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The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

This study aims to elucidate the machining properties of low-cost expanded clay-reinforced syntactic foams by using different neural network models for the first time in the literature. The main goal of this endeavor is to create a casting machining-neural network modeling flow-line for real-time foam manufacturing in the industry.

Samples were manufactured via an industry-based die-casting technology. For the slot milling tests performed with different cutting speeds, depth of cut and lubrication conditions, a 3-axis computer numerical control (CNC) machine was used and the force data were collected through a digital dynamometer. These signals were used as input parameters in neural network modelings.

Among the algorithms, the scaled-conjugated-gradient (SCG) methodology was the weakest average results, whereas the Levenberg–Marquard (LM) approach was highly successful in foreseeing the cutting forces. As for the input variables, an increase in the depth of cut entailed the cutting forces, and this circumstance was more obvious at the higher cutting speeds.

The effect of milling parameters on the cutting forces of low-cost clay-filled metallic syntactics was examined, and the correct detection of these impacts is considerably prominent in this paper. On the other side, tool life and wear analyses can be studied in future investigations.

It was indicated that the milling forces of the clay-added AA7075 syntactic foams, depending on the cutting parameters, can be anticipated through artificial neural network modeling.

It is hoped that analyzing the influence of the cutting parameters using neural network models on the slot milling forces of metallic syntactic foams (MSFs) will be notably useful for research and development (R&D) researchers and design engineers.

This work is the first investigation that focuses on the estimation of slot milling forces of the expanded clay-added AA7075 syntactic foams by using different artificial neural network modeling approaches.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

Fabrics, which are one of the raw materials of the clothing industry, constitute approximately 40–45% of the total cost of an apparel product. Due to the labor-intensive nature of this industry and failure to apply scientific methods along with the manufacturing processes, the wastes in the raw materials, including fabrics, become higher. Besides, quality deficiencies are encountered due to the same reasons. This study aims to determine the optimum total fabric layer height based on the fabric type during the cutting process with a straight knife cutting machine, which provided a decrease in the cutting errors.

Frequently used fabric types in an enterprise operating in organic cotton knitwear were listed. During the cutting tests, the straight knife cutting machine was used as the cutting device. The weight and thickness values of the fabrics were obtained to provide a comparison basis. Two different algorithms were created to evaluate the defective pieces according to fabric type, cutting height and error placement. Cutting resistances of these fabrics were also determined to evaluate the defect reasons. In the end, optimum total fabric layer count and total cutting height suggestions were proposed for each fabric type for a minimum cutting error.

At the end of this study, the error-free layers were identified per fabric type. At the same time, the optimum cutting height was suggested for each fabric basis. For 40/1 single jersey fabrics, the cutting height should be between 2.10 cm and 10.40 cm; for 30/1 single jersey fabrics, between 1.65 cm and 5.70 cm; for 20/1 single jersey fabrics, between 1.83 cm and 6.70 cm; for two-thread fleece fabrics, between 2.13 cm and 4.70 cm; and for three-thread fleece fabrics, between 0 cm and 4.90 cm.

Within the scope of the study, since the products made of knitted fabric were produced more frequently and in large quantities, the study was carried out with 15 different types of knitted fabrics at 10 different layers. The same methods should be applied for woven, denim and nonwoven fabric types, which would shed light on the following studies.

Due to scarce research carried out on the cutting procedure of the clothing industry in regards to sustainability, this study aims to contribute to this area. The main difference between this study and the studies that mostly make mathematical predictions about the cutting procedure is that it is practice-oriented.

The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

The purpose of this paper is to investigate the performances of vegetable based cutting fluids by comparing tool life, surface roughness and cutting force during end milling of AISI 304 stainless steel. In the experiments, three different vegetable based cutting fluids developed from sunflower and canola oils (SCF‐II with 8% extreme pressure (EP), CCF‐II without EP and CCF‐II with 8% EP) and a commercial type of semi‐synthetic cutting fluid were used. Cutting fluid was applied to the cutting zone via two nozzles.

Effects of different cutting speeds (100, 150 and 200 m/min) and different feed rates (0.2, 0.25 and 0.3 mm/rev) on tool life, surface roughness and cutting force in milling of AISI 304 stainless steel were investigated. Depth of cut and step over were kept constant as 0.3 mm and 10 mm at both conditions, respectively.

Results indicated that CCF‐II with 8% EP cutting fluid showed better performance than the others.

In this study, effect of extreme pressure additive on milling performance was investigated.

The knowledge over the performance of cutting fluids when applied under different machining conditions (such as distinct work material and cutting parameters) is critical in order to improve the efficiency of most machining operations. This paper is concerned with the performance of cutting fluids employed under two distinct machining operations involving aluminium alloys: drilling of AA 1050‐O aluminium applying cutting fluid as a mist and turning of AA 6262‐T6 aluminium alloy using cutting fluids (as a flood) with distinct extreme pressure additives (chlorine, sulphur and phosphor).

This work reports on a experimental study of the performance of cutting fluids when machining aluminium alloys.

The results indicated an increase in the flow rate of the mist led to lower feed forces but higher torque, power consumption and specific cutting pressure in the drilling operation (AA 1050‐O aluminium). The surface finish was not drastically affected by the cutting fluid flow rate. When turning AA 6162‐T6 aluminium alloy, in general, best results were observed using 10 per cent fluid concentration applied at the tool‐workpiece interface. The cutting fluid containing chlorine as extreme pressure additive produced lower cutting forces and better surface finish at high cutting speed and low feed rate and depth of cut.

The novel element of this paper is the use of minimal lubrication (drilling) and cutting fluids with distinct extreme pressure (turning).

To have all the required components of batches of product orders ready for timely assembly and delivery, the real time wood strip cutting patterns in a major solid wood furniture manufacturing plant has to be dynamically generated based on both the order priority and the minimum wood waste.

An adaptive fuzzy ranking method and a recursive function for pattern generation were integrated into an optimization procedure to solve the real time one‐dimensional multiple‐grade cutting stock problem when orders are prioritized.

The simulation results illustrate that the optimization algorithm produce considerably less waste than the current approach. If implemented in the industry, the saving in raw material could be in the range of 5‐10 percent.

The optimization algorithm is for the cut‐to‐size decisions only with the consideration of the order priorities. The overall scheduling of the production shop floor is not addressed.

The algorithm can be used on the cutting machines as an online patterns generator and cutting optimizer.

There is no literature available for the real time one‐dimensional multiple‐grade cutting stock problem when orders are prioritized. The few commercial optimizers have unknown algorithms with unpredictable waste.

WHEN METALS ARE SHEARED or “cut” a compressive force of high intensity is applied to the metal by the cutting tool. The metal crystals are subjected to this force and the resulting stresses cause certain crystals to slip or flow in various directions along planes of slippage, the direction of slip normally being along planes of greatest atom density.

The influence of mechanical and physical properties of fabrics on cutting process and the behaviours of fabrics during automatic cutting of a fabrics lay into garment pieces is studied. It is shown that the blade forces and the mechanical and physical properties of fabrics, such as elongation strength, bending and shear rigidity have significant influences on cutting of fabrics lay. The velocity of vertical and horizontal components of movements of the knife blade, elastic properties of fibres and the air permeability have significant influence on the quality of cutting and pattern pieces, as well.