is widely employed in modern manufacturing systems. Due to the characteristics of multistage systems, the product quality not only depends on operations in current stage but is also affected by operations in upstream stages. Most existing studies use Stream of Variation models to analyze error propagation and interactions among multiple stages in discrete manufacturing systems such as machining shops and assembly systems. In this paper, a multistage model based on the “Stream of Variation” concept is developed to investigate the tension propagation in a flexible material roll-to-roll manufacturing system. This modeling method uses a physical model coupled with a data-driven model to correlate the roller operation performance and product quality characteristics. Torque equilibrium analysis and Hooke’s law are employed for physical model and the censored regression model is used to explore unknown structures/parameters. A web unwinding process demonstrates the feasibility and prediction performance of the proposed model. The result shows that the proposed multistage model can serve as a virtual metrology method to increase system visibility, enhance health management capability and eventually improve product quality.