Search

With the wide application of new electric vehicle (EV) battery in various industrial fields, it is important to establish a systematic intelligent battery recycling system that can be used to find out the resource wastes and environmental impacts for the retired EV battery. By combining the disassembly and echelon utilization of EV battery recycling in the re-manufacturing fields, human-robot collaboration (HRC) disassembly method can be used to solve many huge challenges about the efficiency and safety of retired EV battery recycling. In order to find out the common problems in the human-robot collaboration disassembly process of EV battery recycling, a dynamic disassembly process optimization method based on Multi-Agent Reinforcement Learning (MARL) algorithm is proposed. Furthermore, it is necessary to disassemble the EV battery disassembly task trajectory based on human-robot collaboration disassembly task in 2D planar, which can be used to acquire the optimal disassembly paths in the same disassembly planar combining the Q-learning algorithm. The disassembly task sequence can be completed through standard trajectory matching. Finally the feasibility of the method is verified by disassembly operations for a specific battery module case.

The modeling and simulation of cylindrical surfaces with consideration of form defects have led to considerable research outcomes in the field of Computer-Aided Tolerancing (CAT). However, further consideration of surface deformations caused by external forces still remains a challenge. To address this issue, this paper properly considers the form defects and surface deformations for tolerance analysis of cylindrical components. First, form defects are considered by modeling skin model shapes of cylindrical surfaces. Afterwards, the tight fit and loose fit of a pair of cylindrical surfaces are identified, and the simulation methods of their positioning are presented. Specifically, for tight fit situation, a k-d tree based Iterative Closest Point (ICP) algorithm is used, and for loose fit situation, the constrained registration approach is adopted. Moreover, a Conjugate Gradient-Fast Fourier Transform (CG-FFT) method is presented for the consideration of surface deformations. In addition, simulations of given examples are conducted, which show the considerable effects of form defects and surface deformations. The simulations may also help determine the best performance of the to-be-assembled cylindrical components.

Digital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.

Advanced manufacturing in the context of the digital thread has mainly focused on process information related to the integration of machining processes, tools, operations, and specific manufacturing parameters. However, no unified information model has been developed to represent specific manufacturing process in 3D printing. This work therefore proposes a STEP/STEP-NC-compliant process data model to define specific application objects and entities, to model manufacturing data relationships and constraints, and to standardize process parameters through extended 3D printing concepts in fused deposition modelling (FDM). This data model describes manufacturing layers, deposition operations and process parameters in the context of the digital thread. As a special feature, manufacturing layers provide geometric information to layered operations based on STEP/STEP-NC standards for additive manufacturing (AM). Deposition operations specify the definitions of print devices or headers, header paths, FDM technology and functions, and even deposition strategy. Process parameters mainly provide process information in the FDM process. Finally, a conformance testing process is developed, focussed on manufacturing layers and header paths and by means of information exchange.

Reverse engineering can be considered as the methodological process of analyzing and reconstructing a digital model of a physical asset. It has gained considerable interest with the advent of sophisticated sensors and data processing technologies hence becoming an important enabler for the product digital twin. However, while existing approaches to reverse engineering focus on the mere geometric reconstruction, this paper presents a novel paradigm called function-oriented surface reconstruction, that is capable of reconstructing the underlying part and surface function and thus outperforms existing methods. The applicability of the presented method is demonstrated through a case study of a gearbox.

An increasing interest in lattice structures is developing in various industrial sectors. These structures, composed of several cavities or voids, are used in a wide range of applications. They allow to lighten workpieces while maintaining good mechanical characteristics. Although these structures offer new design opportunities, they are difficult to manufacture because of their complex geometry and internal surfaces that are difficult to access. The special characteristics of lattice structures almost always require the use of additive manufacturing processes. By using lattice structures, new problems arise in additive manufacturing, such as lattice defects characterization and measurement issues. The objective of this study is to characterize the geometric defects of lattice structures obtained using L-PBF additive manufacturing process. Lattice structures exhibit not only common defects inherent to L-PBF process, but other geometric defects specific to their unit cell that require appropriate measurement systems and new processing techniques. This paper deals with a systematic approach to evaluate geometric defects obtained from Additive Manufacturing simulation software. First, lattice structures are generated using Rhino-Grasshoper visual programming capabilities. Then, virtual parts are built using Ansys Additive software. Registration methods are used thereafter to compare the simulated and the nominal models. The advantage of non-rigid methods such as CPD and BCPD for registration is highlighted. Finally, the obtained data can be used in the future to enhance the predictive modeling of geometric defects in L-PBF.

Over the past 70 years, product design has undergone many important changes due to the impact of contemporary digital technologies (i.e. digital design). To support digital design and information flow throughout the product lifecycle, the digital-driven technologies currently in use rely on the evolution of CAD and PLM systems to address new design and manufacturing challenges generated by the new era of 4.0 digital transformation. This paper will discuss the past and present coevolution and shortcomings within industrial organisations, the digital technologies employed in the product development cycle and will illustrate the current challenges and future prospects of the digital thread for design.

Tolerance analysis aims to evaluate the functional requirement of mechanical assemblies in the design stage. As one of the typical tolerance analysis methods, the polytope model has shown great pot...

Performance analysis, which plays a key role in the design stage, is employed to estimate whether product performance can satisfy design requirements. In general, product performance is gained after parts are assembled; product performance is influenced by the position and orientation deviations (PODs) that occur in directions of the constrained degrees of freedom (DOFs) due to the surface deviations of mating-surfaces. Furthermore, PODs are uncertain because the surface deviations as well as positions in the unconstrained DOF directions can vary randomly. Thus, predicting the consequences of uncertain PODs on product performance is key for performance analysis. Considering that planes are extensively used in assemblies, this study aims to propose a statistical approach to analyze the uncertain PODs of non-ideal planes. A modeling method from the perspective of manufacturing errors is employed to describe the uncertain surface deviations. A method for computing the uncertain PODs based on the progressive adjustment of coordinate systems is proposed. The maximum PODs that characterize the most unfavorable assembly situation are determined as evaluation indicators. Finally, the effectiveness of the presented approach is verified by a case study. Because both the effects of uncertain surface deviations and uncertain positions on PODs can be considered, the approach is expected to help predict the practical effects of uncertain PODs on product performance accurately during the design stage.

International audience; The geometrical quality of AM products is an indispensable concern when conducting Design for Additive Manufacturing (DfAM), since it dominates the geometrical consistency between the manufactured samples and the design intent and has an impact on the functionality of the products. Therefore, effective prediction of the geometric deviations prior to the mass production will provide useful information for designers in order for design optimization. Data-driven methods open up new possibilities to gain high-fidelity prediction based on existing observable data. In this paper, a Convolutional Neural Network based deep learning method is proposed which enables the prediction of deviations for different shapes and process settings. A data augmentation technique is also introduced to generate samples for network training based on a small number of available data. Through a case study, it's demonstrated that the trained network manages to accurately predict the geometric deviations of shapes manufactured with varied size and process parameter settings. The predicted deviations could substantially benefit DfAM in evaluation of geometrical consistency. Moreover, reverse compensation can be accordingly applied to the CAD model prior to the manufacturing process, thus increasing the geometrical accuracy of the manufactured parts. c 2020 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the CIRP Design Conference 2020.

Polytope-based tolerance analysis has shown a great potential in the Computer Aided Tolerancing (CAT) field. Previous works have considered form defects into this model, which overcomes the drawback of considering toleranced features as surfaces of perfect form. To enrich the capability and applicability of conventional polytope model, this paper further integrates the combined effects of form defects and surface deformations into polytope-based tolerance analysis. Specifically, form defects are considered based on the concept of Skin Model Shapes and surface deformations are calculated based on a Conjugate Gradient Fast Fourier Transform (CG-FFT) method. Thereafter, the polytope-based tolerance analysis is applied to an over-constrained mechanism. With the consideration of non-ideal surface contact status, this new polytope model provides more realistic and accurate analysis results.

Geometrical features are fundamental elements for the fulfilment of the functional product requirements. Therefore, their inevitable deviations caused by manufacturing inaccuracies are the main issues in the field of tolerance management. To close the gap between physical part description and nominal model representation, the framework of discrete geometrical representatives via Skin Model Shapes has been developed and continuously enhanced. As the underlying concepts are based on final virtual parts, consistent inferences to every single manufacturing step are not fully investigated in literature. This paper presents the development of Skin Model Shapes based on the virtual representation of intermediate part outcome (in-process geometry) and the integration of machining processes. Thus, integration and simulation of different kinds of deviations are enabled at each manufacturing stage considering error accumulation due to machine specific inaccuracies, such as tool movement and fixturing. Furthermore, the effect of inaccuracies on the final conformance result can be quantified at each manufacturing stage. A case study is presented to illustrate the entire approach and to demonstrate the validity of the proposed methods.

Geometric tolerance characteristics of metal additive manufactured (AM) parts play a significant role in ensuring the part functionality. In such cases, prior estimation of geometric tolerances, i.e. geometric dimensioning and tolerancing (GD&T) characteristics, can prove vital to reduce part rejection and to minimize material wastage and cost. This article presents a framework to estimate geometric tolerances in laser powder bed fusion (LPBF) processes. For a given geometry, skin model shapes are generated based on material shrinkage and thermo-mechanical simulation. Samples from skin model shapes are utilized for geometric tolerance estimation. A case study is presented to validate the developed framework and demonstrate its applicability in metal AM.

We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD objects. This benchmark is meant to help both method developers and those who want to identify the best performing tools. The Fit4CAD dataset is composed by 225 high quality point clouds, each of which has been obtained by sampling a CAD object. The way these elements were created by using existing platforms and datasets makes the benchmark easily expandable. The dataset is already split into a training set and a test set. To assess performance and accuracy of the different primitive fitting methods, various measures are defined. To demonstrate the effective use of Fit4CAD, we have tested it on two methods belonging to two different categories of approaches to the primitive fitting problem: a clustering method based on a primitive growing framework and a parametric method based on the Hough transform.