A hybrid method is proposed for optimizing rigid tapping parameters and reducing synchronization errors in Computer Numerical Control (CNC) machines. The proposed method integrates uniform design (UD), regression analysis, Taguchi method, and fractional-order particle swarm optimizer (FPSO) to optimize rigid tapping parameters. Rigid tapping parameters were laid out in a 28-level uniform layout for the experiments in this study. Since the UD method provided a layout with uniform dispersion in the experimental space, the UD method’s uniform layout provided iconic experimental points. Next, the 28-level uniform layout results and regression analysis results were used to obtain significant parameters and a regression function. To obtain the parameter values from the regression function, FPSO was selected because its diversity and algorithmic effectiveness are enhanced compared with PSO. The experimental results indicated that the proposed method could obtain suitable parameter values. The best parameter combination in FPSO yielded the best results in comparisons of the non-systematic method. Next, the best parameter combination was used to optimize actual CNC machining tools during the factory commissioning process. From the commissioning process perspective, the proposed method rapidly and accurately minimizes synchronization error from 23 pulses to 18 pulses and processing time from 20.8 s to 20 s. In conclusion, the proposed method reduced the time needed to tune factory parameters for CNC machining tools and increased machining precision and decreased synchronization errors.

During the machining process, the computer numerical control machine is susceptible to variations in ambient temperature, cutting heat, and friction within the transmission parts, which generate different heat sources. These heat sources affect the machine structure in different ways, causing deformation of the machine and displacement of the tooltip and workpiece position, ultimately resulting in deviations in machining accuracy. The amount of thermal drift depends on several factors, including the material of the machine components, the cutting conditions, the duration of the machining process, and the environment. This study proposes a hybrid optimization algorithm to optimize the thermal variables of computer numerical control machine tool spindles. The proposed approach combines regression analysis and fuzzy inference to model the thermal behavior of the spindle. Spindle speed and 16 temperature measurement points distributed on the machine are input factors, while the spindle's axial thermal error is considered an output factor. This study develops a regression equation for each speed to account for the different temperature rise slopes and spindle thermal variations at different speeds. The experimental results show that the hybrid thermal displacement compensation framework proposed in this study effectively reduces the thermal displacement error caused by spindle temperature variation. Furthermore, the study finds that the model can be adapted to significant variations in environmental conditions by limiting the machining speed range, which significantly reduces the amount of data needed for model adaptation and shortens the adaptation time of the thermal displacement compensation model. As a result, this framework can indirectly improve product yield. The effects observed in this study are remarkable.

This paper proposes a hybrid multi-object optimization method integrating a uniform design, an adaptive network-based fuzzy inference system (ANFIS), and a multi-objective particle swarm optimizer (MOPSO) to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control (CNC) machines. First, rigid tapping parameters and uniform (including 41-level and 19-level) layouts were adopted to collect representative data for modeling. Next, ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data. In tapping center machines, the synchronization errors and cycle times are important considerations, so these two objects were used to build the ANFIS models. Then, a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously. The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the non-systematic method. Additionally, the optimal parameter combination was used to optimize existing CNC tools during the commissioning process. Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping. The position gain and pre-feedback coefficient can reduce the synchronization errors significantly, and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors. The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms; it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield, thereby helping factories achieve carbon reduction.

To build a synchronization error prediction model for the machine tool efficiently, a robust whale optimization algorithm (RWOA) method proposed in this study is applied to the hyperparameter optimization of its model. The proposed RWOA method integrated a non-linear time-invariant inertia weighting (NTIW) method and a Taguchi-based adaptive parameter exploration (ATPE) to improve the performance of WOA and promote robustness. The NTIW method can improve the performance of other algorithms, so this study used the NTIW method in the WOA. In addition, the Taguchi method can get an excellent combination of variables with optimal values and stable performance, making the WOA robust. First, to verify the validity of the proposed RWOA method, 13 benchmark functions were used in this study. The results of the benchmark function tests include the mean, standard deviation, and p-value of the t-distribution test. The results show that 11 of the 13 functions differ significantly. In other non-significant difference functions, the means and standard deviations obtained by the proposed RWOA are considerably better than those obtained by WOA. Since the product cost of machine tools is higher, if a prediction model can be built effectively, it can reduce the cost. Therefore, in this study, the proposed RWOA was used to explore the best hyperparameter combination for the model. From the results, the model’s average MAPE (mean absolute percentage error) was 7.2604% for training data and 9.2603% for the testing data under 30 modeling runs. For the best one in 30 models, the MAPE was 6.8384% for the training data and 6.7372% for the testing data. This model was also introduced into the actual machine in this study, and the experimental results showed the MAPE 6.3447%. The proposed RWOA method effectively explores a suitable synchronization error model for the tool machine.