Simplified and efficient calibration of a mechanistic cutting force model for ball-end milling

Accurate evaluation of the empirical coefficients of a mechanistic cutting force model is critical to the reliability of the predicted cutting forces. This paper presents a simplified and efficient method to determine the cutting force coefficients of a ball-end milling model. The unique feature of this new method is that only a single half-slot cut is to be performed to calibrate the empirical force coefficients that are valid over a wide range of cutting conditions. The instantaneous cutting forces are used with the established helical cutting edge profile on the ball-end mill. The half-slot calibration cut enables successive determination of the lumped discrete values of the varying cutting mechanics parameters along the cutter axis whereas the size effect parameters are determined from the known variation of undeformed chip thickness with cutter rotation. The effectiveness of the present method in determining the cutting force coefficients has been demonstrated experimentally with a series of verification test cuts.     

Development of a production inventory model with uncertainty and reliability considerations

This paper addresses a problem of an imperfect production system under fuzzy demand and inventory holding cost. Production process reliability is considered because of the imperfect production process. In this problem, reliability of the system in regards to producing defective and non-defective items is considered as a decision variable. The objective is to maximize the graded mean integration value (GMIV) of the expected average profit while considering revenues as well as any other relevant costs. The developed model belongs to the class of a geometric programming. We have developed a simple mathematical methodology to solve the model. Genetic algorithm and simulated annealing algorithms are also applied to solve and validate the results. A numerical example has been presented to interpret the solutions.     

Generating efficient tool paths from point cloud data via machining area segmentation

This paper presents a method of generating efficient three-axis ball-end milling tool paths directly from point cloud data. The primary objective is to achieve high efficiency in the machining of free-form surface geometry having isolated complex machining area. The high machining efficiency is attained by segmenting the entire machining domain into distinct areas according to the geometric complexity of the data points and by using cutters of different sizes for the segmented machining areas. An iterative numerical procedure is derived to determine the critical complexity that separates the data points with higher complexity (the complex points) from those with lower complexity (the non-complex points). A larger and more efficient ball-end mill is used to machine the area defined by the non-complex points. The gouging condition of all the data points is then evaluated with respect to the given ball-end mill. The isolated complex machining area is established by enclosing both the complex points and the gouge points. The smaller and gouge-free ball-end mill for the isolated complex machining area is subsequently selected from the standard commercial cutter series. Implementation of the presented method clearly demonstrates the high efficiency of the generated tool paths.     

Production of Micro- and Nanofibrillated Cellulose through an Aqueous Counter Collision System Followed by Ultrasound: Effect of Mechanical Pretreatments

ABSTRACT

The purpose of this work was the production of cellulose micro-nanofibrils from wood pulps through a combination of mechanical treatments, including a recently patented process, a so-called aqueous counter collision (ACC) method followed by ultrasound treatment. Previously to ACC process, the raw pulp materials had to be downscaled to micro-sized dimension, due to the limits of collision jets nozzle diameter. Therefore, the softwood and hardwood pulps were mechanically pre-treated with valley beater refining or dry ball milling. Optical microscopy, atomic force microscopy, X-ray diffraction, thermogravimetric analysis, and gravimetric yield were estimated, to evaluate the effect of mechanical pretreatments on the proposed integrated fibrillation process. The characterizations indicated that valley beater as mechanical pretreatment was found to be more efficient compared to ball milling, of producing cellulose micro-nanofibrils in compliance with the proposed experimental procedure.     

Sustainable operator assignment in an assembly line using genetic algorithm

This paper addresses the operator assignment in predefined workstations of an assembly line to get a sustainable result of fitness function of cycle time, total idle time and output where genetic algorithm is used as a solving tool. A proper operator assignment is important to get a sustainable balanced line. To improve the efficiency and meet the desired target output within the time limit, a balanced assembly line is a must. Real world lines consist of a large number of tasks and it is very time consuming and crucial to choose the most suitable operator for a particular workstation. In addition, it is very important to assign the suitable operator at the right place as his skill of operating machines finally reflects in productivity or in the cost of production. To verify better assignments of workers, a genetic algorithm is adopted here. A heuristic is proposed to find out the sustainable assignment of operators in the predefined workstations.     

Transparent and Flexible Cellulose Nanocrystal/Reduced Graphene Oxide Film for Proximity Sensing

The rapid development of touch screens as well as photoelectric sensors has stimulated the fabrication of reliable, convenient, and human‐friendly devices. Other than sensors that detect physical touch or are based on pressure sensing, proximity sensors offer controlled sensibility without physical contact. In this work we present a transparent and eco‐friendly sensor made through layer‐by‐layer spraying of modified graphene oxide filled cellulose nanocrystals on lithographic patterns of interdigitated electrodes on polymer substrates, which help to realize the precise location of approaching objects. Stable and reproducible signals generated by keeping the finger in close proximity to the sensor can be controlled by humidity, temperature, and the distance and number of sprayed layers. The chemical modification and reduction of the graphene oxide/cellulose crystal composite and its excellent nanostructure enable the development of proximity sensors with faster response and higher sensitivity, the integration of which resolves nearly all of the technological issues imposed on optoelectronic sensing devices.     

Performance evaluation of control chart for multiple assignable causes using genetic algorithm

With a view to monitoring and controlling manufacturing processes in industries, control charts are widely used and needed to be designed economically to achieve minimum quality costs. Many authors have studied the economic design of the $ \overline{X} $ control chart after Duncan (J Am Stat Assoc 51(274):228–242, 1956) first proposed the economic model of the $ \overline{X} $ control chart for a single assignable cause. But, in practice, multiple assignable causes are more logical and realistic. Moreover, the economic design does not consider statistical properties like bound on type I and type II error, and average time to signal (ATS). This paper focuses on evaluating the performance of genetic algorithm (GA) in pure economic and economic statistical design of the $ \overline{X} $ control chart for multiple assignable causes. The performances of GA are demonstrated by comparing its result with the previously proposed grid search technique for a numerical example. The Duncan model of multiple assignable causes is adopted to formulate objective function, and the computation is achieved by approximation through a numerical method named Simpson's 1/3 rule. Comparison distinctly shows the superiority of GA over grid search results for economic statistical design.     

Cutting force prediction for ball-end mills with non-horizontal and rotational cutting motions

Accurate cutting force prediction is essential to precision machining operations as cutting force is a process variable that directly relates to machining quality and efficiency. This paper presents an improved mechanistic cutting force model for multi-axis ball-end milling. Multi-axis ball-end milling is mainly used for sculptured surface machining where non-horizontal (upward and downward) and rotational cutting tool motions are common. Unlike the existing research studies, the present work attempts to explicitly consider the effect of the 3D cutting motions of the ball-end mill on the cutting forces. The main feature of the present work is thus the proposed generalized concept of characterizing the undeformed chip thickness for 3D cutter movements. The proposed concept evaluates the undeformed chip thickness of an engaged cutting element in the principal normal direction of its 3D trochoidal trajectory. This concept is unique and it leads to the first cutting force model that specifically applies to non-horizontal and rotational cutting tool motions. The resulting cutting force model has been validated experimentally with extensive verification test cuts consisting of horizontal, non-horizontal, and rotational cutting motions of a ball-end mill.