Search

Due to the intricate mechanism of Abbe error's impact on spatial accuracy, the accumulated Abbe error in the traditional spatial accuracy model is difficult to identify and eliminate, resulting in reduced modeling accuracy and limiting accuracy improvement. This paper proposes a data-driven spatial accuracy modeling approach for machine tools under the influence of Abbe error, using a three-axis coupling measurement optical path to directly measure the comprehensive spatial accuracy data of the machine tool containing Abbe error. Firstly, to effectively identify Abbe error in the comprehensive spatial accuracy, an Abbe error quantization function is established, which analyzes its formation mechanism in the measurement process to eliminate Abbe error in the machine tool's spatial accuracy data. Furthermore, to address the issue of limited data samples after eliminating Abbe error, data samples are extended based on the degradation mechanism of machine tool spatial accuracy at different coordinate positions, resulting in a high-precision spatial error model for machine tools. Finally, the experiment is conducted on a three-axis CNC machine tool, with a model accuracy of over 95%. The example application verification demonstrates that the developed model scheme is feasible and effective. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is the core link with prognostic and health management (PHM). Compared with the stable service environment, the degradation trend of key parts of electromechanical equipment under dynamic service environment has greater uncertainty. Dynamic changes of the service environment in the subsequent work tasks can result in inaccurate or even invalid current prediction results. To this end, considering that tasks can be known in advance and affect the service environment, a novel RUL prediction method is proposed which updates the predicting results as the tasks change based on dynamic correlation of planned tasks, service environment, degradation characteristics, and RUL. Firstly, the influence of the changes in service environment on the RUL of key parts is analyzed, and a task-driven classification and matching method of dynamic service environment is presented. Secondly, the key service environment variables affecting RUL of parts are screened employing grey relation analysis (GRA) and Pearson correlation coefficient. Thirdly, a task-driven deep long-short time memory (DLSTM) neural network prediction model under dynamic service environment is established, and a regularization method for optimizing the DLSTM model is given to improve the prediction accuracy. Finally, the RUL prediction of a machine tool spindle is taken as an example to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Design for remanufacturing process (DFRP) plays a key role in implementing remanufacturing because it directly affects the performance recovery of the End-of-Life (EoL) products. Since the used parts of EoL products have various failure forms and defects, which make it hard to rapidly generate remanufacturing process scheme to satisfy the performance demand of the remanufactured products. Moreover, remanufacturing process parameters are prone to conflicts during remanufacturing processes, often leading to unsatisfactory remanufacturing processes. To accurately generate remanufacturing process scheme and solve the conflicts, an integrated design method for remanufacturing processes based on performance demand is proposed, which reuses the historical remanufacturing process data to generate the remanufacturing process scheme. Firstly, the Kansei Engineering (KE) and Quality Functional Development (QFD) are applied to analyze the performance demand data and map the demands to the engineering features. Then, Back Propagation Neural Network (BPNN) is applied to inversely generate the remanufacturing process scheme rapidly to satisfy the performance demands by reusing the historical remanufacturing process data. Meanwhile, Theory of Constraint (TOC) and TRIZ are used to identify and solve the conflicts of the remanufacturing process for the remanufacturing process scheme optimization. Finally, the DFRP of an EoL guide rail is taken as an example to demonstrate the effectiveness of the proposed method, the result of which shows the design method can quickly and efficiently generate the remanufacturing process scheme. [ABSTRACT FROM AUTHOR]

Eco-efficiency has been proved as one of the powerful indicators in link with environment management and economic output. As a core procedure of remanufacturing activities, upgrading the eco-efficiency of remanufacturing process planning is of great significance for maximizing the circular economic advantages of remanufacturing. With the increasing of individual differences in used parts, the remanufacturing process planning is becoming more complicated. In this paper, a knowledge-based method for remanufacturing process planning is proposed as part of the efforts in upgrading eco-efficiency which also aims to improve the efficiency of process planning and realize the inheritance and evolvability of the process planning knowledge. Based on the identification of basic attribute characteristics and damage characteristics of used parts, a similarity calculation method is presented to match the most relevant historical remanufacturing process planning knowledge from the existing knowledge base, so as to design the feasible initial process plan set. For eco-efficiency upgrading, a remanufacturing process planning model is proposed to obtain the process plan with the optimal economic and environmental performance, and then the knowledge base can be updated and expanded. Finally, a case study of remanufacturing a used worm gear is demonstrated to validate the proposed method. [ABSTRACT FROM AUTHOR]

Remanufacturing represents an activity that often offers significant profit opportunities and can provide substantial environmental benefits. Process plan selection is one of the most important operational decisions in remanufacturing because it directly affects the success rate of remanufacturing as well as cost and quality. There is a need for a decision-making aid which can optimize process plan selection in the presence of wide-ranging alternatives. In response to this need, a decision method has been developed that combines the strengths of quality function deployment (QFD) and fuzzy linear regression. In this method, quality function deployment is employed to not only frame the problem but also to establish relationships between remanufacturing performance and process quality characteristics of interest. Fuzzy linear regression is used to determine the functional relationships between remanufacturing performance and process quality characteristics and obtain the optimal solution. If desired, the most suitable process plan alternative may be determined. To assess the usefulness and practicality of the proposed method, an illustrative example is given and the results are discussed. [ABSTRACT FROM AUTHOR]

The environmental impacts of the manufacturing stage of the product life cycle are resource consumption and waste emissions. The development of an appropriate assessment method for evaluating the environmental performance of a manufacturing process, or an entire process plan, is one of the keys to realize a successful sustainable manufacturing strategy. This paper describes a new method for the environmental evaluation of a manufacturing process plan, where actual values for various environmental measures for the different operations in the plan are used to form an impact matrix. If desired, scores for each process, each environmental measure, and for the entire plan may be obtained by applying weights obtained from the analytical hierarchy process. The method has been encoded in software (Manufacturing Environmental Performance Evaluation), which provides insights into how manufacturing decisions affect the environment. A case study is used to illustrate how the assessment method may be applied and to demonstrate its applicability. [ABSTRACT FROM AUTHOR]

End-of-life (EOL) strategies selection of used parts is important to ensure efficient resource utilization. However, the diversity and uncertainty of remaining life characteristics of used parts make the EOL strategies selection more complicated. To select appropriate EOL strategies scientifically and effectively, this paper proposes a decision-making approach for EOL strategies selection, considering remaining useful life (RUL) estimation and reliability analysis. By comparing the reliabilities of a used part at the end of its actual useful life and estimated RUL with its reliability threshold, the optimal EOL strategy of used part is determined. Furthermore, an EOL strategies evaluation support system (EOLSESS) is developed for easier application of the presented method. Finally, an illustrative example is provided to demonstrate the feasibility and validity of this method. [ABSTRACT FROM AUTHOR]

A blank dimension is not only a premise of blank design and manufacture, but also plays a significant role to decide the machining process and the energy consumption of the machining process. The design of an appropriate blank dimension is important to ensure efficient energy usage. However, consideration of a workpiece machining process and energy consumption during the machining process in the blank dimension design phase is often neglected. To design a blank dimension scientifically and effectively, this paper proposes an optimization approach for blank dimension design, by considering energy consumption, machine cost, and process time. The three objectives are affected by three variables, cutting depth, feed rate, and cutting speed. The non-dominated sorting genetic algorithm-II (NSGA-II) algorithm was used to solve multi-objective optimization problem, and blank dimensions could be calculated from the cutting depth of minimum energy consumption and workpiece dimension. The results indicated that the proposed approach is effective in realizing energy savings due to the appropriate blank dimension design. The optimization approach not only offers some insights for designing a new blank dimension, but also provides a theoretical basis for existing blank dimension selection. [ABSTRACT FROM AUTHOR]