تلفیق حسگرها در سامانه پایش وضعیت ابزار با استفاده از سیستم استنتاج عصبی- فازی تطبیقی بهینه‌شده

نویسندگان

1 عضو هیات علمی دانشگاه فنی و مهندسی بوئین زهرا

2 دانسگاه آزاد تهران شرق

3 دانشگاه بیرجند

چکیده

در حال حاضر بیشتر سیستم­های پایش وضعیت سایش ابزار براده­ برداری مبتنی بر مقادیر مشخصه­هایی از سیگنال که مرتبط با سایش ابزار هستند می­باشند. ارزیابی وضعیت ابزار بر­اساس مشخصه­های سیگنال یک حسگر قابل اطمینان نمی­باشد زیرا مشخصه به­دست آمده از سیگنال یک حسگر علاوه بر سایش ابزار به سایر عوامل غیر مرتبط با سایش ابزار مانند پارامترهای فرآیند و اغتشاشات تصادفی نیز وابسته است. راه حل این مساله، تلفیق داده­های چند حسگر غیر­متجانس می­باشد. اطلاعات به­دست­آمده از این روش کامل­تر و دارای دقت و قابلیت اطمینان­ بالاتری است. در این تحقیق، ترکیب حسگرهای بینایی، جریان، کرنش و ارتعاشات به­منظور پیش­بینی وضعیت سایش سطح آزاد ابزار پیشنهاد شده است. مدل سیستم استنتاج عصبی فازی تطبیقی (ANFIS) بهینه شده جهت تلفیق مشخصه­های سیگنال بافت سطح، جریان موتور، کرنش و ارتعاشات توسعه شده است. ساختار مدل ANFIS پیشنهادی دارای چهار ورودی و یک خروجی می­باشد. ورودی­های مدل شامل بی نظمی بافت سطح قطعه­کار (که توسط تبدیل موجک فیلتر شده)، انتگرال حاشیه­ای زمان فرکانس سیگنال جریان موتور اسپیندل و بی نظمی شانون سیگنال­های کرنش و ارتعاشات ابزار می­باشد. نتایج به­دست­آمده نشان داد با استفاده از مدل ANFIS بهینه شده می­توان مشخصه­های سیگنال­ها را تلفیق و با دقت بالایی در پیش­بینی وضعیت ابزار استفاده نمود.

کلیدواژه‌ها

عنوان مقاله [English]

Multi-Sensory Data Fusion System for Tool Condition Monitoring Using Optimized Artificial Fuzzy Inference System

نویسندگان [English]

  • mahdi danesh 1
  • sedighe danesh 2
  • khalil khalili 3
1 booin zahra
2 azad tehran shargh
3 birjand
چکیده [English]

Currently, most of the available tool wear condition monitoring systems are based on the signal features quantities that are correlated with tool wear. The evaluation of tool wear based on one sensor is not reliable because the measured features depends not only on tool wear but also on other process parameters and random disturbances. For solving this problem, multi sensor data fusion is used to combine disparate sensory data. The obtained information are more accurate and reliable. In this research, combination of vision, current, strain and vibration sensors for predicting flank wear land is proposed. An optimized adaptive neuro-fuzzy inference system (ANFIS) model is developed to fuse the surface image, motor current, strain and vibration signal features. The structure of proposed ANFIS model has four inputs and one output. The inputs of the model are entropy of surface image (which is filtered by undecimated wavelet transform), time-frequency marginal integral of the motor current, Shannon entropy of strain and Shannon entropy of vibration signals, while output of the model is the flank wear. The results showed that the optimized ANFIS model can be used to fuse the signal features and predict tool flank wear with high accuracy.

کلیدواژه‌ها [English]

  • Cutting Tool
  • Wear
  • Sensor Fusion
  • ANFIS
  • Meta-Heuristic Algorithms

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فایل ها

سابقه مقاله

  • تاریخ دریافت: 05 دی 1395
  • تاریخ بازنگری: 01 اسفند 1397
  • تاریخ پذیرش: 28 شهریور 1397
  • تاریخ انتشار: 01 تیر 1398

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