Enhancing Efficiency with Smart Cutting Solutions in Apparel Industry

Enhancing Efficiency with Smart Cutting Solutions in the Apparel Industry

Saiful Islam
Lecturer
Department of Textile Engineering
Atish Dipankar University of Science & Technology
Email: saiful@adust.edu.bd

Cutting Section

Apparel manufacturing essentially comprises 3 sections: cutting, sewing, and finishing. Among them, cutting is the first and foremost section to start apparel manufacturing. In this study, the focus is on the cutting section, i.e., which has been illustrated in Figure 1. Specifically, there is a comparative discussion of technologies available in the cutting section to this day. It will seem easier if technologies are divided into 2 groups, such as traditional technologies and advanced technologies. Moreover, there are 3 basic things to consider in the cutting section, i.e., i) marker making, ii) fabric spreading, & iii) fabric cutting.

In terms of marker making, there are 2 basic methods, such as manual and digital. Manually, patterns are drawn using a pencil and scale over a thin sheet called a marker. On the other hand, markers can be generated digitally by CAD (Computer Aided Design). CAD-utilized marker-making is significant due to its high marker efficiency. It is done using some software such as LECTRA and GERBER, etc.

After marker making, fabric spreading is done to lay the fabric as per marker length and fabric lay planning. It can be done by 3 basic methods, such as manual, semi-automatic, and automatic. Manually, it is executed through laying fabric by humans. After that, semi-automatic takes the coordination of both humans and a spreading machine. Finally, the automatic fabric-spreading machine is capable of laying fabric without any human help. Also, machine learning and artificial intelligence have the capability of adding value to automatic fabric spreading.

Eventually, the prepared fabric lay is ready to be cut as per the marker. Generally, fabric cutting is done by using 2 basic methods, such as semi-automatic and automatic. Between them, semi-automatic methods include several cutting machines such as scissors, straight knife cutting machines, round knife cutting machines, etc. On the other hand, automatic methods are automatic fabric cutting machines, water jet cutting machines, laser cutting machines, etc. Besides, automatic methods are adding a new era with the help of machine learning, several operating systems (Arduino) and artificial intelligence, etc.

Technologies for Apparel Manufacturing 

Technologies for apparel manufacturing have been evolving with time. Advanced technologies outperform traditional technologies in many aspects such as quality, time, and production. In terms of marker making, CAD systems have been widely used. Recently, there have been trials of artificial intelligence and algorithms for better marker planning and efficiency. Moreover, statistical analysis indicates that there is a linear relationship between marker efficiency and fabric width and implies that markers are more productive with larger fabric widths in all styles in both genders. [5]. Besides, Domovic et al. reported about hyper-heuristic methods, such as grid-shaking. The methods can gain high marker efficiency because of determining the best sequence of cutting parts. [1]. In addition, Xu et al. reported on predicting marker length efficiently by handling the complexity of garment sizes and types. They adopted machine learning models, such as multiple linear regression (MLR) and radial basis function neural network (RBF-NN) for mass production and customization [12].

In the case of spreading, the automatic fabric spreading machine has been the top choice of the manufacturers. Moreover, many uses of several sensors and optimization techniques have been going on. Particularly, Zhiyong et al. reported on automation in fabric spreading by using conveyor belts and wrinkle-removing mechanisms, enhancing flatness. In another study, there is an attempt to minimize the cost and frequency of spreading and cutting. It functions by performing the rearrangement of lays efficiently. There is a heuristic algorithm behind it that determines the whole set of lay sequences [6].
Figure: Fabric spreading and Cutting Process

The main task, i.e., cutting, has relied greatly on the automatic fabric-cutting machine. Furthermore, there are several uses of microcomputer-controlled systems, lasers, water jet systems, ultrasounds, plasmas, etc. [11]. Specifically, different cutting machines have been used to date. In terms of advanced technologies, fabric cutting has become fully automated, such as Arduino-driven devices have been used to spread fabric as well as to maintain the precision of cut. These devices have also outperformed by ensuring accuracy, efficiency, and productivity [10]. Another study has utilized laser cutting, which facilitates cutting intricate patterns with high precision. This has made it easier to be an efficient and sustainable technology by reducing fabric wastage [7]. Similarly, cut order planning was enhanced by integer programming models yielding maximum fabric utilization. LINGO (Language for Interactive General Optimization) software was used to determine optimum results, which cannot be done by an operator [9]. Automatic fabric-cutting machines are widely used in industries nowadays. Because it has the capability of higher precision and production at the same time in the case of a conveyorized cutting table [4]. Moreover, cutting room management software enhances cutting production planning and tracking to a great extent. [11]. Most importantly, artificial intelligence in the cutting section has emerged as a time-effective, productive, and cost-effective approach (Xu Yanni and Thomassey, 2018).Figure 1: Process flow of the cutting department [9]

Critical Analysis

Cutting section efficiency depends on marker, spreading, and cutting efficiency. Moreover, manufacturers prioritize both production and quality simultaneously. That’s why it is hardly possible to maintain those factors for every past and recent development technology in the cutting section.

Initially, cut order planning has a vital role in the optimization of fabric consumption, which requires almost 50 to 60% of the total cost of apparel making. This planning can be optimized by running a code in LINGO software. There is an example of shirt production in Table 1.

Table 1: Cut order planning for shirt production

Spreading Number	No of plies		Marker plan length cm		Total length cm
	Manual	LINGO	Manual	LINGO	Manual	LINGO
Spreading 1	45	30	792.19	891.01	40600.55	38186.33
Spreading 2	8	9	619	1272.8

There are several ways to prioritize LINGO software results in terms of the number of plies, marker plan length, and total length. In every aspect, it has given better results, which refer to better fabric utilization and fewer fabric wastages [9].

In marker making, the CAD system has the maximum outcome compared to other systems. This requires software like LECTRA, GERBER, etc., where pattern-making and marker-making can be efficiently executed by skilled persons [2]. There can be assistance like artificial intelligence about how efficiency can be increased, such as by calculating with a continuous loop and algorithms. Moreover, fabric width has a significant impact on marker efficiency. There has been a study on fabric width based on 2 categories, such as gender (male and female) and body shape (triangle, oval, square, and circle) [5]. From the study, the best results that were found are given in Table 2.

Table 2: Relationship of fabric width and marker based on gender and human body shape.

Similarly, a study has been conducted to develop new heuristics for making optimized markers. In this article, there has been an introduction to Grid, Grid-BLP, Grid-Shaking, Grid-All, and AEF-All Equal First. Among them, Grid-BLP and Grid-Shaking have been proven to yield better results. Exceptionally, Grid-Shaking did well, for which the hyper-heuristic utilized this in most cases (88%) [1].

In fabric spreading, the automatic fabric spreading machine has reported the maximum efficiency. This machine needs only one skilled person to operate. Also, this machine can count plies, detect faults, and apply splicing, etc. There can also be assistance to control the number of lay to save time [2]. For instance, Shang et al. reported a heuristic algorithm that attempts to reduce the frequency of cutting beds. The proposed algorithm runs after every lay-making on a loop. This was experimented on in C# for 500 cases where they accomplished effective results [6].

In cutting, the automatic cutting machine has been the best choice due to its high production rate and precision of cutting. It is way better than a straight knife-cutting machine, as it has to be operated by a skilled human, resulting in less production and quality. Also, this machine doesn’t require any marker as it is connected to the marker-making software. This machine cuts fabric automatically as per the marker without extra instructions except those given in the software. Hence, it saves time and labor costs at the same time [2]. Also, an automatic system can cut in a conveyorized cutting table to maintain continuity, resulting in higher productivity. Surprisingly, Serkin Tekstil brought a new mechanism of an oscillating cutting knife that moves side to side along with up and down. At ITMA, novel things have been presented, like a dual-cutting head and an automatic labeler by Morgan Technica and Serkon Tekstil. Here, labelers are for human help to avoid serial or sticker mistakes after cutting [4]. Moreover, an automatic system can be based on an Arduino system. In that case, it consisted of 2 parts, such as the conveyor part and the cutting part. One user is needed here who will operate the system. It has been reported as a way of automatic cutting resulting in a reduction of fabric wastage [10]. Besides, a laser cutting (computer-controlled) system can be used for multi-ply cutting [2]. However, there has been a study on the straight knife cutting machine where optimum height for different knitted fabric types has been reported, such as for 40/1 single jersey, the cutting height was suggested between 2.10 cm and 10.40 cm [3].

Figure 2: Advanced manufacturing technology readiness factors [8]

In a study of the Sri Lankan apparel industry, it is mentioned that the adoption of technology has been influenced by employee readiness. The article emphasized 5 factors (Figure 2), such as perceived usefulness, attitude, ease of use, management support, and techno-optimism. The study has shown that perceived usefulness, attitude, and techno-optimism have a moderate positive relation with readiness. In the meantime, ease of use and management support have strong positive and weak positive relations with readiness, respectively [8].

Technologies in the cutting section for apparel manufacturing have evolved a lot. Hence, more efficiency has been possible to achieve in marker making, fabric spreading, and fabric cutting processes. These processes have involved CAD, automation, and AI owing to better efficiency and fewer fabric wastages. In addition, automatic marker making, automatic fabric spreading, and automatic cutting have been dominating the cutting section to date. However, other technologies to date can hardly compete due to the production and quality tests. Moreover, adapting to new technologies has not been so easy. It is because of less or no training, lack of strong attitude, ease of use, etc., towards modern technologies. Eventually, more specific research is badly needed, as future research outcomes will shape the cutting section differently.

References

[1] D. Domović, T. Rolich, M. Golub, Hyper-Heuristic Approach for Improving Marker Efficiency, Autex Research Journal 18 (2018) 348–363.

[2] H. Jindal, S. Kaur, Robotics and Automation in Textile Industry, Int J Sci Res Sci Eng Technol (2021) 40–45.

[3] M. Küçük, A new technique for determination of the optimum cut height according to the fabric type in garment production, Research Journal of Textile and Apparel (2024).

[4] Minyoung Suh, Automated Cutting and Sewing for Industry 4.0 at ITMA 2019, Textile and Apparel Technology and Management (JTATM) (2019).

[5] T. Naveed, Y. Zhong, Y. Zhicai, M.A. Naeem, L. Kai, X. Haoyang, A. Farooq, Z.A. Abro, Influence of woven fabric width and human body types on the fabric efficiencies in the apparel manufacturing, Autex Research Journal 20 (2020) 484–496.

[6] X. Shang, D. Shen, F.-Y. Wang, T.R. Nyberg, A heuristic algorithm for the fabric spreading and cutting problem in apparel factories, IEEE/CAA Journal of Automatica Sinica 6 (2019) 961–968.

[7] J. Su, N. Wang, F. Zhang, A design of bionic soft gripper for automatic fabric grasping in apparel manufacturing, Textile Research Journal 93 (2023) 1587–1601.

[8] S.D.E. Susitha, Influencing factors of employee readiness to adopt advanced manufacturing technology (AMT) on apparel shop floor in Sri Lanka, International Journal of Multidisciplinary Studies 8 (2021) 1.

[9] C. Ünal, A.D. Yüksel, Cut order planning optimisation in the apparel industry, Fibres and Textiles in Eastern Europe 28 (2020) 8–13.

[10] P. Vidhyalakshmi, G.M. Janani, C. Janani, J.M. Shajith, Automatic fabric cutting, in: 2021: p. 140026.

[11] I. Vilumsone-Nemes, 6 – Automation in spreading and cutting, in: R. Nayak, R. Padhye (Eds.), Automation in Garment Manufacturing, Woodhead Publishing, 2018: pp. 139–164.

[12] Y. Xu, S. Thomassey, X. Zeng, An Application of Machine Learning to Marker Prediction in Garment Industry: Marker Length Estimation by Neural Network for the Exponentially Increasing Magnitude of Possible Size Combinations, in: Proceedings of the 3rd International Conference on Applications of Intelligent Systems, Association for Computing Machinery, New York, NY, USA, 2020.

[13] S. and Z.X. Xu Yanni and Thomassey, AI for Apparel Manufacturing in Big Data Era: A Focus on Cutting and Sewing, in: X. Thomassey Sébastien and Zeng (Ed.), Artificial Intelligence for Fashion Industry in the Big Data Era, Springer Singapore, Singapore, 2018: pp. 125–151.