Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic production.Thus,it is necessary to monitor tool condition for a.machining process to have superior quality and economic production.In the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique.Cutting force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the study.The machine learning technique was applied to both vibration and cutting force signals.Discrete wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the dataset.Significant features,among features extracted,were selected using J48 decision tree technique.Clas-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.

Utility of MtCOI polymerase chain reaction-restriction fragment length polymorphism in differentiating between Q and B whitefly Bemisia tabaci biotypes

The invasive, insecticide-resistant, Q whitefly biotype, has gradually spread to other countries including the US via human-mediated movement of plant materials. We assessed the utility of the VspI-based mtCOI （mitochondrion cytochrome oxidase I） polymerase chain reaction-restriction fragment length polymorphism （PCR-RFLP） technique as a rapid, cost-effective, and reliable alternative for differentiating the Q from the dominant B biotype in Arizona. Using the standard mtCOI gene sequencing and mtCOI PCR-RFLP techniques, we biotyped eight whitefly strains of five individuals each collected from poinsettia and cotton at different locations in Arizona. Complete concordance was observed between the two methods, with three strains being identified as the Q biotype and five samples as the B biotype. We also scanned the mtCOI gene sequences for VspI polymorphisms in the B and Q biotype whiteflies currently available in the GenBank database. This global screening revealed the existence of three and four VspI polymorphic types for the Q and B biotypes, respectively. Nevertheless, all three VspI polymorphic Q biotype whiteflies shared a common and unique VspI site that can be used to differentiate Q biotype from the four VspI polymorphic B biotype whiteflies identified. These results demonstrate that the VspI-based mtCOI gene PCR-RFLP provides a reliable diagnostic tool for differentiating the Q and B biotype whiteflies in the US and elsewhere.