De-manufacturing systems

open3noDe-Manufacturing Systems allow implementing optimized End-Of-Life strategies and are necessary to support a sustainable and competitive Manufacturing/De-Manufacturing integrated paradigm. However, available technologies, management methods and business models present several limitations that make landfill and, at a lower extent, materials recycling, the most diffused End-Of-Life practices. To overcome these limitations, this paper proposes an integrated multi-disciplinary research framework addressing single technologies improvement, system integration and business model coherency. The main challenges and research opportunities are presented that can boost the development of sustainable De-manufacturing Systems at industrial level.Colledani, Marcello; Copani, Giacomo; Tolio, TullioColledani, Marcello; Copani, Giacomo; Tolio, TULLIO ANTONIO MARI

An automated approach to reuse machining knowledge through 3D – CNN based classification of voxelized geometric features

The enhanced digitalization in the manufacturing sector is claimed to facilitate the generation or the use of the existing process data incorporating the production variations and offers a significant increase in the productivity and efficiency of a system. Moreover, manufacturing companies possess substantial knowledge while designing a product and manufacturing procedures. The primary requirement is to link and organize all the information sources related to the operation design and production. This research is concerned with the reuse of machining knowledge for existing and new parts having similarities in geometric features and operational conditions. The proposed methodology starts by extracting each machining operation's geometric information and cutting parameters using industrial part programs in the numerical control (NC) simulator VERICUT. The removed material between two consecutive operations is obtained through mesh comparison in the simulator to analyze the feature interactions. A deep learning approach based on 3D convolutional neural networks (CNN) is applied to classify similar geometries to reuse the process design knowledge by creating a library of operations. The proposed approach is implemented on actual machining data, and the results demonstrate the effectiveness of the proposed solution. The obtained knowledge clusters in the operations library assist in making propositions related to operational parameters for similar geometric features during the process planning phase reducing the planning and designing time of operations

Standardizing the Process Information for Machining Operations Through Self-Contained Structures

A mechanical product is manufactured through multiple processes and procedures. The process information is coded in a part program, and a large amount of unstructured information comes from the shop floor. This results in the loss of logic formulated for the creation of a code. Moreover, it is impossible to track the modifications carried out during these processes. Thus, the unavailability of appropriate and standard knowledge of part processing leads to the situation where the information must be recreated every time a similar part is manufactured, hence, increasing the process planning time. One solution is to divide it into two steps: first, by fetching the information and coding it in a standardized structure; second saving it in a suitable form, facilitating in improving the efficiency and effectiveness of process design for available parts as well as anticipating the new parts. This was achieved by using the previous information related to the process combined with the one obtained from the shop floor. The proposed work concerns capturing the unstructured information from the existing part programs and regaining it using process simulation (VERICUT). Through the extraction of theoretical and graphical geometric data, the interactions between the operations were analyzed. The operational knowledge in this work includes: origin, feed-rate, rotating speed of the tool, rapid movement, cutting tool, material knowledge, and some geometric information of the process. The proposed approach based on simulations and mathematical programming logic is a way to improve flexibility at process and system level by formalizing the available operational knowledge. To illustrate the applicability of the proposed approach, a case study was carried out on a real industrial part program

Multi-robot spot-welding cells: An integrated approach to cell design and motion planning

The necessity to manage several vehicle models on the same robotized assembly cell has made the cell design and the robot off-line motion planning two fundamental activities. Industrial practice and state-of-the-art methods focus on the technical issues of each activity, but no integrated approach has been yet proposed, resulting in a lack of optimality for the final cell configuration. The paper introduces a formalization of the whole process and proposes a heuristic multi-stage method for the identification of the optimal combination of cell design choices and motion planning. The proposed architecture is depicted through a real case for welding application

Multi-robot spot-welding cell design: Problem formalization and proposed architecture

The multi-robot cell design for car-body spot welding is faced by industry as a sequence of tasks, where researches are focused on issues of the problem as a whole. In authors’ knowledge, none work in literature have suggested any formalization for the complete process. This paper tries to bridges the gap proposing coherent process formalization, and presenting a corresponding innovative architecture for the automatic optimal cell design. Specifically, the formalization involves the identification and allocation of the resources in terms of a set of decisional variables (e.g. robot model/positioning/number, welding gun models/allocation/number, welding point allocation etc.); then, the design optimization process minimizes the investment costs granting the cycle time. The multi-loop optimization architecture integrates both new algorithms and existent procedures from different fields. Test-bed showing its feasibility is reported

Validation of an extended approach to multi-robot cell design and motion planning

According to both industrial practice and literature, multi-robot cell design and robot motion planning for vehicle spot welding are two sequential activities, managed by different functional units through different software tools. Due to this sequential computation, the whole process suffers from inherent inefficiency. In this work, a new methodology is proposed, that overcomes the above inefficiency through the simultaneous resolution of design and motion planning problems. Specifically, three mathematical models were introduced that (i) select and positions the resources, (ii) allocate the tasks to the resources and (iii) identify a coordinated robot motion plan. Based on the proposed methodology, we built three ad-hoc cases with the goal to highlight the relations between design, motion planning and environment complexity. These cases could be taken as reference cases so on. Moreover, results on an industrial case are presented

CIRP Encyclopedia of Production Engineering

This high quality reference work has been written and reviewed by members of The International Academy for Production Engineering, also known as CIRP. This Academy is recognized worldwide to represent the highest standards in research on production engineering, which includes design, optimization, control, management of processes, machines, and systems. One key concept behind this Encyclopedia is that apart from covering fundamental concepts in the field of production engineering, it also closely follows recent developments and emerging concepts. In particular this renewed print edition covers a wide range of new topical entries such as Hybrid Processes, High Performance Grinding, Biomimetic Design, Cold Spray, Sheet-bulk Metal Forming, Ecodesign, Cyber Physical System, Nano Technology, or Geometrical Product Specification. The second edition also comprises reviewed entries from the first version, which have been updated to reflect new standards or developments. The target audience primarily comprises researchers, engineers, managers, graduate students, and many others whose day-to-day work gravitates around production engineering technologies in the global market

Design and motion planning of body-in-white assembly cells

This paper proposes a method for the automatic and simultaneous identification of the body-in-white assembly cell design and motion plan. The method solution is based on an iterative algorithm that looks for a global optimum by iteratively identifying the optimum of three sub-problems. These sub-problems concern system layout design and motion planning for single and multi-robot systems, while collision detection is addressed. The sub-problems are handled through ad-hoc developed Mixed Integer Programming (MIP) models. The proposed solution overcomes the limitations of the current design and motion plan approaches. In fact, the design of body-in-white assembly cell and the robot motion planning are two time-expensive and interconnected activities, up to now generally managed from different human operators. The resolution of these two activities as non-interrelated could lead to an increase of the engineer-to-order time and a reduction of the solution quality. Thus, a test bed is described in order to prove the applicability of the approach