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The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. [ABSTRACT FROM AUTHOR]

Based on some studies, there are many important parts of tool machines, all of which have some essential smaller-the-better-type quality characteristics. The six sigma quality index of the smaller-the-better type offers accurate measurement of the process yield and the six sigma quality level. In this paper, we first proposed a six sigma product index by integrating all evaluation indicators for products that contain several quality characteristics of the smaller-the-better type. Next, we derived the confidence interval of this six sigma product index and developed an evaluation model for product quality. When a product passes the evaluation of this model, not only can it be guaranteed that the product reaches the required quality level, but also a high rate of product yield can be ensured. In addition, we also created a product improvement testing model, which can avoid missing opportunities for improvement in the process to ensure improvement effects. This complete evaluation and improvement model is applicable to the entire machine tool industry chain. It can not only increase the product value of the machine tool industry chain but also decrease environmental pollution caused by rework or scrap, which is beneficial to companies to enhance their image of fulfilling social responsibilities. Apart from the above advantages, the model formed in this paper is based on confidence intervals, thereby reducing the chance of misjudgment resulting from sampling error. [ABSTRACT FROM AUTHOR]

In the present study, a new method is proposed to separate machining errors of a 5-axis machine tool with a double turntable. Moreover, a high-precision compensation model is established to calculate machining errors of thin-walled parts in a 5-axis machine tool. In this regard, a comprehensive error model of the S-shaped sample part considering geometric, thermal, and machining errors is established. Then based on the homogeneous transformation matrix, the geometric error models of translational axes and the A and C axes of the rotation axis are established. Accordingly, geometric errors of each motion axes are identified and measured. Meanwhile, the thermal error of the spindle is measured, and the thermal error model of the machine tool along X/Y/Z directions is established using the multiple linear regression method. Finally, the S-shaped sample is processed and the on-machine measurement of the sample surface is carried out. Based on the obtained machining error from the separation method, the distribution of the total surface error at different curvatures of the S-shaped sample is analyzed, and the distributions of geometric and thermal errors of the machine tool are obtained. It is found that when the geometric and thermal errors of the machine tool are compensated, the measured machining error of each point of the S-shaped sample can be reduced by 10–15 µm compared with that before compensation. [ABSTRACT FROM AUTHOR]

In order to improve the predictive ability of the error model of the five-axis machine tool, an indirect error measurement (IEM) method of the five-axis machine tool based on the sample test method was proposed, and the optimized design of the stepped axis sample was completed. By inversely calculating the spatial error model of the machine tool, the function analysis relationship between the sample machining error and different error terms of the machine tool was established, and then the dynamic errors of the machine tool in different machining states were obtained. The reliability and practical effects of the method was verified through experiments. The results showed that the proposed IEM method was in good agreement with the laser measurement method. The predictive ability of the error model of the five-axis machine tool can be improved by using the IEM method, thereby more machining accuracy of the machine tool will be ensured. [ABSTRACT FROM AUTHOR]

Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain. [ABSTRACT FROM AUTHOR]

[Display omitted] • GCN is embedded into LSTM and GRU to propose GCLSTM and GCGRU. • MPSTGCN realizes fusion and interaction of spatiotemporal feature. • TL-MPSTGCN realizes cross-domain and poor information prediction. • TL-MPSTGCN is embedded into thermal error service architecture. • Service architecture is designed based on cloud-edge collaboration. Thermal error, which has spatiotemporal behavior, severely reduces machining accuracy of high-accuracy machine tools and should be controlled in real time. The deep learning is used to establish spatiotemporal thermal error model with large-sample thermal information as input. But the acquisition of large-sample thermal information is extremely difficult and costly. So, in the actual application, thermal error is predicted with poor thermal information as input, and then the robustness is weak because the cross-domain and poor spatiotemporal thermal information prediction is still a severe challenge. In this study, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed without deepening network depth, instead, its width is expanded. Specifically, the adjacency matrix is constructed to consider the spatial information by defining the distance between each two sensors, and then the graph convolutional network is integrated into long short-term memory and gated recurrent unit to propose graph convolutional long short-term memory and graph convolutional gated recurrent unit with constructed adjacency matrix as input, respectively. The graph convolutional long short-term memory and graph convolutional gated recurrent unit are superimposed to propose the multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network. To improve the robustness, the adjacency matrix is retrained, and the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed and applied to forecast thermal error, and its prediction accuracy is 94.926%. Spatiotemporal characteristics of temperature and thermal error are fully captured by transfer learning model of graph convolutional long short-term memory and graph convolutional gated recurrent unit. Finally, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is embedded into thermal error control service architecture based on cloud-edge collaboration. With implementation of thermal error control service architecture based on cloud-edge collaboration, machining error of machine tools is reduced by more than 80%. [ABSTRACT FROM AUTHOR]

• A universal kinematic model of five-axis machine tools based on the UDQ is proposed. • The PIGEs of translational/rotary axes are redefined based on the UDQ. • The synchronous motion trajectory of dual rotary axes is proposed using a DBB. • A DBB trajectory equalization algorithm is established. • The identification method is extended to other five-axis machine tools. A universal identification method for position independent geometric errors (PIGEs) of dual rotary axes of five-axis machine tools with arbitrary rotary axis position structures is proposed based on the unit dual quaternion (UDQ). The PIGEs of translational/rotary axes are redefined based on UDQ transformation, which effectively reduces the transformation parameters and avoids the unitization process of UDQ compared with the existing UDQ kinematics model. Based on the redefined PIGEs, a kinematic error model of a five-axis machine tool is established for error identification. Taking the cradle-type five-axis machine too with non-intersecting rotary axes as the research objective, a novel method for identifying PIGEs through the synchronous motion of dual rotary axes using a double ball bar (DBB) is proposed. Compared with BK1 and BK2 in ISO10791-6 and other existing methods, the advantage of the proposed method is that it can greatly reduce the complexity of the experiment and the operation steps by directly constructing the relative kinematic relationship between the tool and the workpiece based on the error model. Only one installation and one measurement trajectory are needed to simultaneously identify the eight PIGEs of the dual rotary axes. In addition, a trajectory equalization algorithm is proposed to effectively solve the asynchronous problem of motion and data sampling in DBB trajectory operation. By combining the simplified error model with the sampling data of DBB, the pseudo-inverse matrix is used for decoupling. The effectiveness of the proposed method is verified by DBB compensation comparison experiment. The proposed error identification method can be applied to other types of five-axis machine tools with a few changes in the configural parameters, which is ideal for periodic checks and calibration to ensure the machining accuracy. [Display omitted] [ABSTRACT FROM AUTHOR]